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2212-441112152016MayOral surgery, oral medicine, oral pathology and oral radiologyOral Surg Oral Med Oral Pathol Oral RadiolComparison of the performance of intraoral X-ray sensors using objective image quality assessment.e129e137e129-3710.1016/j.oooo.2016.01.016S2212-4403(16)00039-0The main aim of this study was to evaluate the performance of 10 individual sensors of the same make, using objective measures of key image quality parameters. A further aim was to compare 8 brands of sensors.Ten new sensors of 8 different models from 6 manufacturers (i.e., 80 sensors) were included in the study. All sensors were exposed in a standardized way using an X-ray tube voltage of 60 kVp and different exposure times. Sensor response, noise, low-contrast resolution, spatial resolution and uniformity were measured.Individual differences between sensors of the same brand were surprisingly large in some cases. There were clear differences in the characteristics of the different brands of sensors. The largest variations were found for individual sensor response for some of the brands studied. Also, noise level and low contrast resolution showed large variations between brands.Sensors, even of the same brand, vary significantly in their quality. It is thus valuable to establish action levels for the acceptance of newly delivered sensors and to use objective image quality control for commissioning purposes and periodic checks to ensure high performance of individual digital sensors.Copyright © 2016 Elsevier Inc. All rights reserved.Hellén-HalmeKristinaKDepartment of Oral and Maxillofacial Radiology, Faculty of Odontology, Malmö University, Malmö, Sweden. Electronic address: Kristina.Hellen-Halme@mah.se.JohanssonCurtCDepartment of Oral and Maxillofacial Radiology, Faculty of Odontology, Malmö University, Malmö, Sweden.NilssonMatsMDepartment of Oral and Maxillofacial Radiology, Faculty of Odontology, Malmö University, Malmö, Sweden; Department of Radiation Physics, Skåne University Hospital, Malmö, Sweden.engComparative StudyJournal Article20160213
United StatesOral Surg Oral Med Oral Pathol Oral Radiol101576782IMRadiation DosageRadiographic Image EnhancementinstrumentationRadiography, Dental, DigitalinstrumentationSoftwareX-Rays
201594201512152016123201641360201641460201711060ppublish2706831710.1016/j.oooo.2016.01.016S2212-4403(16)00039-0
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1933-069312622017FebJournal of neurosurgeryJ NeurosurgAccess to the brain parenchyma using endovascular techniques and a micro-working channel.511517511-51710.3171/2016.1.JNS152543OBJECTIVE Several older studies report a low risk for parenchymal access to the CNS by surgical techniques. In more recent studies, including those with post-puncture CT scans, there are indications that the risk of bleeding might approach 8%. New therapies, such as those that use viral vectors, modified mRNA, or cell transplantation, will probably warrant more parenchymal access to the CNS. Other minimally invasive routes might then be tempting to explore. This study was designed in 2 parts to address the possibility of using the endovascular route. The first aim was to test the ability to create a parenchymal micro-working channel to the CNS in macaque monkeys through the vessel wall. Second, the biocompatibility of a device-associated, detached, distal securing plug that was made of nitinol was investigated in swine for 1 year. METHODS Trans-vessel wall intervention in the middle cerebral artery and associated cerebral parenchyma was performed in 4 rhesus macaque monkeys using a full clinical angiography suite. A contrast agent and methylene blue were injected to test the working channel and then detached at the distal end to act as a securing plug through the vessel wall. One-year follow-ups were also performed using angiography and histological analysis in 10 swine with 24 implants that were distributed in the external carotid artery tree. RESULTS The cerebral interventions were performed without acute bleeding. Both the contrast agent and methylene blue were infused into the brain parenchyma and subarachnoidal space via the endovascular micro-working channel (7 injections in 4 animals). In the 1-year follow-up period, the implant that was left in the external carotid vessel wall in the swine was covered by the endothelium, which was followed by dislodgement just outside the blood vessel with thin capsule formation. No stenosis in the artery was detected on 1-year angiography. The animals showed normal behavior and blood sample results during the follow-up period. This is the first histological demonstration of nitinol biocompatibility when the implant is positioned through an arterial wall and indicates that the trans-vessel wall technique is not comparable with stent placement and its ability to induce intimal hyperplasia and restenosis. CONCLUSIONS This study demonstrates that the trans-vessel wall technique is applicable to brain intervention in macaque monkeys, providing a micro-working channel for delivery or sampling. The long-term follow-up study of the detached device in swine showed no clinical or biochemical complications and a normal angiography appearance.LundbergJohanJDepartment of Clinical Neuroscience, Karolinska Institutet.Department of Neuroradiology, Karolinska University Hospital, Stockholm.JohanssonCarina BCBDepartment of Prosthodontics/Dental Materials, Science, Sahlgrenska Academy, Institute of Odontology, University of Gothenburg, Sweden; and.JonssonStefanSDepartment of Materials Science and Engineering, Royal Institute of Technology, Stockholm, Sweden.HolminStaffanSDepartment of Clinical Neuroscience, Karolinska Institutet.Department of Neuroradiology, Karolinska University Hospital, Stockholm.engJournal Article20160325
United StatesJ Neurosurg02533570022-30850Alloys2EWL73IJ7FnitinolIMAlloysAnimalsBrainsurgeryEndovascular ProceduresmethodsMacaca mulattaMiddle Cerebral ArterysurgeryParenchymal TissuesurgeryStentsSwineECA = external carotid arteryendovascularmacaqueminimal invasivenitinolparenchymal punctureswinevascular disorders
20163266020199560201632660ppublish2701540010.3171/2016.1.JNS152543
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1654-72094552016SepAmbioAmbioChanging Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts.516537516-3710.1007/s13280-016-0770-0Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.BokhorstStefS0000-0003-0184-1162FRAM - High North Research Centre on Climate and the Environment, Norwegian Institute for Nature Research (NINA), PO Box 6606, Langnes, 9296, Tromsø, Norway. stefbokhorst@hotmail.com.Department of Ecological Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands. stefbokhorst@hotmail.com.PedersenStine HøjlundSHDepartment of Bioscience, Arctic Research Centre, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark.BruckerLudovicLNASA GSFC Cryospheric Sciences Laboratory, Code 615, Greenbelt, MD, 20771, USA.Goddard Earth Sciences Technology and Research Studies and Investigations, Universities Space Research Association, Columbia, MD, 21044, USA.AnisimovOlegOState Hydrological Institute of Roshydromet, 23 Second Line V.O., St.Petersburg, Russia, 199053.International Centre for Science and Education "Best", North-East Federal University, Yakutsk, Russia.BjerkeJarle WJWFRAM - High North Research Centre on Climate and the Environment, Norwegian Institute for Nature Research (NINA), PO Box 6606, Langnes, 9296, Tromsø, Norway.BrownRoss DRDClimate Research Division, Environment Canada Ouranos, 550 Sherbrooke St. West, 19th Floor, Montreal, QC, H3A 1B9, Canada.EhrichDorotheeDDepartment of Arctic and Marine Biology, University of Tromsø, 9037, Tromsø, Norway.EsseryRichard L HRLSchool of GeoSciences, University of Edinburgh, Edinburgh, UK.HeiligAchimAInstitute of Environmental Physics, University of Heidelberg, Im Neuenheimer Feld 229, 69120, Heidelberg, Germany.IngvanderSusanneSDepartment of Physical Geography, Stockholm University, 106 91, Stockholm, Sweden.JohanssonCeciliaCDepartment of Earth Sciences, Uppsala University, Villavägen 16, 75236, Uppsala, Sweden.JohanssonMargaretaMDepartment of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62, Lund, Sweden.Royal Swedish Academy of Sciences, PO Box 50005, 104 05, Stockholm, Sweden.JónsdóttirIngibjörg SvalaISUniversity Centre in Svalbard, PO Box 156, 9171, Longyearbyen, Norway.Faculty of Life- and Environmental Sciences, University of Iceland, Sturlugata 7, 101, Reykjavík, Iceland.IngaNiilaNLeavas Sámi Community, Box 53, 981 21, Kiruna, Sweden.LuojusKariKArctic Research, Finnish Meteorological Institute, P.O. Box 503, 00101, Helsinki, Finland.MacelloniGiovanniGIFAC-CNR - Institute of Applied Physics "Nello Carrara", National Research Council, Via Madonna del Piano 10, 50019, Sesto Fiorentino, FI, Italy.MariashHeatherHNational Wildlife Research Centre, Environment Canada, 1125 Colonel By Drive, Ottawa, K1A 0H3, Canada.McLennanDonaldDCanadian High Arctic Research Station (CHARS), 360 Albert Street, Suite 1710, Ottawa, ON, K1R 7X7, Canada.RosqvistGunhild NinisGNDepartment of Physical Geography, Stockholm University, 106 91, Stockholm, Sweden.Department of Earth Sciences, University of Bergen, 5020, Bergen, Norway.SatoAtsushiASnow and Ice Research Center, National Research Institute for Earth Science and Disaster Prevention, 187-16 Suyoshi, Nagaoka, Niigata, 940-0821, Japan.SavelaHanneleHThule Insitute, University of Oulu, PO Box 7300, 90014, Oulu, Finland.SchneebeliMartinMWSL Institute for Snow and Avalanche Research SLF, Flüelastrasse 11, 7260, Davos Dorf, Switzerland.SokolovAleksandrAArctic Research Station of Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences, Labytnangi, Russia, 629400.Science Center for Arctic Studies, State Organization of Yamal-Nenets Autonomous District, Salekhard, Russia.SokratovSergey ASAArctic Environment Laboratory, Faculty of Geography, M.V. Lomonosov Moscow State University, Leninskie gory 1, Moscow, Russia, 119991.TerzagoSilviaSInstitute of Atmospheric Sciences and Climate, National Research Council (ISAC-CNR), Corso Fiume 4, 10133, Turin, Italy.Vikhamar-SchulerDagrunDDivision for Model and Climate Analysis, R&D Department, The Norwegian Meteorological Institute, Postboks 43, Blindern, 0313, Oslo, Norway.WilliamsonScottSDepartment of Biological Sciences, University of Alberta, CW 405, Biological Sciences Bldg., Edmonton, AB, T6G 2E9, Canada.QiuYubaoYInstitute of Remote Sensing and Digital Earth, Chinese Academic of Science, Beijing, 100094, China.Group on Earth Observations, Cold Regions Initiative, Geneva, Switzerland.CallaghanTerry VTVDepartment of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62, Lund, Sweden.Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK.National Research Tomsk Stated University, 36, Lenin Ave., Tomsk, Russia, 634050.engJournal ArticleReview20160317
SwedenAmbio03642200044-7447IMArctic RegionsCold ClimateEcosystemEnvironmental MonitoringeconomicsmethodsModels, TheoreticalSeasonsSnowClimate changeEcosystem servicesHuman healthIndigenousSnowSocietal costs
2015102920162520151132016318602016318602017127602016317ppublish26984258PMC498031510.1007/s13280-016-0770-010.1007/s13280-016-0770-0Alou-Font E, Mundy CJ, Roy S, Gosselin M, Agusti S. Snow cover affects ice algal pigment composition in the coastal Arctic Ocean during spring. Marine Ecology Progress Series. 2013;474:89–104. doi: 10.3354/meps10107.10.3354/meps10107AMAP. 2011. Snow, water, Ice and Permafrost in the Arctic (SWIPA): Climate change and the cryosphere, xii–538. Oslo: Arctic Monitoring and Assessment Programme (AMAP).Aoki T, Matoba S, Yamaguchi S, et al. Light-absorbing snow impurity concentrations measured on Northwest Greenland ice sheet in 2011 and 2012. Bulletin of Glaciological Research. 2014;32:21–31. doi: 10.5331/bgr.32.21.10.5331/bgr.32.21Arctic-Council. 1996. Declaration on the establishment of the Arctic Council, 1–5. Ottawa: Arctic-Council.Arnaud L, Picard G, Champollion N, et al. Measurement of vertical profiles of snow specific surface area with a 1 cm resolution using infrared reflectance: Instrument description and validation. Journal of Glaciology. 2011;57:17–29. doi: 10.3189/002214311795306664.10.3189/002214311795306664Ask J, Karlsson J, Persson L, Ask P, Byström P, Jansson M. Terrestrial organic matter and light penetration: Effects on bacterial and primary production in lakes. Limnology and Oceanography. 2009;54:2034–2040. doi: 10.4319/lo.2009.54.6.2034.10.4319/lo.2009.54.6.2034Avanzi F, Caruso M, Jommi C, De Michele C, Ghezzi A. Continuous-time monitoring of liquid water content in snowpacks using capacitance probes: A preliminary feasibility study. Advances in Water Resources. 2014;68:32–41. doi: 10.1016/j.advwatres.2014.02.012.10.1016/j.advwatres.2014.02.012Barichivich J, Briffa KR, Myneni RB, et al. Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Global Change Biology. 2013;19:3167–3183. doi: 10.1111/gcb.12283.10.1111/gcb.1228323749553Bartsch A, Kumpula T, Forbes BC, Stammler F. Detection of snow surface thawing and refreezing in the Eurasian Arctic with QuikSCAT: Implications for reindeer herding. Ecological Applications. 2010;20:2346–2358. doi: 10.1890/09-1927.1.10.1890/09-1927.121265463Bernard E, Friedt JM, Tolle F, Griselin M, Martin G, Laffly D, Marlin C. Monitoring seasonal snow dynamics using ground based high resolution photography (Austre Lovenbreen, Svalbard, 79°N) ISPRS Journal of Photogrammetry and Remote Sensing. 2013;75:92–100. doi: 10.1016/j.isprsjprs.2012.11.001.10.1016/j.isprsjprs.2012.11.001Biedunkiewicz A, Ejdys E. Icicles as carriers of yeast-like fungi potentially pathogenic to human. Aerobiologia. 2011;27:333–337. doi: 10.1007/s10453-011-9198-y.10.1007/s10453-011-9198-yBilodeau F, Gauthier G, Berteaux D. Effect of snow cover on the vulnerability of lemmings to mammalian predators in the Canadian Arctic. Journal of Mammalogy. 2013;94:813–819. doi: 10.1644/12-MAMM-A-260.1.10.1644/12-MAMM-A-260.1Bjerke JW, Karlsen SR, Høgda KA, et al. Record-low primary productivity and high plant damage in the Nordic Arctic Region in 2012 caused by multiple weather events and pest outbreaks. Environmental Research Letters. 2014;9:084006. doi: 10.1088/1748-9326/9/8/084006.10.1088/1748-9326/9/8/084006Bjerke JW, Tømmervik H, Zielke M, Jørgensen M. Impacts of snow season on ground-ice accumulation, soil frost and primary productivity in a grassland of sub-Arctic Norway. Environmental Research Letters. 2015;10:095007. doi: 10.1088/1748-9326/10/9/095007.10.1088/1748-9326/10/9/095007Bokhorst S, Bjerke JW, Davey M, et al. Impacts of extreme winter warming events on plant physiology in a sub-Arctic heath community. Physiologia Plantarum. 2010;140:128–140. doi: 10.1111/j.1399-3054.2010.01386.x.10.1111/j.1399-3054.2010.01386.x20497369Bokhorst S, Bjerke JW, Street L, Callaghan TV, Phoenix GK. Impacts of multiple extreme winter warming events on sub-Arctic heathland: phenology, reproduction, growth, and CO2 flux responses. Global Change Biology. 2011;17:2817–2830. doi: 10.1111/j.1365-2486.2011.02424.x.10.1111/j.1365-2486.2011.02424.xBokhorst S, Phoenix GK, Bjerke JW, Callaghan TV, Huyer-Brugman F, Berg MP. Extreme winter warming events more negatively impact small rather than large soil fauna: Shift in community composition explained by traits not taxa. Global Change Biology. 2012;18:1152–1162. doi: 10.1111/j.1365-2486.2011.02565.x.10.1111/j.1365-2486.2011.02565.xBokhorst S, Metcalfe DB, Wardle DA. Reduction in snow depth negatively affects decomposers but impact on decomposition rates is substrate dependent. Soil Biology & Biochemistry. 2013;62:157–164. doi: 10.1016/j.soilbio.2013.03.016.10.1016/j.soilbio.2013.03.016Borzenkova AB, Shmakin AB. Changes in the snow cover thickness and of daily snowfall intensity affecting the highways cleaning expenses in Russian cities. Ice and Snow. 2012;2:59–70.Bougamont M, Bamber JL, Ridley JK, et al. Impact of model physics on estimating the surface mass balance of the Greenland ice sheet. Geophysical Research Letters. 2007Bowden JJ, Eskildsen A, Hansen RR, Olsen K, Kurle CM, Høye TT. High-Arctic butterflies become smaller with rising temperatures. Biology Letters. 2015;11:20150574. doi: 10.1098/rsbl.2015.0574.10.1098/rsbl.2015.0574PMC465017326445981Brown LC, Duguay CR. The response and role of ice cover in lake–climate interactions. Progress in Physical Geography. 2010;34:671–704. doi: 10.1177/0309133310375653.10.1177/0309133310375653Brucker L, Markus T. Arctic-scale assessment of satellite passive microwave-derived snow depth on sea ice using Operation IceBridge airborne data. Journal of Geophysical Research-Oceans. 2013;118:2892–2905. doi: 10.1002/jgrc.20228.10.1002/jgrc.20228Brucker L, Royer A, Picard G, Langlois A, Fily M. Hourly simulations of the microwave brightness temperature of seasonal snow in Quebec, Canada, using a coupled snow evolution–emission model. Remote Sensing of Environment. 2011;115:1966–1977. doi: 10.1016/j.rse.2011.03.019.10.1016/j.rse.2011.03.019Brun E, Vionnet V, Boone A, et al. Simulation of northern Eurasian local snow depth, mass, and density using a detailed snowpack model and meteorological reanalyses. Journal of Hydrometeorology. 2013;14:203–219. doi: 10.1175/JHM-D-12-012.1.10.1175/JHM-D-12-012.1Brutel-Vuilmet C, Ménégoz M, Krinner G. An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models. The Cryosphere. 2013;7:67–80. doi: 10.5194/tc-7-67-2013.10.5194/tc-7-67-2013Bulygina ON, Groisman PY, Razuvaev VN, Radionov VF. Snow cover basal ice layer changes over Northern Eurasia since 1966. Environmental Research Letters. 2010;5:015004. doi: 10.1088/1748-9326/5/1/015004.10.1088/1748-9326/5/1/015004Caduff R, Wiesmann A, Bühler Y, Pielmeier C. Continuous monitoring of snowpack displacement at high spatial and temporal resolution with terrestrial radar interferometry. Geophysical Research Letters. 2015;42:813–820. doi: 10.1002/2014GL062442.10.1002/2014GL062442Callaghan T, Johansson M, Brown R, et al. The changing face of Arctic snow cover: A synthesis of observed and projected changes. Ambio. 2011;40:17–31. doi: 10.1007/s13280-011-0212-y.10.1007/s13280-011-0212-yCarmagnola CM, Morin S, Lafaysse M, et al. Implementation and evaluation of prognostic representations of the optical diameter of snow in the SURFEX/ISBA-Crocus detailed snowpack model. The Cryosphere. 2014;8:417–437. doi: 10.5194/tc-8-417-2014.10.5194/tc-8-417-2014Cavalieri DJ, Markus T, Ivanoff A, et al. A comparison of snow depth on sea ice retrievals using airborne altimeters and an AMSR-E simulator. IEEE Transactions on Geoscience and Remote Sensing. 2012;50:3027–3040. doi: 10.1109/TGRS.2011.2180535.10.1109/TGRS.2011.2180535Cheng B, Vihma T, Rontu L, Kontu A, Pour HK, Duguay C, Pulliainen J. Evolution of snow and ice temperature, thickness and energy balance in Lake Orajärvi, northern Finland. Tellus A. 2014Clarke GKC, Jarosch AH, Anslow FS, Radic V, Menounos B. Projected deglaciation of western Canada in the twenty-first century. Nature Geoscience. 2015;8:372–377. doi: 10.1038/ngeo2407.10.1038/ngeo2407Cohen J, Jones J, Furtado JC, Tziperman E. Warm Arctic, cold continents a common pattern related to Arctic sea ice melt, snow advance, and extreme winter weather. Oceanography. 2013;26:152–160. doi: 10.5670/oceanog.2013.70.10.5670/oceanog.2013.70Cohen J, Furtado JC, Jones J, Barlow M, Whittleston D, Entekhabi D. Linking Siberian snow cover to precursors of stratospheric variability. Journal of Climate. 2014;27:5422–5432. doi: 10.1175/JCLI-D-13-00779.1.10.1175/JCLI-D-13-00779.1Cooper EJ. Warmer shorter winters disrupt Arctic terrestrial ecosystems. Annual Review of Ecology Evolution and Systematics. 2014;45:271–295. doi: 10.1146/annurev-ecolsys-120213-091620.10.1146/annurev-ecolsys-120213-091620Crook JA, Forster PM, Stuber N. Spatial patterns of modeled climate feedback and contributions to temperature response and Polar amplification. Journal of Climate. 2011;24:3575–3592. doi: 10.1175/2011JCLI3863.1.10.1175/2011JCLI3863.1Deems JS, Painter TH, Finnegan DC. Lidar measurement of snow depth: A review. Journal of Glaciology. 2013;59:467–479. doi: 10.3189/2013JoG12J154.10.3189/2013JoG12J154Denoth A. An electronic devise for long-term snow wetness recording. Annals of Glaciology. 1994;19:104–106.Derksen C, Brown R, Mudryk L, Luojus K. Arctic: Terrestrial Snow. State of the Climate in 2014. J. Blunden and D. S. Arndt. Bulletin of the American Meteorological Society. 2015;96:133–135.Dibike Y, Prowse T, Bonsal B, de Rham L, Saloranta T. Simulation of North American lake-ice cover characteristics under contemporary and future climate conditions. International Journal of Climatology. 2012;32:695–709. doi: 10.1002/joc.2300.10.1002/joc.2300Doherty SJ, Grenfell TC, Forsstrom S, Hegg DL, Brandt RE, Warren SG. Observed vertical redistribution of black carbon and other insoluble light-absorbing particles in melting snow. Journal of Geophysical Research—Atmospheres. 2013;118:5553–5569. doi: 10.1002/jgrd.50235.10.1002/jgrd.50235Domine F, Albert M, Huthwelker T, et al. Snow physics as relevant to snow photochemistry. Atmospheric Chemistry and Physics. 2008;8:171–208. doi: 10.5194/acp-8-171-2008.10.5194/acp-8-171-2008Douglas TA, Loseto LL, Macdonald RW, et al. The fate of mercury in Arctic terrestrial and aquatic ecosystems: A review. Environmental Chemistry. 2012;9:321–355. doi: 10.1071/EN11140.10.1071/EN11140Duguay CR, Bernier M, Gauthier Y, Kouraev A. Remote sensing of lake and river ice. In: Tedesco M, editor. Remote sensing of the cryosphere. Oxford: Wiley-Blackwell; 2015. pp. 273–306.Dumont M, Brun E, Picard G, et al. Contribution of light-absorbing impurities in snow to Greenland’s darkening since 2009. Nature Geoscience. 2014;7:509–512. doi: 10.1038/ngeo2180.10.1038/ngeo2180Dupuis AP, Hann BJ. Climate change, diapause termination and zooplankton population dynamics: An experimental and modelling approach. Freshwater Biology. 2009;54:221–235. doi: 10.1111/j.1365-2427.2008.02103.x.10.1111/j.1365-2427.2008.02103.xDutra E, Balsamo G, Viterbo P, Miranda PMA, Beljaars A, Schär C, Elder K. An improved snow scheme for the ECMWF land surface model: Description and offline validation. Journal of Hydrometeorology. 2010;11:899–916. doi: 10.1175/2010JHM1249.1.10.1175/2010JHM1249.1Eckerstorfer M, Christiansen HH. Meteorology, topography and snowpack conditions causing two extreme mid-winter slush and wet slab avalanche periods in high Arctic maritime Svalbard. Permafrost and Periglacial Processes. 2012;23:15–25. doi: 10.1002/ppp.734.10.1002/ppp.734Eira IMG, Jaedicke C, Magga OH, Maynard NG, Vikhamar-Schuler D, Mathiesen SD. Traditional Sami snow terminology and physical snow classification—Two ways of knowing. Cold Regions Science and Technology. 2013;85:117–130. doi: 10.1016/j.coldregions.2012.09.004.10.1016/j.coldregions.2012.09.004Ejdys E, Biedunkiewicz A, Dynowska M, Sucharzewska E. Snow in the city as a spore bank of potentially pathogenic fungi. Science of the Total Environment. 2014;470:646–650. doi: 10.1016/j.scitotenv.2013.10.045.10.1016/j.scitotenv.2013.10.04524176713Essery R. Large-scale simulations of snow albedo masking by forests. Geophysical Research Letters. 2013;40:5521–5525. doi: 10.1002/grl.51008.10.1002/grl.51008Essery R, Morin S, Lejeune Y, Ménard CB. A comparison of 1701 snow models using observations from an alpine site. Advances in Water Resources. 2013;55:131–148. doi: 10.1016/j.advwatres.2012.07.013.10.1016/j.advwatres.2012.07.013Fiddes J, Gruber S. TopoSCALE v. 1.0: Downscaling gridded climate data in complex terrain. Geoscientific Model Development. 2014;7:387–405. doi: 10.5194/gmd-7-387-2014.10.5194/gmd-7-387-2014Fierz, C.R., R.L. Armstrong, Y. Durand, P. Etchevers, E. Greene, D.M. McClung, K. Nishimura, P.K. Satyawali, and S.A. Sokratov. 2009. The international classification for seasonal snow on the ground. Paper presented at the UNESCO-IHP, Paris, France.Fletcher CG, Thackeray CW, Burgers TM. Evaluating biases in simulated snow albedo feedback in two generations of climate models. Journal of Geophysical Research—Atmospheres. 2015;120:12–26. doi: 10.1002/2014JD022546.10.1002/2014JD022546Francis JA, Vavrus SJ. Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophysical Research Letters. 2012;39:L06801. doi: 10.1029/2012GL051000.10.1029/2012GL051000Fuller MC, Geldsetzer T, Yackel JJ. Surface-based polarimetric C-band microwave scatterometer measurements of snow during a Chinook event. IEEE Transactions on Geoscience and Remote Sensing. 2009;47:1766–1776. doi: 10.1109/TGRS.2008.2006684.10.1109/TGRS.2008.2006684Gallet JC, Domine F, Zender CS, Picard G. Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm. Cryosphere. 2009;3:167–182. doi: 10.5194/tc-3-167-2009.10.5194/tc-3-167-2009Hachikubo A, Yamaguchi S, Arakawa H, et al. Effects of temperature and grain type on time variation of snow specific surface area. Bulletin of Glaciological Research. 2014;32(1):33–45.Hall, D.K., G.A. Riggs, and V.V. Salomonson. 2006. MODIS snow and sea ice products. In Earth science satellite remote sensing, vol. I: Science and Instruments, ed. J.J. Qu, W. Gao, M. Kafatos, R.E. Murphy, and V.V. Salomonson, 154–181. New York: Springer.Hall DK, Riggs GA, Salomonson VV, DiGirolamo NE, Bayr KJ. MODIS snow-cover products. Remote Sensing of Environment. 2002;83:181–194. doi: 10.1016/S0034-4257(02)00095-0.10.1016/S0034-4257(02)00095-0Hanbali RM. Economic impact of winter road maintenance on road users. Transportation Research Record. 1994;1442:151–161.Hanewinkel M, Hummel S, Albrecht A. Assessing natural hazards in forestry for risk management: A review. European Journal of Forest Research. 2011;130:329–351. doi: 10.1007/s10342-010-0392-1.10.1007/s10342-010-0392-1Hansen BB, Grøtan V, Aanes R, et al. Climate events synchronize the dynamics of a resident vertebrate community in the high Arctic. Science. 2013;339:313–315. doi: 10.1126/science.1226766.10.1126/science.122676623329044Hansen BB, Isaksen K, Benestad RE, et al. Warmer and wetter winters: Characteristics and implications of an extreme weather event in the High Arctic. Environmental Research Letters. 2014;9:114021. doi: 10.1088/1748-9326/9/11/114021.10.1088/1748-9326/9/11/114021Havens S, Marshall H-P, Johnson JB, Nicholson B. Calculating the velocity of a fast-moving snow avalanche using an infrasound array. Geophysical Research Letters. 2014;41:6191–6198. doi: 10.1002/2014GL061254.10.1002/2014GL061254Haynes KM, Mitchell CPJ. Inter-annual and spatial variability in hillslope runoff and mercury flux during spring snowmelt. Journal of Environmental Monitoring. 2012;14:2083–2091. doi: 10.1039/c2em30267e.10.1039/c2em30267e22739974Heilig A, Schneebeli M, Eisen O. Upward-looking ground-penetrating radar for monitoring snowpack stratigraphy. Cold Regions Science and Technology. 2009;59:152–162. doi: 10.1016/j.coldregions.2009.07.008.10.1016/j.coldregions.2009.07.008Heilig A, Mitterer C, Schmid L, Wever N, Schweizer J, Marshall HP, Eisen O. Seasonal and diurnal cycles of liquid water in snow—Measurements and modeling. Journal of Geophysical Research: Earth Surface. 2015Holemann JA, Schirmacher M, Prange A. Seasonal variability of trace metals in the Lena River and the southeastern Laptev Sea: Impact of the spring freshet. Global and Planetary Change. 2005;48:112–125. doi: 10.1016/j.gloplacha.2004.12.008.10.1016/j.gloplacha.2004.12.008Hori M, Aoki T, Tanikawa T, Kuchiki K, Niwano M, Yamaguchi S, Matoba S. Dependence of thermal infrared emissive behaviors of snow cover on the surface snow type. Bulletin of Glaciological Research. 2014;32:33–45. doi: 10.5331/bgr.32.33.10.5331/bgr.32.33Ims RA, Yoccoz NG, Killengreen ST. Determinants of lemming outbreaks. Proceedings of the National Academy of Sciences of the United States of America. 2011;108:1970–1974. doi: 10.1073/pnas.1012714108.10.1073/pnas.1012714108PMC303325121245340Jeelani G, Feddema JJ, van der Veen CJ, Stearns L. Role of snow and glacier melt in controlling river hydrology in Liddar watershed (western Himalaya) under current and future climate. Water Resources Research. 2012;48:W12508. doi: 10.1029/2011WR011590.10.1029/2011WR011590Jiao C, Flanner MG, Balkanski Y, et al. An AeroCom assessment of black carbon in Arctic snow and sea ice. Atmospheric Chemistry and Physics. 2014;14:2399–2417. doi: 10.5194/acp-14-2399-2014.10.5194/acp-14-2399-2014Johansson C, Pohjola VA, Jonasson C, Callaghan TV. Multi-decadal changes in snow characteristics in sub-Arctic Sweden. Ambio. 2011;40:566–574. doi: 10.1007/s13280-011-0164-2.10.1007/s13280-011-0164-2PMC335786321954720Johansson M, Jonasson C, Sonesson M, Christensen TR. The man, the myth, the legend: Professor Terry V. Callaghan and his 3M concept. Ambio. 2012;41:175–177. doi: 10.1007/s13280-012-0300-7.10.1007/s13280-012-0300-7PMC353506422864691Kapnick SB, Delworth TL. Controls of global snow under a changed climate. Journal of Climate. 2013;26:5537–5562. doi: 10.1175/JCLI-D-12-00528.1.10.1175/JCLI-D-12-00528.1Kelly R. The AMSR-E snow depth algorithm: Description and initial results. Journal of The Remote Sensing Society of Japan. 2009;29:307–317.Koch F, Prasch M, Schmid L, Schweizer J, Mauser W. Measuring snow liquid water content with low-cost GPS receivers. Sensors. 2014;14:20975–20999. doi: 10.3390/s141120975.10.3390/s141120975PMC427952125384007Koven CD, Riley WJ, Stern A. Analysis of permafrost thermal dynamics and response to climate change in the CMIP5 earth system models. Journal of Climate. 2013;26:1877–1900. doi: 10.1175/JCLI-D-12-00228.1.10.1175/JCLI-D-12-00228.1Krenke AN, Cherenkova EA, Chernavskaya MM. Stability of snow cover on the territory of Russia in relation to climate change. Ice and Snow. 2012;1:29–37.Kuipers Munneke P, van den Broeke MR, Lenaerts JTM, Flanner MG, Gardner AS, van de Berg WJ. A new albedo parameterization for use in climate models over the Antarctic ice sheet. Journal of Geophysical Research: Atmospheres. 2011Kumar M, Marks D, Dozier J, Reba M, Winstral A. Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models. Advances in Water Resources. 2013;56:77–89. doi: 10.1016/j.advwatres.2013.03.006.10.1016/j.advwatres.2013.03.006Kurtz N, Richter-Menge J, Farrell S, Studinger M, Paden J, Sonntag J, Yungel J. IceBridge airborne survey data support Arctic sea ice predictions. Eos, Transactions American Geophysical Union. 2013;94:41–41. doi: 10.1002/2013EO040001.10.1002/2013EO040001Langford H, Hodson A, Banwart S, Boggild C. The microstructure and biogeochemistry of Arctic cryoconite granules. Annals of Glaciology. 2010;51:87–94. doi: 10.3189/172756411795932083.10.3189/172756411795932083Lawrence DM, Swenson SC. Permafrost response to increasing Arctic shrub abundance depends on the relative influence of shrubs on local soil cooling versus large-scale climate warming. Environmental Research Letters. 2011;6:045504. doi: 10.1088/1748-9326/6/4/045504.10.1088/1748-9326/6/4/045504Leinss S, Parrella G, Hajnsek I. Snow height determination by polarimetric phase differences in X-Band SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2014;7:3794–3810. doi: 10.1109/JSTARS.2014.2323199.10.1109/JSTARS.2014.2323199Liston GE, Hiemstra CA. The changing cryosphere: Pan-Arctic snow trends (1979–2009) Journal of Climate. 2011;24:5691–5712. doi: 10.1175/JCLI-D-11-00081.1.10.1175/JCLI-D-11-00081.1Lund-Hansen L, Hawes I, Sorrell B, Nielsen M. Removal of snow cover inhibits spring growth of Arctic ice algae through physiological and behavioral effects. Polar Biology. 2014;37:471–481. doi: 10.1007/s00300-013-1444-z.10.1007/s00300-013-1444-zLutz S, Anesio AM, Villar SEJ, Benning LG. Variations of algal communities cause darkening of a Greenland glacier. FEMS Microbiology Ecology. 2014;89:402–414. doi: 10.1111/1574-6941.12351.10.1111/1574-6941.1235124920320Marks D, Winstral A, Reba M, Pomeroy J, Kumar M. An evaluation of methods for determining during-storm precipitation phase and the rain/snow transition elevation at the surface in a mountain basin. Advances in Water Resources. 2013;55:98–110. doi: 10.1016/j.advwatres.2012.11.012.10.1016/j.advwatres.2012.11.012Marshall H-P, Koh G, Forster RR. Estimating alpine snowpack properties using FMCW radar. Annals of Glaciology. 2005;40:157–162. doi: 10.3189/172756405781813500.10.3189/172756405781813500Matsumoto N, Hoshino T. Fungi in snow environments: psychrophilic molds—A group of pathogens affecting plants under snow. Enfield: Science Publishers Inc; 2009.Matsumura S, Zhang X, Yamazaki K. Summer Arctic atmospheric circulation response to spring Eurasian snow cover and its possible linkage to accelerated sea ice decrease. Journal of Climate. 2014;27:6551–6558. doi: 10.1175/JCLI-D-13-00549.1.10.1175/JCLI-D-13-00549.1Matzl M, Schneebeli M. Measuring specific surface area of snow by near-infrared photography. Journal of Glaciology. 2006;52:558–564. doi: 10.3189/172756506781828412.10.3189/172756506781828412McCreight JL, Small EE, Larson KM. Snow depth, density, and SWE estimates derived from GPS reflection data: Validation in the western U.S. Water Resources Research. 2014;50:6892–6909. doi: 10.1002/2014WR015561.10.1002/2014WR015561McKinnon L, Berteaux D, Gauthier G, Bêty J. Predator-mediated interactions between preferred, alternative and incidental prey in the arctic tundra. Oikos. 2013;122:1042–1048. doi: 10.1111/j.1600-0706.2012.20708.x.10.1111/j.1600-0706.2012.20708.xMeltofte, H. 2013. Arctic biodiversity assessment. Status and trends in Arctic biodiversity. Akureyri: Conservation of Arctic Flora and Fauna.Menard CB, Essery R, Pomeroy J. Modelled sensitivity of the snow regime to topography, shrub fraction and shrub height. Hydrology and Earth System Sciences. 2014;18:2375–2392. doi: 10.5194/hess-18-2375-2014.10.5194/hess-18-2375-2014Michel D, Philipona R, Ruckstuhl C, Vogt R, Vuilleumier L. Performance and uncertainty of CNR1 net radiometers during a one-year field comparison. Journal of Atmospheric and Oceanic Technology. 2008;25:442–451. doi: 10.1175/2007JTECHA973.1.10.1175/2007JTECHA973.1Mitterer C, Heilig A, Schweizer J, Eisen O. Upwardlooking ground-penetrating radar for measuring wet-snow properties. Cold Regions Science and Technology. 2011;69:129–138. doi: 10.1016/j.coldregions.2011.06.003.10.1016/j.coldregions.2011.06.003Mizukami N, Koren V, Smith M, Kingsmill D, Zhang Z, Cosgrove B, Cui Z. The impact of precipitation type discrimination on hydrologic simulation: Rain–snow partitioning derived from HMT-West radar-detected brightband height versus surface temperature data. Journal of Hydrometeorology. 2013;14:1139–1158. doi: 10.1175/JHM-D-12-035.1.10.1175/JHM-D-12-035.1Montpetit B, Royer A, Langlois A, et al. New shortwave infrared albedo measurements for snow specific surface area retrieval. Journal of Glaciology. 2012;58:941–952. doi: 10.3189/2012JoG11J248.10.3189/2012JoG11J248Mudryk LR, Derksen C, Kushner PJ, Brown R. Characterization of northern hemisphere snow water equivalent datasets, 1981–2010. Journal of Climate. 2015;28:8037–8051. doi: 10.1175/JCLI-D-15-0229.1.10.1175/JCLI-D-15-0229.1Myers-Smith IH, Hik DS. Shrub canopies influence soil temperatures but not nutrient dynamics: An experimental test of tundra snow–shrub interactions. Ecology and Evolution. 2013;3:3683–3700. doi: 10.1002/ece3.710.10.1002/ece3.710PMC381086824198933Myers-Smith IH, Forbes BC, Wilmking M, et al. Shrub expansion in tundra ecosystems: Dynamics, impacts and research priorities. Environmental Research Letters. 2011;6:045509. doi: 10.1088/1748-9326/6/4/045509.10.1088/1748-9326/6/4/045509Niemi, J., and J. Ahlstedt. 2012. Finnish agriculture and rural industries, 112a. Helsinki: Agrifood Research.Nolet BA, Bauer S, Feige N, Kokorev YI, Popov IY, Ebbinge BS. Faltering lemming cycles reduce productivity and population size of a migratory Arctic goose species. Journal of Animal Ecology. 2013;82:804–813. doi: 10.1111/1365-2656.12060.10.1111/1365-2656.12060PMC374476223419215Oleson, K.W., D.M. Lawrence, B. Gordon, et al. 2010. Technical description of version 4.0 of the community land model (CLM). NCAR Technical Notes.Panzer B, Gomez-Garcia D, Leuschen C, et al. An ultra-wideband, microwave radar for measuring snow thickness on sea ice and mapping near-surface internal layers in polar firn. Journal of Glaciology. 2013;59:244–254. doi: 10.3189/2013JoG12J128.10.3189/2013JoG12J128Parham PE, Waldock J, Christophides GK, et al. Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission. Philosophical Transactions of the Royal Society of London B: Biological Sciences. 2015;370:20130557. doi: 10.1098/rstb.2013.0557.10.1098/rstb.2013.0557PMC434295725688012Pearson RG, Phillips SJ, Loranty MM, Beck PSA, Damoulas T, Knight SJ, Goetz SJ. Shifts in Arctic vegetation and associated feedbacks under climate change. Nature Climate Change. 2013;3:673–677. doi: 10.1038/nclimate1858.10.1038/nclimate1858Pedersen SH, Liston GE, Tamstorf MP, Westergaard-Nielsen A, Schmidt NM. Quantifying episodic snowmelt events in Arctic ecosystems. Ecosystems. 2015;18:839–856. doi: 10.1007/s10021-015-9867-8.10.1007/s10021-015-9867-8Picard G, Royer A, Arnaud L, Fily M. Influence of meter-scale wind-formed features on the variability of the microwave brightness temperature around Dome C in Antarctica. The Cryosphere. 2014;8:1105–1119. doi: 10.5194/tc-8-1105-2014.10.5194/tc-8-1105-2014Pinzer BR, Schneebeli M, Kaempfer TU. Vapor flux and recrystallization during dry snow metamorphism under a steady temperature gradient as observed by time-lapse micro-tomography. Cryosphere. 2012;6:1141–1155. doi: 10.5194/tc-6-1141-2012.10.5194/tc-6-1141-2012Popova VV. The snow storage contribution in major changes of river runoff in the Arctic Ocean drainage basin during the current warming period. Ice and Snow. 2011;51:69–78.Preece C, Callaghan TV, Phoenix GK. Impacts of winter icing events on the growth, phenology and physiology of sub-arctic dwarf shrubs. Physiologia Plantarum. 201222568724Proksch M, Löwe H, Schneebeli M. Density, specific surface area, and correlation length of snow measured by high-resolution penetrometry. Journal of Geophysical Research: Earth Surface. 2015;120:346–362.Prowse TD, Brown K. Hydro-ecological effects of changing Arctic river and lake ice covers: A review. Hydrological Research. 2010;41:454–461. doi: 10.2166/nh.2010.142.10.2166/nh.2010.142Prowse T, Alfredsen K, Beltaos S, et al. Past and future changes in Arctic lake and river ice. Ambio. 2011;40:53–62. doi: 10.1007/s13280-011-0216-7.10.1007/s13280-011-0216-7Qian Y, Yasunari TJ, Doherty SJ, et al. Light-absorbing particles in snow and ice: Measurement and modeling of climatic and hydrological impact. Advances in Atmospheric Sciences. 2015;32:64–91. doi: 10.1007/s00376-014-0010-0.10.1007/s00376-014-0010-0Qiu J. Avalanche hotspot revealed. Nature. 2014;509:142–143. doi: 10.1038/509142a.10.1038/509142a24805323Qu X, Hall A. On the persistent spread in snow-albedo feedback. Climate Dynamics. 2014;42:69–81. doi: 10.1007/s00382-013-1774-0.10.1007/s00382-013-1774-0Rautio M, Mariash H, Forsström L. Seasonal shifts between autochthonous and allochthonous carbon contributions to zooplankton diets in a subarctic lake. Limnology and Oceanography. 2011;56:1513–1524. doi: 10.4319/lo.2011.56.4.1513.10.4319/lo.2011.56.4.1513Reiweger I, Mayer K, Steiner K, Dual J, Schweizer J. Measuring and localizing acoustic emission events in snow prior to fracture. Cold Regions Science and Technology. 2015;110:160–169. doi: 10.1016/j.coldregions.2014.12.002.10.1016/j.coldregions.2014.12.002Rennert KJ, Roe G, Putkonen J, Bitz CM. Soil thermal and ecological impacts of rain on snow events in the circumpolar Arctic. Journal of Climate. 2009;22:2302–2315. doi: 10.1175/2008JCLI2117.1.10.1175/2008JCLI2117.1Riehm M, Nordin L. Optimization of winter road maintenance energy costs in Sweden: A critique of site specific frost warning techniques. Meteorological Applications. 2012;19:443–453. doi: 10.1002/met.275.10.1002/met.275Riseth JA, Tømmervik H, Helander-Renvall E, et al. Sámi traditional ecological knowledge as a guide to science: Snow, ice and reindeer pasture facing climate change. Polar Record. 2011;47:202–217. doi: 10.1017/S0032247410000434.10.1017/S0032247410000434Rouse WR, Blanken PD, Duguay CR, Oswald CJ, Schertzer WM. Climate lake interactions. In: Woo MK, editor. Cold region atmospheric and hydrologic studies: The Mackenzie GEWEX experience. Berlin: Springer; 2008. pp. 139–160.Roy A, Picard G, Royer A, et al. Brightness temperature simulations of the Canadian seasonal snowpack driven by measurements of the snow specific surface area. IEEE Transactions on Geoscience and Remote Sensing. 2013;51:4692–4704. doi: 10.1109/TGRS.2012.2235842.10.1109/TGRS.2012.2235842Rumpf SB, Semenchuk PR, Dullinger S, Cooper EJ. Idiosyncratic responses of high Arctic plants to changing snow regimes. PLoS ONE. 2014;9:10. doi: 10.1371/journal.pone.0086281.10.1371/journal.pone.0086281PMC392110824523859Saloranta TM. Simulating snow maps for Norway: Description and statistical evaluation of the seNorge snow model. The Cryosphere. 2012;6:1323–1337. doi: 10.5194/tc-6-1323-2012.10.5194/tc-6-1323-2012Schmid L, Heilig A, Mitterer C, Schweizer J, Maurer H, Okorn R, Eisen O. Continuous snowpack monitoring using upward-looking ground-penetrating radar technology. Journal of Glaciology. 2014;60:509–525. doi: 10.3189/2014JoG13J084.10.3189/2014JoG13J084Schmidt NM, Ims RA, Høye TT, et al. Response of an arctic predator guild to collapsing lemming cycles. Proceedings of the Royal Society B—Biological Sciences. 2012;279:4417–4422. doi: 10.1098/rspb.2012.1490.10.1098/rspb.2012.1490PMC347980222977153Schneebeli M, Johnson JB. A constant-speed penetrometer for high-resolution snow stratigraphy. Annals of Glaciology. 1998;26:107–111.Screen JA. Arctic amplification decreases temperature variance in northern mid- to high-latitudes. Nature Climate Change. 2014;4:577–582. doi: 10.1038/nclimate2268.10.1038/nclimate2268Screen JA, Simmonds I. Declining summer snowfall in the Arctic: Causes, impacts and feedbacks. Climate Dynamics. 2012;38:2243–2256. doi: 10.1007/s00382-011-1105-2.10.1007/s00382-011-1105-2Semenchuk PR, Elberling B, Cooper EJ. Snow cover and extreme winter warming events control flower abundance of some, but not all species in high arctic Svalbard. Ecology and Evolution. 2013;3:2586–2599. doi: 10.1002/ece3.648.10.1002/ece3.648PMC393005024567826Semenov VA. Climate-related change of snow contribution in the development of dangerous hydrological phenomena on rivers. Ice and Snow. 2013;53:107–112.Semmens KA, Ramage J, Bartsch A, Liston GE. Early snowmelt events: Detection, distribution, and significance in a major sub-arctic watershed. Environmental Research Letters. 2013;8:11. doi: 10.1088/1748-9326/8/1/014020.10.1088/1748-9326/8/1/014020Serreze MC, Barry RG. Processes and impacts of Arctic amplification: A research synthesis. Global and Planetary Change. 2011;77:85–96. doi: 10.1016/j.gloplacha.2011.03.004.10.1016/j.gloplacha.2011.03.004Shen FX, Yao MS. Are we biologically safe with snow precipitation? A case study in Beijing. PLoS ONE. 2013;8:11. doi: 10.1371/annotation/e2536fcb-3ab3-44a0-8eab-91aaeb8e49b6.10.1371/annotation/e2536fcb-3ab3-44a0-8eab-91aaeb8e49b6PMC367514623762327Shnyparkov A, Fuchs S, Sokratov S, Koltermann K, Seliverstov YG, Vikulina M. Theory and practice of individual snow avalanche risk assessment in the Russian Arctic. Geography, Environment, Sustainability. 2012;5:64–81. doi: 10.15356/2071-9388_03v05_2012_05.10.15356/2071-9388_03v05_2012_05Sihvola A, Tiuri M. Snow fork for field determination of the density and wetness profiles of a snow pack. IEEE Transactions on Geoscience and Remote Sensing. 1986;GE-24:717–721. doi: 10.1109/TGRS.1986.289619.10.1109/TGRS.1986.289619Simon A, Poulin MB, Rousseau AN, Ogden NH. Fate and transport of Toxoplasma gondii Oocysts in seasonally snow covered watersheds: A conceptual framework from a melting snowpack to the Canadian Arctic coasts. International Journal of Environmental Research and Public Health. 2013;10:994–1005. doi: 10.3390/ijerph10030994.10.3390/ijerph10030994PMC370929923478399Slater AG, Lawrence DM. Diagnosing present and future permafrost from climate models. Journal of Climate. 2013;26:5608–5623. doi: 10.1175/JCLI-D-12-00341.1.10.1175/JCLI-D-12-00341.1Sosnovsky AV, Nakalov PR, Nenashev SV. Physical-geographical aspects of formation of artificial firn-ice massives. Ice and Snow. 2014;54:113–119.Stacheder M. TDR and low-frequency measurements for continuous monitoring of moisture and density in a snow pack. International Agrophysics. 2005;19:75–78.Steffen K, Nghiem SV, Huff R, Neumann G. The melt anomaly of 2002 on the Greenland Ice Sheet from active and passive microwave satellite observations. Geophysical Research Letters. 2004Stien A, Ims RA, Albon SD, et al. Congruent responses to weather variability in high arctic herbivores. Biology Letters. 2012;8:1002–1005. doi: 10.1098/rsbl.2012.0764.10.1098/rsbl.2012.0764PMC349714523015455Sturm M, Maslanik JA, Perovich DK, Stroeve JC, Richter-Menge J, Markus T, Holmgren J, Heinrichs JF, Tape K. Snow depth and ice thickness measurements from the Beaufort and Chukchi seas collected during the AMSR-Ice03 campaign. IEEE Transactions on Geoscience and Remote Sensing. 2006;44:3009–3020. doi: 10.1109/TGRS.2006.878236.10.1109/TGRS.2006.878236Surdu CM, Duguay CR, Brown LC, Fernández Prieto D. Response of ice cover on shallow lakes of the North Slope of Alaska to contemporary climate conditions (1950–2011): Radar remote-sensing and numerical modeling data analysis. The Cryosphere. 2014;8:167–180. doi: 10.5194/tc-8-167-2014.10.5194/tc-8-167-2014Takala M, Luojus K, Pulliainen J, et al. Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sensing of Environment. 2011;115:3517–3529. doi: 10.1016/j.rse.2011.08.014.10.1016/j.rse.2011.08.014Tang Q, Zhang X, Yang X, Francis JA. Cold winter extremes in northern continents linked to Arctic sea ice loss. Environmental Research Letters. 2013;8:014036. doi: 10.1088/1748-9326/8/1/014036.10.1088/1748-9326/8/1/014036Tedesco M, Fettweis X, Mote T, Wahr J, Alexander P, Box JE, Wouters B. Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional climate model and reanalysis data. Cryosphere. 2013;7:615–630. doi: 10.5194/tc-7-615-2013.10.5194/tc-7-615-2013Terzago S, von Hardenberg J, Palazzi E, Provenzale A. Snowpack changes in the Hindu Kush-Karakoram-Himalaya from CMIP5 global climate models. Journal of Hydrometeorology. 2014;15:2293–2313. doi: 10.1175/JHM-D-13-0196.1.10.1175/JHM-D-13-0196.1Thackeray CW, Fletcher CG, Derksen C. The influence of canopy snow parameterizations on snow albedo feedback in boreal forest regions. Journal of Geophysical Research: Atmospheres. 2014;119:9810–9821.Urban M, Forkel M, Eberle J, Huettich C, Schmullius C, Herold M. Pan-Arctic climate and land cover trends derived from multi-variate and multi-scale analyses (1981–2012) Remote Sensing. 2014;6:2296–2316. doi: 10.3390/rs6032296.10.3390/rs6032296Van Den Broeke M, Bus C, Ettema J, Smeets P. Temperature thresholds for degree-day modelling of Greenland ice sheet melt rates. Geophysical Research Letters. 2010;37:L18501.Van Herwijnen A, Schweizer J. Seismic sensor array for monitoring an avalanche start zone: Design, deployment and preliminary results. Journal of Glaciology. 2011;57:267–276. doi: 10.3189/002214311796405933.10.3189/002214311796405933Vikhamar-Schuler D, Hanssen-Bauer I, Schuler TV, Mathiesen SD, Lehning M. Use of a multilayer snow model to assess grazing conditions for reindeer. Annals of Glaciology. 2013;54:214–226. doi: 10.3189/2013AoG62A306.10.3189/2013AoG62A306Vionnet V, Brun E, Morin S, et al. The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2. Geoscientific Model Development. 2012;5:773–791. doi: 10.5194/gmd-5-773-2012.10.5194/gmd-5-773-2012Walsh JE. Intensified warming of the Arctic: Causes and impacts on middle latitudes. Global and Planetary Change. 2014;117:52–63. doi: 10.1016/j.gloplacha.2014.03.003.10.1016/j.gloplacha.2014.03.003Webster MA, Rigor IG, Nghiem SV, Kurtz NT, Farrell SL, Perovich DK, Sturm M. Interdecadal changes in snow depth on Arctic sea ice. Journal of Geophysical Research-Oceans. 2014;119:5395–5406. doi: 10.1002/2014JC009985.10.1002/2014JC009985Wever N, Fierz C, Mitterer C, Hirashima H, Lehning M. Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model. The Cryosphere. 2014;8:257–274. doi: 10.5194/tc-8-257-2014.10.5194/tc-8-257-2014Wilson RR, Bartsch A, Joly K, Reynolds JH, Orlando A, Loya WM. Frequency, timing, extent, and size of winter thaw-refreeze events in Alaska 2001–2008 detected by remotely sensed microwave backscatter data. Polar Biology. 2013;36:419–426. doi: 10.1007/s00300-012-1272-6.10.1007/s00300-012-1272-6
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1660-46011332016Feb24International journal of environmental research and public healthInt J Environ Res Public HealthThe Use of Carbonaceous Particle Exposure Metrics in Health Impact Calculations.24910.3390/ijerph13030249Combustion-related carbonaceous particles seem to be a better indicator of adverse health effects compared to PM2.5 and PM10. Historical studies are based on black smoke (BS), but more recent studies use absorbance (Abs), black carbon (BC) or elemental carbon (EC) as exposure indicators. To estimate health risks based on BS, we review the literature regarding the relationship between Abs, BS, BC and EC. We also discuss the uncertainties associated with the comparison of relative risks (RRs) based on these conversions. EC is reported to represent a proportion between 5.2% and 27% of BS with a mean value of 12%. Correlations of different metrics at one particular site are higher than when different sites are compared. Comparing all traffic, urban and rural sites, there is no systematic site dependence, indicating that other properties of the particles or errors affect the measurements and obscure the results. It is shown that the estimated daily mortality associated with short-term levels of EC is in the same range as PM10, but this is highly dependent on the EC to BS relationship that is used. RRs for all-cause mortality associated with short-term exposure to PM10 seem to be higher at sites with higher EC concentrations, but more data are needed to verify this.OlstrupHenrikHAtmospheric Science Unit, Department of Environmental Science and Analytical Chemistry, Stockholm University, 11418 Stockholm, Sweden. henrik.olstrup@aces.su.se.JohanssonChristerCAtmospheric Science Unit, Department of Environmental Science and Analytical Chemistry, Stockholm University, 11418 Stockholm, Sweden. christer.johansson@aces.su.se.Environment and Health Administration, SLB, Box 8136, 104 20 Stockholm, Sweden. christer.johansson@aces.su.se.ForsbergBertilBDivision of Occupational and Environmental Medicine, Department of Public Health and Clinical Medicine, Umeå University, 90187 Umeå, Sweden. bertil.forsberg@envmed.umu.se.engJournal Article20160224
SwitzerlandInt J Environ Res Public Health1012384551660-46010Air Pollutants0Vehicle Emissions7440-44-0CarbonIMAir PollutantsanalysisCarbonanalysisCitiesEnvironmental MonitoringmethodsHealth Status IndicatorsPublic Healthstatistics & numerical dataRisk AssessmentVehicle EmissionsanalysisPM10black carbonblack smokecombustion-related particleselemental carbonhealth effectsrelative riskurban air pollution
2015982016212201621520163260201632602016101960201631epublish26927139PMC480891210.3390/ijerph13030249ijerph13030249Janssen N.A.H., Hoek G., Simic-Lawson M., Fischer P., van Bree L., ten Brink H., Keuken M., Atkinson R.W., Anderson H.R., Brunekreef B., et al. Black Carbon as an Additional Indicator of the Adverse Health Effects of Airborne Particles Compared with PM10 and PM2.5. Environ. Health Perspect. 2011;119:1691–1699. doi: 10.1289/ehp.1003369.10.1289/ehp.1003369PMC326197621810552Janssen N.A.H., Gerlofs-Nijland M.E., Lanki T., Salonen R.O., Cassee F., Hoek G., Fischer P., Brunekreef B., Krzyzanowski M. Health Effects of Black Carbon. World Health Organization Regional Office for Europe; Copenhagen, Denmark: 2012.Bond T.C., Doherty S.J., Fahey D.W., Forster P.M., Berntsen T., DeAngelo B.J., Flanner M.G., Ghan S., Kärcher B., Koch D., et al. Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. 2013;118:5380–5552. doi: 10.1002/jgrd.50171.10.1002/jgrd.50171Quincey P.A. Relationship between Black Smoke Index and Black Carbon concentration. Atmos. Environ. 2007;41:7964–7968. doi: 10.1016/j.atmosenv.2007.09.033.10.1016/j.atmosenv.2007.09.033Andersen H.R. Air pollution and mortality: A history. Atmos. Environ. 2009;43:142–152. doi: 10.1016/j.atmosenv.2008.09.026.10.1016/j.atmosenv.2008.09.026OECD . Methods of Measuring Air Pollution. OECD; Paris, France: 1964. Report of the Working Party on Methods of Measuring Air Pollution and Survey Techniques. Directorate for Scientific Affairs, Organization for Economic Co-operation and Development. Working Party on Methods of Measuring Air Pollution and Survey Techniques.Biersterker K., Bustueva K.A., Camner P., Friberg L., Fugas M., Horton R.J.M. Sulfur Oxides and Suspended Particulate Matter. World Health Organization; Geneva, Switzerland: 1979. International Programme on Chemical Safety.Bell M.L., Ebisu E., Peng R.D., Samet J.M., Dominici F. Hospital admission and chemical composition of fine particle air pollution. Am. J. Resp. Crit. Care Med. 2009;179:1115–1120. doi: 10.1164/rccm.200808-1240OC.10.1164/rccm.200808-1240OCPMC269549719299499Grahame T.J., Klemm R., Schlesinger R.B. Public health and components of particulate matter: The changing assessment of black carbon. J. Air Waste Manag. Assoc. 2014;64:620–660. doi: 10.1080/10962247.2014.912692.10.1080/10962247.2014.91269225039199Lavanchy V.M.H., Gäggeler H.W., Nyeki S., Baltensperger U. Elemental carbon (EC) and black carbon (BC) measurements with a thermal method and an aethalometer at the high-alpine research station Jungfraujoch. Atmos. Environ. 1999;33:2759–2769. doi: 10.1016/S1352-2310(98)00328-8.10.1016/S1352-2310(98)00328-8Seinfeld H., Pandis S.N. Atmospheric Chemistry and Physics—From Air Pollution to Climate Change. 2nd ed. John Wiley & Inc.; Hoboken, NJ, USA: 2006. pp. 370–373, 628–634.Chow J.C., Watson J.G., Chen L.-W.A., Arnott W.P., Moosmüller H. Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. Environ. Sci. Tech. 2004;38:4414–4422. doi: 10.1021/es034936u.10.1021/es034936u15382872Ruzar L.S., Harley N.H. Aerosols Handbook, Measurement, Dosimetry, and Health Effects. 2nd ed. CRC Press, Taylor & Francis Group; Boca Raton, FL, USA: 2013. p. 189.Cavalli F., Viana M., Yttri K.E., Genberg J., Putaud J.P. Toward a standardised thermal-optical protocol for measuring atmospheric organic and elemental carbon: The EUSAAR protocol. Atmos. Meas. Tech. 2010;3:79–89. doi: 10.5194/amt-3-79-2010.10.5194/amt-3-79-2010Schmid H., Laskus L., Abraham H.J., Baltensperger U., Lavanchy V., Bizjak M. Results of the “carbon conference” international aerosol carbon round robin test stage 1. Atmos. Environ. 2001;35:2111–2121. doi: 10.1016/S1352-2310(00)00493-3.10.1016/S1352-2310(00)00493-3Hansson H.C., Nyquist G., Rosman K. Utvärdering av Sotmätningar Utförda Enligt OECD-Metoden. Resultat Från Mätningar i Stockholm Mars-Maj 1996. ITM Rapport 58. Institutet för tillämpad miljöforskning, Stockholms Universitet; Stockholm, Sweden: 1997.Zheng G.J., Cheng Y., He K.B., Duan F.K., Ma Y.L. A newly identified calculation discrepancy of the Sunset semi-continuous carbon analyzer. Atmos. Meas. Tech. 2014;7:1969–1977. doi: 10.5194/amt-7-1969-2014.10.5194/amt-7-1969-2014Kath H.-G. Soot measurements in Saxon air monitoring sites with the thermal-optical method and the EUSAAR-II-Programme; Proceedings of the Workshop on Measurement Methods and Perspectives; Leipzig, Germany. 8 October 2014.Erdman A., Israel G., Ulrich E. Comparative measurements of atmospheric elemental carbon using the British Black Smoke sampler and a thermal carbon analyser. Staub. 1993;53:183–191.Schaap M., Denier van der Gon H.A.C. On the variability of black smoke and carbonaceous aerosols in the Netherlands. Atmos. Environ. 2007;41:5908–5920. doi: 10.1016/j.atmosenv.2007.03.042.10.1016/j.atmosenv.2007.03.042Lena T.S., Ochieng V., Carter M., Holguin-Veras J., Kinney P.L. Elemental Carbon and PM2.5 levels in an urban community heavily impacted by truck traffic. Environ. Health Perspect. 2002;110:1009–1015. doi: 10.1289/ehp.021101009.10.1289/ehp.021101009PMC124102712361926Ballach J., Hitzenberger R., Schultz E., Jaeschke W. Development of an improved optical transmission technique for black carbon (BC) analysis. Atmos. Environ. 2001;35:2089–2100. doi: 10.1016/S1352-2310(00)00499-4.10.1016/S1352-2310(00)00499-4European Environment Agency . Status of Black Carbon Monitoring in Ambient Air in Europe. European Environment Agency; Copenhagen, Denmark: 2013. EEA Technical Report 18/2013.Roemer W.H., van Wijnen J.H. Differences among Black Smoke, PM10, and PM1.0 Levels at Urban Measurement Sites. Environ. Health Perspect. 2001;109:151–154. doi: 10.1289/ehp.01109151.10.1289/ehp.01109151PMC124063511266325Bailey D.L.R., Clayton P. The measurement of suspended particle and total carbon concentrations in the atmosphere using standard smoke shade methods. Atmos. Environ. 1982;16:2683–2690. doi: 10.1016/0004-6981(82)90350-X.10.1016/0004-6981(82)90350-XHeal M.R., Quincey P. The relationship between black carbon concentration and black smoke: A more general approach. Atmos. Environ. 2012;54:538–544. doi: 10.1016/j.atmosenv.2012.02.067.10.1016/j.atmosenv.2012.02.067Edwards J.D., Ogren J.A., Weiss R.E., Charlson R.J. Particulate air pollutants—A comparison of British “smoke” with optical absorption coefficient and elemental carbon concentration. Atmos. Environ. 1983;17:2337–2341. doi: 10.1016/0004-6981(83)90233-0.10.1016/0004-6981(83)90233-0Watson J.G., Chow J.C., Chen L.-W.A. Summary of organic and elemental carbon/black carbon analysis methods and intercomparisons. Aerosol Air Qual. Res. 2005;5:65–102.Fuller K.A., Malm W.C., Kreidenweis S.M. Effects of mixing on extinction by carbonaceous particles. J. Geophys. Res. 1999;104:15941–15954. doi: 10.1029/1998JD100069.10.1029/1998JD100069Quincey P., Butterfield D., Green G., Fuller G.W. Black Smoke and Black Carbon: Further investigation of the relationship between these ambient air metrics. Atmos. Environ. 2011;45:3528–3534. doi: 10.1016/j.atmosenv.2011.04.009.10.1016/j.atmosenv.2011.04.009Butterfield D., Beccaceci S., Quincey P., Lilley A., Bradshaw C., Fuller G., Green D., Font A. 2013 Annual Report for the UK Black Carbon Network. Queen’s Printer and Controller of HMSO; London, UK: 2013. NPL Report AS 92.Janssen N.A.H., van Vliet P.H.N., Aarts F., Harssema H., Brunekreef B. Assessment of exposure to traffic-related air pollution of children attending schools near motorways. Atmos. Environ. 2001;35:3875–3884. doi: 10.1016/S1352-2310(01)00144-3.10.1016/S1352-2310(01)00144-3Cyrys J., Heinrich J., Hoek G., Meliefste K., Lewne M., Gehring U., Bellander T., Fischer P., van Vliet P., Brauer M., et al. Comparison between different traffic-related particle indicators: Elemental carbon (EC), PM2.5 mass, and absorbance. J. Expo. Sci. Environ. Epidemiol. 2003;13:134–143. doi: 10.1038/sj.jea.7500262.10.1038/sj.jea.750026212679793Roorda-Knape M.C., Janssen N.A.H., de Hartog J.J., van Vliet P.H.N., Harssema H., Brunekreef B. Air pollution from traffic in city districts near major motorways. Atmos. Environ. 1998;32:1921–1930. doi: 10.1016/S1352-2310(97)00496-2.10.1016/S1352-2310(97)00496-2Adams H.S., Nieuwenhuijsen M.J., Colvile R.N., Older M.J., Kendall M. Assessment of road users’ elemental carbon personal exposure levels, London, UK. Atmos. Environ. 2002;36:5335–5342. doi: 10.1016/S1352-2310(02)00637-4.10.1016/S1352-2310(02)00637-4Kinney P.L., Aggarwal M., Northridge M.E., Janssen N.A., Shepard P. Airborne concentrations of PM2.5 and diesel exhaust particles on Harlem sidewalks: A community-based pilot study. Environ. Health Perspect. 2000;108:213–218. doi: 10.1289/ehp.00108213.10.1289/ehp.00108213PMC163797810706526Keuken M., Zandveld P., van den Elshout S., Janssen N.A.H., Hoek G. Air quality and health impact of PM10 and EC in the city of Rotterdam, the Netherlands in 1985–2008. Atmos. Environ. 2011;45:5294–5301. doi: 10.1016/j.atmosenv.2011.06.058.10.1016/j.atmosenv.2011.06.058Putaud J.-P., Raes F., van Dingenen R., Brüggeman E., Facchini M.-C., Decesari S., Fuzzi S., Gehrig R., Hüglin C., Laj P., et al. A European aerosol phenomenology-2: Chemical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe. Atmos. Environ. 2004;38:2579–2595. doi: 10.1016/j.atmosenv.2004.01.041.10.1016/j.atmosenv.2004.01.041Reche C., Querol X., Alastuey A., Viana M., Pey J., Moreno T., Rodríguez S., González Y., Fernández-Camacho R., de la Rosa J., et al. New considerations for PM, Black Carbon and particle number concentration for air quality monitoring across different European cities. Atmos. Chem. Phys. 2011;11:6207–6227. doi: 10.5194/acp-11-6207-2011.10.5194/acp-11-6207-2011Eeftens M., Tsai M.-Y., Ampe C., Anwander B., Beelen R., Bellander T., Cesaroni G., Cirach M., Cyrys J., de Hoogh K., et al. Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2—Results of the ESCAPE project. Atmos. Environ. 2012;62:303–317. doi: 10.1016/j.atmosenv.2012.08.038.10.1016/j.atmosenv.2012.08.038Jedynska A., Hoek G., Eeftens M., Cyrys J., Keuken M., Ampe C., Beelen R., Cesaroni G., Forastiere F., Cirach M., et al. Spatial variations of PAH, hopanes/steranes and EC/OC concentrations within and between European study areas. Atmos. Environ. 2014;87:239–248. doi: 10.1016/j.atmosenv.2014.01.026.10.1016/j.atmosenv.2014.01.026Brunekreef B., Forsberg B. Epidemiological evidence of effects of coarse airborne particles on health. Eur. Respir. J. 2005;26:309–318. doi: 10.1183/09031936.05.00001805.10.1183/09031936.05.0000180516055881Ulrich A., Wichser A., Hess A., Heeb N., Emmenegger L., Czerwinski J., Kasper M., Mooney J., Mayer A. Particle and Metal Emissions of Diesel and Gasoline Engines—Are Particle Filters Appropriate Measures? Swiss Federal Laboratories for Material Testing and Research; Dübendorf, Switzerland: 2012.Grahame T.J. Does improved exposure information for PM2.5 constituents explain differing results among epidemiological studies? Inhal. Toxicol. 2009;21:381–393. doi: 10.1080/08958370802380495.10.1080/0895837080238049519234961Schwartz J., Litonjua A., Suh H., Verrier M., Zanobetti A., Syring M., Nearing B., Verrier R., Stone P., MacCallum G., et al. Traffic related pollution and heart rate variability in a panel of elderly subjects. Thorax. 2005;60:455–461. doi: 10.1136/thx.2004.024836.10.1136/thx.2004.024836PMC174741915923244Chang E.T., Adami H.-O., Bailey W.H., Boffetta P., Krieger R.I., Moolgavkar S.H., Mandel J.S. Validity of geographically modeled environmental exposure estimates. Crit. Rev. Toxicol. 2014;44:450–466. doi: 10.3109/10408444.2014.902029.10.3109/10408444.2014.90202924766059Krecl P., Targino A.C., Johansson C. Spatiotemporal distribution of light-absorbing carbon and its relationship to other atmospheric pollutants in Stockholm. Atmos. Chem. Phys. 2011;11:11553–11567. doi: 10.5194/acp-11-11553-2011.10.5194/acp-11-11553-2011Schleicher N., Norra S., Fricker M., Kaminski U., Chen Y., Chai F., Wang S., Yu Y., Cen K. Spatio-temporal variations of black carbon concentrations in the Megacity Beijing. Environ. Pollut. 2013;182:392–401. doi: 10.1016/j.envpol.2013.07.042.10.1016/j.envpol.2013.07.04223978522Wang Y., Hopke P.K., Utell M.J. Urban-scale Spatial-temporal Variability of Black Carbon and Winter Residential Wood Combustion Particles. Aerosol Air Qual. Res. 2011;11:473–481. doi: 10.4209/aaqr.2011.01.0005.10.4209/aaqr.2011.01.0005
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1614-74992382016AprEnvironmental science and pollution research internationalEnviron Sci Pollut Res Int14th congress of combustion by-products and their health effects-origin, fate, and health effects of combustion-related air pollutants in the coming era of bio-based energy sources.814181598141-5910.1007/s11356-016-6308-yThe 14th International Congress on Combustion By-Products and Their Health Effects was held in Umeå, Sweden from June 14th to 17th, 2015. The Congress, mainly sponsored by the National Institute of Environmental Health Sciences Superfund Research Program and the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, focused on the "Origin, fate and health effects of combustion-related air pollutants in the coming era of bio-based energy sources". The international delegates included academic and government researchers, engineers, scientists, policymakers and representatives of industrial partners. The Congress provided a unique forum for the discussion of scientific advances in this research area since it addressed in combination the health-related issues and the environmental implications of combustion by-products. The scientific outcomes of the Congress included the consensus opinions that: (a) there is a correlation between human exposure to particulate matter and increased cardiac and respiratory morbidity and mortality; (b) because currently available data does not support the assessment of differences in health outcomes between biomass smoke and other particulates in outdoor air, the potential human health and environmental impacts of emerging air-pollution sources must be addressed. Assessment will require the development of new approaches to characterize combustion emissions through advanced sampling and analytical methods. The Congress also concluded the need for better and more sustainable e-waste management and improved policies, usage and disposal methods for materials containing flame retardants.WeidemannEvaE0000-0001-5415-9330Department of Chemistry, Umeå University, Umea, Sweden. eva.weidemann@umu.se.AnderssonPatrik LPLDepartment of Chemistry, Umeå University, Umea, Sweden.BidlemanTerryTDepartment of Chemistry, Umeå University, Umea, Sweden.BomanChristofferCThermochemical Energy Conversion Laboratory, Department of Applied Physics and Electronics, Umeå University, Umea, Sweden.CarlinDanielle JDJDepartment of Health and Human Services, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA.CollinaElenaEDepartment of Earth and Environmental Sciences, University of Milano-Bicocca, Milano, Italy.CormierStephania ASADepartment of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA.Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, USA.Gouveia-FigueiraSandra CSCDepartment of Chemistry, Umeå University, Umea, Sweden.GullettBrian KBKU.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, Research Triangle Park, NC, USA.JohanssonChristerCDepartment of Environmental Science and Analytical Chemistry, Stockholm University, Stockholm, Sweden.Environment and Health Administration, Stockholm, Sweden.LucasDonaldDLawrence Berkeley National Laboratory, University of California, Berkeley, CA, USA.LundinLisaLDepartment of Chemistry, Umeå University, Umea, Sweden.LundstedtStaffanSDepartment of Chemistry, Umeå University, Umea, Sweden.MarklundStellanSBio4Energy, Umeå University, Umea, Sweden.NordingMalin LMLDepartment of Chemistry, Umeå University, Umea, Sweden.OrtuñoNuriaNChemical Engineering Department, University of Alicante, Alicante, Spain.SallamAsmaa AAADepartment of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA.Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, USA.SchmidtFlorian MFMThermochemical Energy Conversion Laboratory, Department of Applied Physics and Electronics, Umeå University, Umea, Sweden.JanssonStinaSDepartment of Chemistry, Umeå University, Umea, Sweden.engP42 ES013648ESNIEHS NIH HHSUnited StatesR01 ES015050ESNIEHS NIH HHSUnited StatesCongress20160224
GermanyEnviron Sci Pollut Res Int94417690944-13440Air Pollutants0Particulate Matter0SmokeIMAir PollutantsanalysisBiomassEnergy-Generating ResourcesHealthHumansParticulate MatteranalysisSmokeSwedenCongress paperHuman healthParticlesPolychlorinated dibenzo-p-dioxinsPolychlorinated dibenzofuransProducts of incomplete combustionSoot
20151221201621520162256020162266020174760ppublish2690600610.1007/s11356-016-6308-y10.1007/s11356-016-6308-yAnal Chem. 2015 Jul 7;87(13):6493-926024433Chemosphere. 2014 Jan;94:42-724120013Environ Sci Technol. 2014 Apr 1;48(7):3995-400124617498Waste Manag Res. 2015 Jul;33(7):630-4326185164Chemosphere. 2011 Jan;82(1):72-721040943J Hazard Mater. 2013 Sep 15;260:819-2423856312Int J Toxicol. 2014 Jan-Feb;33(1):3-1324434722Waste Manag. 2014 Nov;34(11):2407-1325002370Environ Eng Sci. 2008 Oct;25(8):1107-111422476005Chemosphere. 2013 Apr;91(2):118-2323232045Sci Total Environ. 2014 Nov 15;499:27-3525173859Part Fibre Toxicol. 2014 Oct 30;11:5725358535Environ Sci Technol. 2013 Aug 6;47(15):8443-5223895511Chemosphere. 2016 Feb;145:193-926688256Ital J Pediatr. 2013 Jan 11;39(1):123311474
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1745-50651222016Women's health (London, England)Womens Health (Lond)The 2015 Pregnancy Summit, London, UK.167170167-7010.2217/whe.15.107Pregnancy Summit, Cineworld, The O2, London, UK, 29 September to 1 October 2015 The 2015 Pregnancy Summit was held over 3 days from 29 September to 1 October at Cineworld, The O2, London, UK. The event brings together a multidisciplinary faculty of international researchers and clinicians to discuss both scientific and clinical aspects of pregnancy-related issues in an informal setting. The goal of the meeting was to provide delegates with an update of recent advances in management of pregnancy-related conditions, to present research data and to discuss the current attitudes and practices in relevant topics. An extensive range of topics were discussed, from preeclampsia and treatment of hypertension, to the psychological impact of termination of pregnancy and feticide. This report will summarize a selection of the lectures presented.JohanssonCherynneCDepartment of Obstetrics & Gynaecology, Liverpool Hospital, Elizabeth & Campbell Streets, Liverpool, New South Wales 2170, Australia.engJournal Article20160222
United StatesWomens Health (Lond)1012712491745-5057IMCongresses as TopicFemaleHumansLondonPregnancyPregnancy Complicationsdiagnosistherapy
2016223602016224602016122860201731ppublish26900652PMC537505010.2217/whe.15.107Morrison-Beedy D, Jones SH, Xia Y, et al. Reducing sexual risk behavior in adolescent girls: results from a randomized controlled trial. J. Adolesc. Health 52, 314–321 (2013).PMC358000423299011Lowry P. Endokinin: the placental tachykinin behind morning sickness? The Endocrinologist 115, 26 (2015).Hansson SR, Gram M, Akerstrom B. Fetal haemoglobin in preeclampsia: a new causative factor, a tool for prediction/diagnosis and a potential target for therapy. Curr. Opin. Obstet. Gynecol. 25(6), 448–455 (2013).24185004Walter-Rosenlof L, Casslen V, Axelsson J, et al. A1M/α1-microglobulin protects from heme-induced placental and renal damage in a pregnant sheep model of preeclampsia. PLoS ONE 9(1), e86353 (2014).PMC390488224489717Sverisson K, Axelsson J, Rippe A, et al. Extracellular fetal haemoglobin induces increases in glomerular permeability: inhibition with α1-microglobulin and tempol. Am. J. Physiol. Renal Physiol. 306, F442–F448 (2014).24338823Naav A, Erlandsson L, Axelsson J, et al. A1M ameliorates preeclampsia-like symptoms in placenta and kidney induced by cell-free fetal hemoglobin in rabbit. PLoS ONE 10 (5), e0125499 (2015).PMC442545725955715Chaffin D. A decade of hemodynamic evaluation of Appalachian pregnancies. Presented at: The 2015 Pregnancy Summit London, UK, 29 September–1 October 2015.Siegler E. LLETZ treatment of CIN 2–3 in the first trimester of pregnancy – is it time to change the indications? Presented at: The 2015 Pregnancy Summit. London, UK, 29 September–1 October 2015.Azarpazhooh A. Separating fact from fiction: interactions between oral health and adverse pregnancy outcome. Presented at: The 2015 Pregnancy Summit London, UK, 29 September–1 October 2015.Lafarge C, Mitchell K, Fox P. Women's coping with pregnancy termination for fetal abnormality: an interpretative phenomenological analysis of women's experiences. Qual. Health Res. 23(7), 924–936 (2013).23558712Leichtentritt RD. Silenced voices: Israeli mothers' experience of feticide. Soc. Sci. Med. 72(5), 747–754 (2011).21306809Leichtentritt RD, Mahat-Shamir M. Mothers' continuing bond with the baby: the case of feticide. Qual. Health Res. doi:10.1177/1049732315616626 (2015) (Epub ahead of print).26631684Andersson M, Raibhandari R, et al. Mother-to-child transmission of hepatitis B virus in sub-Saharan Africa: time to act. Lancet Global Health 3(7), e358–e359 (2015).26087980Life Science Events. www.lifescienceevents.com
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1860-71871152016Mar04ChemMedChemChemMedChemFragment Screening of Soluble Epoxide Hydrolase for Lead Generation-Structure-Based Hit Evaluation and Chemistry Exploration.497508497-50810.1002/cmdc.201500575Soluble epoxide hydrolase (sEH) is involved in the regulation of many biological processes by metabolizing the key bioactive lipid mediator, epoxyeicosatrienoic acids. For the development of sEH inhibitors with improved physicochemical properties, we performed both a fragment screening and a high-throughput screening aiming at an integrated hit evaluation and lead generation. Followed by a joint dose-response analysis to confirm the hits, the identified actives were then effectively triaged by a structure-based hit-classification approach to three prioritized series. Two distinct scaffolds were identified as tractable starting points for potential lead chemistry work. The oxoindoline series bind at the right-hand side of the active-site pocket with hydrogen bonds to the protein. The 2-phenylbenzimidazole-4-sulfonamide series bind at the central channel with significant induced fit, which has not been previously reported. On the basis of the encouraging initial results, we envision that a new lead series with improved properties could be generated if a vector is found that could merge the cyclohexyl functionality of the oxoindoline series with the trifluoromethyl moiety of the 2-phenylbenzimidazole-4-sulfonamide series.© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.XueYafengYDepartment Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden.OlssonThomasTDepartment Medicinal Chemistry, CVMD iMED, AstraZeneca R&D Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden.JohanssonCarina ACADepartment Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden.ÖsterLindaLDepartment Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden.BeiselHans-GeorgHGDepartment Medicinal Chemistry, CVMD iMED, AstraZeneca R&D Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden.RohmanMattiasMDepartment Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden.KarisDavidDDepartment Medicinal Chemistry, CVMD iMED, AstraZeneca R&D Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden.BäckströmStefanSDepartment Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden. yafeng.xue@astrazeneca.com.engJournal ArticleValidation Study20160204
GermanyChemMedChem1012590131860-7179EC 3.3.2.-Epoxide HydrolasesIMCatalytic DomainEpoxide Hydrolasesantagonists & inhibitorschemistrymetabolismHigh-Throughput Screening AssaysModels, MolecularMolecular StructureSolubilitydrug discoveryhigh-throughput screeninginhibitorsligand complex structuressoluble epoxide hydrolase
201512112016256020162560201611160ppublish2684523510.1002/cmdc.201500575
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1460-23503142016AprHuman reproduction (Oxford, England)Hum ReprodProteomic characterization of macro-, micro- and nano-extracellular vesicles derived from the same first trimester placenta: relevance for feto-maternal communication.687699687-9910.1093/humrep/dew004What proteins are carried by extracellular vesicles (EVs) released from normal first trimester placentae?One thousand five hundred and eighty-five, 1656 and 1476 proteins were characterized in macro-, micro- and nano-vesicles, respectively, from first trimester placentae, with all EV fractions being enriched for proteins involved in vesicle transport and inflammation.Placental EVs are being increasingly recognized as important mediators of both healthy and pathological pregnancies. However, current research has focused on detecting changes in specific proteins in particular fractions of vesicles during disease. This is the first study to investigate the full proteome of different-sized fractions of EVs from the same first trimester placenta and highlights the differences/similarities between the vesicle fractions.A well-established ex vivo placental explant culture model was used to generate macro-, micro- and nano-vesicles from 56 first trimester placentae. Vesicle fractions were collected by differential ultracentrifugation, quantified and characterized.Placental macro-, micro- and nano-vesicles were characterized by microscopy, dynamic light scattering and nanoparticle tracking analysis. The proteome of each EV fraction was interrogated using liquid chromatography-coupled tandem mass spectrometry. Results were validated by semi-quantitative western blotting.A total of 1585, 1656 and 1476 proteins were identified in macro-, micro- and nano-vesicles, respectively. One thousand one hundred and twenty-five proteins were shared between all three fractions while up to 223 proteins were unique to each fraction. Gene Ontology pathway analysis showed an enrichment of proteins involved in vesicle transport and inflammation in all three fractions of EVs. The expression levels of proteins involved in internalization of vesicles (annexin V, calreticulin, CD31, CD47), the complement pathway [C3, decay-accelerating factor (DAF), membrane cofactor protein (MCP), protectin] and minor histocompatibility antigens [ATP-dependent RNA helicase (DDX3), ribosomal protein S4 (RPS4)] were different between different-sized EVs.This study is largely hypothesis-generating in nature. It is important to validate these findings using EVs isolated from maternal plasma and the function of the different EV fractions would need further investigation.Our results support the concept that various EV factions can interact with different maternal cells and have unique effects to mediate feto-maternal communication during early pregnancy. This study also provides a list of candidate proteins, which may inform the identification of robust markers that can be used to isolate placental vesicles from the maternal blood in the future.M.T. is a recipient of the University of Auckland Health Research Doctoral Scholarship and the Freemasons Postgraduate Scholarship. This project was supported by a School of Medicine Performance-based research fund (PBRF) grant awarded to L.W.C. No authors have any conflicts of interest to disclose.© The Author 2016. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.TongMancyMDepartment of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, The University of Auckland, 85 Park Road, Auckland 1023, New Zealand mancy.tong@auckland.ac.nz.KleffmannTorstenTCentre for Protein Research, Department of Biochemistry, University of Otago, Dunedin 9016, New Zealand.PradhanShantanuSDepartment of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, The University of Auckland, 85 Park Road, Auckland 1023, New Zealand.JohanssonCaroline LCLDepartment of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, The University of Auckland, 85 Park Road, Auckland 1023, New Zealand Faculty of Medicine and Health Sciences, Linköping University, Linköping SE-581 83, Sweden.DeSousaJoanaJMaternal Fetal Medicine, Auckland City Hospital, Auckland 1023, New Zealand.StonePeter RPRDepartment of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, The University of Auckland, 85 Park Road, Auckland 1023, New Zealand Maternal Fetal Medicine, Auckland City Hospital, Auckland 1023, New Zealand.JamesJoanna LJLDepartment of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, The University of Auckland, 85 Park Road, Auckland 1023, New Zealand.ChenQiQDepartment of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, The University of Auckland, 85 Park Road, Auckland 1023, New Zealand.ChamleyLarry WLWDepartment of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, The University of Auckland, 85 Park Road, Auckland 1023, New Zealand.engComparative StudyJournal ArticleResearch Support, Non-U.S. Gov't20160201
EnglandHum Reprod87011990268-11610Pregnancy Proteins0ProteomeIMAbortion, LegalBlotting, WesternChromatography, High Pressure LiquidDynamic Light ScatteringExtracellular VesicleschemistryphysiologyultrastructureFemaleHumansMaternal-Fetal ExchangeMicroscopy, Electron, TransmissionNew ZealandParticle SizePlacentachemistryphysiologyultrastructurePregnancyPregnancy ProteinschemistryphysiologyPregnancy Trimester, FirstProteomechemistryphysiologyProteomicsmethodsSpectrometry, Mass, Electrospray IonizationTandem Mass SpectrometryTissue Culture Techniquesexosomeextracellular vesiclemicroparticlesyncytial knotssyncytial nuclear aggregatestrophoblast deportationtrophoblastic debris
20157320161220162460201624602016122860ppublish2683915110.1093/humrep/dew004dew004
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1651-20579662016Aug23Acta dermato-venereologicaActa Derm VenereolSerotonergic Markers in Atopic Dermatitis.732736732-610.2340/00015555-2354Stress and anxiety may worsen atopic dermatitis (AD) through the serotonin system. Serotonergic expression was measured in 28 patients with AD in relation to extent of the disease (SCORing of Atopic Dermatitis; SCORAD), pruritus intensity (visual analogue scale; VAS), anxiety traits (Swedish Universities Scales of Personality; SSP) and depression (Montgomery-Åsberg Depression Rating Scale-Self assessment; MADRS-S). Biopsies were taken from lesional and non-lesional AD skin, and investigated for expression of serotonin, its receptors 5-HT1A and 5-HT2, and serotonin transporter protein (SERT), using immunohistochemistry. 5-HT1AR-immunoreactivity (ir) was higher in lesional skin in apical epidermis and in mast cell-like cells in dermis, and 5-HT2AR-ir in apical epidermis and on blood vessels. In contrast, a basement membrane 5-HT2AR-ir signal was higher in non-lesional skin. The distribution of SERT-ir in the basal epidermal layer was higher in lesional skin. Positive and negative correlations were found between serotonergic markers and SCORAD, inflammation, pruritus intensity, anxiety traits, and depression score, indicating that serotonergic mechanisms are involved in AD.RasulAramADermatology and Venereology Unit, Department of Medicine, Solna, Karolinska University Hospital, Karolinska Institutet, SE-171 76 Stockholm, Sweden. aram.rasul@ki.se.El-NourHusameldinHLonne-RahmSol-BrittSBFranssonOscarOJohanssonCharlottaCJohanssonBjörnBZubeidiMarweMSeebergEmmaEDjurfeldtDiana RaduDRAzmitiaEfrain CECNordlindKlasKengJournal Article
SwedenActa Derm Venereol03703100001-55550Receptor, Serotonin, 5-HT2A0Serotonin Plasma Membrane Transport Proteins112692-38-3Receptor, Serotonin, 5-HT1A333DO1RDJYSerotoninIMAdultAnxietypsychologyBiopsyDepressionpsychologyDermatitis, AtopicimmunologyphysiopathologypsychologyFemaleHumansImmunohistochemistryMaleMiddle AgedPruritusimmunologyphysiopathologypsychologyReceptor, Serotonin, 5-HT1AmetabolismReceptor, Serotonin, 5-HT2AmetabolismSelf-AssessmentSerotoninmetabolismSerotonin Plasma Membrane Transport ProteinsmetabolismSeverity of Illness Index
2016236020162360201711460ppublish2683183310.2340/00015555-2354
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2041-172372016Jan18Nature communicationsNat CommunEvidence for an ice shelf covering the central Arctic Ocean during the penultimate glaciation.10365103651036510.1038/ncomms10365The hypothesis of a km-thick ice shelf covering the entire Arctic Ocean during peak glacial conditions was proposed nearly half a century ago. Floating ice shelves preserve few direct traces after their disappearance, making reconstructions difficult. Seafloor imprints of ice shelves should, however, exist where ice grounded along their flow paths. Here we present new evidence of ice-shelf groundings on bathymetric highs in the central Arctic Ocean, resurrecting the concept of an ice shelf extending over the entire central Arctic Ocean during at least one previous ice age. New and previously mapped glacial landforms together reveal flow of a spatially coherent, in some regions >1-km thick, central Arctic Ocean ice shelf dated to marine isotope stage 6 (∼ 140 ka). Bathymetric highs were likely critical in the ice-shelf development by acting as pinning points where stabilizing ice rises formed, thereby providing sufficient back stress to allow ice shelf thickening.JakobssonMartinMDepartment of Geological Sciences, Stockholm University, Stockholm 106 91, Sweden.Bolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.UNIS - The University Centre in Svalbard, Longyearbyen N-9171, Svalbard.NilssonJohanJBolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.Department of Meteorology, Stockholm University, Stockholm 106 91, Sweden.AndersonLeifLDepartment of Marine Sciences, University of Gothenburg, Gothenburg 405 30, Sweden.BackmanJanJDepartment of Geological Sciences, Stockholm University, Stockholm 106 91, Sweden.Bolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.BjörkGöranGDepartment of Marine Sciences, University of Gothenburg, Gothenburg 405 30, Sweden.CroninThomas MTMUS Geological Survey Reston, 12201 Sunrise Valley Drive, Reston, Virginia 20192, USA.KirchnerNinaNDepartment of Physical Geography, Stockholm University, Stockholm 106 91, Sweden.KoshurnikovAndreyANational Research Tomsk Polytechnic University, Tomsk 634050, Russia.Department of Geocryology, Moscow State University, Moscow 119991, Russia.MayerLarryLCenter for Coastal and Ocean Mapping, University of New Hampshire, 24 Colovos Road, Durham, New Hampshire 03824, USA.NoormetsRikoRUNIS - The University Centre in Svalbard, Longyearbyen N-9171, Svalbard.O'ReganMatthewMDepartment of Geological Sciences, Stockholm University, Stockholm 106 91, Sweden.Bolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.StranneChristianCDepartment of Geological Sciences, Stockholm University, Stockholm 106 91, Sweden.Bolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.Center for Coastal and Ocean Mapping, University of New Hampshire, 24 Colovos Road, Durham, New Hampshire 03824, USA.AnanievRomanRNational Research Tomsk Polytechnic University, Tomsk 634050, Russia.Department of Geocryology, Moscow State University, Moscow 119991, Russia.Barrientos MachoNataliaNDepartment of Geological Sciences, Stockholm University, Stockholm 106 91, Sweden.Bolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.CherniykhDenisDNational Research Tomsk Polytechnic University, Tomsk 634050, Russia.Russian Academy of Sciences, Pacific Oceanological Institute, 43 Baltiiskaya Street, Vladivostok 690041, Russia.CoxallHelenHDepartment of Geological Sciences, Stockholm University, Stockholm 106 91, Sweden.Bolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.ErikssonBjörnBDepartment of Geological Sciences, Stockholm University, Stockholm 106 91, Sweden.Bolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.FlodénTomTDepartment of Geological Sciences, Stockholm University, Stockholm 106 91, Sweden.GemeryLauraLUS Geological Survey Reston, 12201 Sunrise Valley Drive, Reston, Virginia 20192, USA.GustafssonÖrjanÖBolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.Department of Environmental Science and Analytical Chemistry, Stockholm University, Stockholm 106 91, Sweden.JerramKevinKCenter for Coastal and Ocean Mapping, University of New Hampshire, 24 Colovos Road, Durham, New Hampshire 03824, USA.JohanssonCarinaCDepartment of Geological Sciences, Stockholm University, Stockholm 106 91, Sweden.Bolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.KhortovAlexeyANational Research Tomsk Polytechnic University, Tomsk 634050, Russia.MohammadRezwanRDepartment of Geological Sciences, Stockholm University, Stockholm 106 91, Sweden.Bolin Centre for Climate Research, Stockholm University, Stockholm 106 91, Sweden.SemiletovIgorINational Research Tomsk Polytechnic University, Tomsk 634050, Russia.Russian Academy of Sciences, Pacific Oceanological Institute, 43 Baltiiskaya Street, Vladivostok 690041, Russia.engJournal ArticleResearch Support, Non-U.S. Gov't20160118
EnglandNat Commun1015285552041-1723Nature. 2016 Feb 11;530(7589):163-4. doi: 10.1038/nature1687826840488
201561020151242016119602016119602016119612016118epublish26778247PMC473563810.1038/ncomms10365ncomms10365Thomson W. Polar ice-caps and their influence on changing sea levels. Trans. Geol. Soc. Glasgow 8, 322–340 (1888).Donn W. L. & Ewing M. A theory of Ice Ages III. Science 152, 1706–1712 (1966).17757794Mercer J. H. A former ice sheet in the Arctic Ocean? Palaeogeogr. Palaeoclimatol. Palaeoecol. 8, 19–27 (1970).Broecker W. S. Floating glacial ice caps in Arctic Ocean. Science 188, 1116–1118 (1975).17798435Hughes T. J., Denton G. H. & Grosswald M. G. Was there a late-Würm Arctic ice sheet? Nature 266, 596–602 (1977).Schytt V., Hoppe G., Blake W. Jr & Grosswald M. G. The extent of the Würm glaciation in the European Arctic. Meddelanden från Naturgeografiska Institutionen vid Stockholms universitet 79, 207–216 (1966).Grosswald M. G. Late Weichselian ice sheets of northern Eurasia. Quat. Res. 13, 1–32 (1980).Denton G. H. & Hughes T. J. in The Last Great Ice Sheets eds Denton G. H., Hughes T. J. Ch. 8, 437–467 Wiley Interscience (1981).Mix A. in The Geology of North America Vol. K-3, eds Ruddiman W. F., Wright H. E. Jr The Geological Society of America (1987).Williams D. F., Moore W. S. & Fillon R. H. Role of glacial Artic Ocean ice sheets in Pleistocene oxygen isotope and sea level records. Earth Planet. Sci. Lett. 56, 157–166 (1981).Johnson R. G. & Andrews J. T. Rapid ice-sheet growth and initiation of the last glaciation. Quat. Res. 12, 119–134 (1979).Johnson R. G. & Andrews J. T. Glacial terminations in the oxygen isotope record of deep sea cores: hypothesis of massive Antarctic ice-shelf destruction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 53, 107–138 (1986).Vogt P. R., Crane K. & Sundvor E. Deep Pleistocene iceberg plowmarks on the Yermak Plateau: sidescan and 3.5 kHz evidence for thick calving ice fronts and a possible marine ice sheet in the Arctic Ocean. Geology 22, 403–406 (1994).Polyak L., Edwards M. H., Coakley B. J. & Jakobsson M. Ice shelves in the Pleistocene Arctic Ocean inferred from glaciogenic deep-sea bedforms. Nature 410, 453–459 (2001).11260709Jakobsson M. First high-resolution chirp sonar profiles from the central Arctic Ocean reveal erosion of Lomonsov Ridge sediments. Mar. Geol. 158, 111–123 (1999).Jakobsson M. et al.. An Arctic Ocean ice shelf during MIS 6 constrained by new geophysical and geological data. Quat. Sci. Rev. 29, 3505–3517 (2010).Jokat W. The sedimentary structure of the Lomonosov Ridge between 88° N and 80° N. Geophys. J. Int. 163, 698–726 (2005).Kristoffersen Y. et al.. Seabed erosion on the Lomonosov Ridge, central Arctic Ocean: a tale of deep draft icebergs in the Eurasia Basin and the influence of Atlantic water inflow on iceberg motion? Paleoceanography 19, PA3006 (2004).Clark C. D. Mega-scale glacial lineations and cross-cutting ice-flow landforms. Earth Surf. Processes Landforms 18, 1–29 (1993).Jokat W., Weigelt E., Kristoffersen Y., Rasmussen T. & Schöne T. New insigths into the evolution of the Lomonsov Ridge and the Eurasaian Basin. Geophys. J. Int. 122, 378–392 (1995).Niessen F. et al.. Repeated Pleistocene glaciation of the East Siberian continental margin. Nat. Geosci. 6, 842–846 (2013).Dove D., Polyak L. & Coakley B. Widespread, multi-source glacial erosion on the Chukchi margin, Arctic Ocean. Quat. Sci. Rev. 92, 112–122 (2014).Jakobsson M. et al.. Arctic Ocean glacial history. Quat. Sci. Rev. 92, 40–67 (2014).Ottesen D. & Dowdeswell J. A. An inter-ice-stream glaciated margin: Submarine landforms and a geomorphic model based on marine-geophysical data from Svalbard. Geol. Soc. Am. Bull. 121, 1647–1665 (2009).Hanslik D. et al.. Quaternary Arctic Ocean sea ice variations and radiocarbon reservoir age corrections. Quat. Sci. Rev. 29, 3430–3441 (2010).Poirier R. K., Cronin T. M., Briggs W. M. Jr & Lockwood R. Central Arctic paleoceanography for the last 50 kyr based on ostracode faunal assemblages. Mar. Micropaleontol. 88–89, 65–76 (2012).O'Regan M. et al.. Constraints on the Pleistocene chronology of sediments from the Lomonosov Ridge. Paleoceanography 23, PA1S19 (2008).Jakobsson M. et al.. Manganese and color cycles in Arctic Ocean sediments constrain Pleistocene chronology. Geology 28, 23–26 (2000).Backman J., Fornaciari E. & Rio D. Biochronology and paleoceanography of late Pleistocene and Holocene calcareous nannofossils across the Arctic Basin. Mar. Micropaleontol. 72, 86–98 (2009).Matthiessen J., Knies J., Nowaczyk N. R. & Stein R. Late Quaternary dinoflagellate cyst stratigraphy at the Eurasian continental margin, Arctic Ocean: indications for Atlantic water inflow in the past 150,000 years. Global Planet. Change 31, 65–68 (2001).Polyak L., Curry W. B., Darby D. A., Bischof J. & Cronin T. M. Contrasting glacial/interglacial regimes in the western Arctic Ocean as exemplified by a sedimentary record from the Mendeleev Ridge. Palaeogeogr. Palaeoclimatol. Palaeoecol. 203, 73–93 (2004).Cronin T. M. et al.. Quaternary ostracode and foraminiferal biostratigraphy and paleoceanography in the western Arctic Ocean. Mar. Micropaleontol. 111, 118–133 (2014).Dowdeswell J. A. et al.. High-resolution geophysical observations from the Yermak Plateau and northern Svalbard margin: implications for ice-sheet grounding and deep-keeled icebergs. Quat. Sci. Rev. 29, 3518–3531 (2010).Arndt J. E., Niessen F., Jokat W. & Dorschel B. Deep water paleo-iceberg scouring on top of Hovgaard Ridge–Arctic Ocean. Geophys. Res. Lett. 41, 2014GL060267 (2014).Jakobsson M. et al.. Pleistocene stratigraphy and paleoenvironmental variation from Lomonosov Ridge sediments, central Arctic Ocean. Global Planet. Change 31, 1–22 (2001).Polyak L., Darby D., Bischof J. & Jakobsson M. Stratigraphic constraints on late Pleistocene glacial erosion and deglaciation of the Chukchi margin, Arctic Ocean. Quat. Res. 67, 234–245 (2007).Cronin T. M. et al.. Deep Arctic Ocean warming during the last glacial cycle. Nat. Geosci. 5, 631–634 (2012).Nøst O. A. et al.. Eddy overturning of the Antarctic slope front controls glacial melting in the Eastern Weddell Sea. J. Geophys. Res. C: Oceans 116, C11014 (2011).Kirchner N., Furrer R., Jakobsson M., Zwally H. J. & Robbins J. W. Statistical modeling of a former Arctic Ocean ice shelf complex using Antarctic analogies. J. Geophys. Res. Earth Surf. 118, 1–13 (2013).Depoorter M. A. et al.. Calving fluxes and basal melt rates of Antarctic ice shelves. Nature 502, 89–92 (2013).24037377Colleoni F., Krinner G., Jakobsson M., Peyaud V. & Ritz C. Influence of regional parameters on the surface mass balance of the Eurasian ice sheet during the peak Saalian (140 kya). Global Planet. Change 68, 132–148 (2009).Colleoni F., Werkele C. & Masina S. Long-Term Safety of a Planned Geological Repository for Spent Nucleaer Fuel in Forsmark - Estimate of Maximum Ice Sheet Thicknesses 1–96Swedish Nuclear Fuel and Waste Management Co (2014).Pierrehumbert R. T., Abbot D. S., Voigt A. & Koll D. Climate of the Neoproterozoic. Ann. Rev. Earth Planet. Sci. 39, 417–460 (2011).Siddall M. et al.. Sea-level fluctuations during the last glacial cycle. Nature 423, 853–858 (2003).12815427Noerdlinger K. W. & Brower K. R. The melting of floating ice raises the ocean level. Geophys. J. Int. 170, 145–150 (2007).Shackleton N. J. & Opdyke N. D. Oxygen isotope and Palaeomagnetic stratigraphy of equatorial pacific core V28-238: oxygen isotope temperatures and ice volumes on a 105 year and 106 year scale. Quat. Res. 3, 39–55 (1973).Colleoni F. On the Late Saalian Glaciation (160 - 140 ka) – A Climate Modeling Study PhD thesis Stockholm University/Université Joseph Fourier (2009).Gill A. E. The hydraulics of rotating-channel flow. J. Fluid Mech. 80, 641–671 (1977).Svendsen J. I. et al.. Late quaternary ice sheet history of northern Eurasia. Quat. Sci. Rev. 23, 1229–1271 (2004).Dyke A. S. et al.. The Laurentide and Innuitian ice sheets during the last glacial maximum. Quat. Sci. Rev. 21, 9–31 (2002).Engels J. L., Edwards M. H., Polyak L. & Johnson P. D. Seafloor evidence for ice shelf flow across the Alaska–Beaufort margin of the Arctic Ocean. Earth Surf. Processes Landforms 32, 1–17 (2008).Jakobsson M. et al.. The International Bathymetric Chart of the Arctic Ocean (IBCAO) Version 3.0. Geophys. Res. Lett. 39, L12609 (2012).
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1365-24862272016JulGlobal change biologyGlob Chang BiolEchinometra sea urchins acclimatized to elevated pCO2 at volcanic vents outperform those under present-day pCO2 conditions.245124612451-6110.1111/gcb.13223Rising atmospheric CO2 concentrations will significantly reduce ocean pH during the 21st century (ocean acidification, OA). This may hamper calcification in marine organisms such as corals and echinoderms, as shown in many laboratory-based experiments. Sea urchins are considered highly vulnerable to OA. We studied an Echinometra species on natural volcanic CO2 vents in Papua New Guinea, where they are CO2 -acclimatized and also subjected to secondary ecological changes from elevated CO2 . Near the vent site, the urchins experienced large daily variations in pH (>1 unit) and pCO2 (>2000 ppm) and average pH values (pHT 7.73) much below those expected under the most pessimistic future emission scenarios. Growth was measured over a 17-month period using tetracycline tagging of the calcareous feeding lanterns. Average-sized urchins grew more than twice as fast at the vent compared with those at an adjacent control site and assumed larger sizes at the vent compared to the control site and two other sites at another reef near-by. A small reduction in gonad weight was detected at the vents, but no differences in mortality, respiration, or degree of test calcification were detected between urchins from vent and control populations. Thus, urchins did not only persist but actually 'thrived' under extreme CO2 conditions. We suggest an ecological basis for this response: Increased algal productivity under increased pCO2 provided more food at the vent, resulting in higher growth rates. The wider implication of our observation is that laboratory studies on non-acclimatized specimens, which typically do not consider ecological changes, can lead to erroneous conclusions on responses to global change.© 2016 John Wiley & Sons Ltd.UthickeSvenSAustralian Institute of Marine Science, PMB No 3, Townsville, Qld, 4810, Australia.EbertThomasTDepartment of Zoology, Oregon State University, Corvallis, OR, 97324, USA.LiddyMichelleMDepartment of Marine Science, University of Otago, 9016, Dunedin, New Zealand.JohanssonCharlotteCAustralian Institute of Marine Science, PMB No 3, Townsville, Qld, 4810, Australia.FabriciusKatharina EKEAustralian Institute of Marine Science, PMB No 3, Townsville, Qld, 4810, Australia.LamareMilesMDepartment of Marine Science, University of Otago, 9016, Dunedin, New Zealand.engGENBANKKT198748KT198751Journal Article20160502
EnglandGlob Chang Biol98887461354-1013142M471B3JCarbon DioxideIMAcclimatizationAnimalsCarbon DioxidechemistryPapua New GuineaSea UrchinsphysiologySeawaterchemistryOcean acidificationcalcifying invertebratescarbon dioxide ventsindirect effects
20158112015121620161420161156020161156020178360ppublish2676261310.1111/gcb.13223
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1520-48045942016Feb25Journal of medicinal chemistryJ Med Chem8-Substituted Pyrido[3,4-d]pyrimidin-4(3H)-one Derivatives As Potent, Cell Permeable, KDM4 (JMJD2) and KDM5 (JARID1) Histone Lysine Demethylase Inhibitors.138814091388-40910.1021/acs.jmedchem.5b01635We report the discovery of N-substituted 4-(pyridin-2-yl)thiazole-2-amine derivatives and their subsequent optimization, guided by structure-based design, to give 8-(1H-pyrazol-3-yl)pyrido[3,4-d]pyrimidin-4(3H)-ones, a series of potent JmjC histone N-methyl lysine demethylase (KDM) inhibitors which bind to Fe(II) in the active site. Substitution from C4 of the pyrazole moiety allows access to the histone peptide substrate binding site; incorporation of a conformationally constrained 4-phenylpiperidine linker gives derivatives such as 54j and 54k which demonstrate equipotent activity versus the KDM4 (JMJD2) and KDM5 (JARID1) subfamily demethylases, selectivity over representative exemplars of the KDM2, KDM3, and KDM6 subfamilies, cellular permeability in the Caco-2 assay, and, for 54k, inhibition of H3K9Me3 and H3K4Me3 demethylation in a cell-based assay.BavetsiasVassiliosVCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.LaniganRachel MRMCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.RudaGian FilippoGFStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Target Discovery Institute (TDI), Nuffield Department of Medicine, University of Oxford , NDMRB Roosevelt Drive, Oxford OX3 7FZ, U.K.AtrashButrusBCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.McLaughlinMark GMGCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.TumberAnthonyAStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Target Discovery Institute (TDI), Nuffield Department of Medicine, University of Oxford , NDMRB Roosevelt Drive, Oxford OX3 7FZ, U.K.MokN YiNYCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.Le BihanYann-VaïYVCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.DempsterSallySCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.BoxallKatherine JKJCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.JeganathanFionaFCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.HatchStephanie BSBStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Target Discovery Institute (TDI), Nuffield Department of Medicine, University of Oxford , NDMRB Roosevelt Drive, Oxford OX3 7FZ, U.K.SavitskyPavelPStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.VelupillaiSrikannathasanSStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.KrojerTobiasTStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.EnglandKatherine SKSStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Target Discovery Institute (TDI), Nuffield Department of Medicine, University of Oxford , NDMRB Roosevelt Drive, Oxford OX3 7FZ, U.K.SejbergJimmyJCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.ThaiChingCCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.DonovanAdamACancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.PalAkosACancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.ScozzafavaGiuseppeGStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Target Discovery Institute (TDI), Nuffield Department of Medicine, University of Oxford , NDMRB Roosevelt Drive, Oxford OX3 7FZ, U.K.BennettJames MJMStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Target Discovery Institute (TDI), Nuffield Department of Medicine, University of Oxford , NDMRB Roosevelt Drive, Oxford OX3 7FZ, U.K.KawamuraAkaneAChemistry Research Laboratory, University of Oxford , Mansfield Road, Oxford OX1 3TA, U.K.JohanssonCatrineCStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Botnar Research Centre, NIHR Oxford Biomedical Research Unit , Oxford OX3 7LD, U.K.SzykowskaAleksandraAStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.GileadiCarinaCStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Burgess-BrownNicola ANAStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.von DelftFrankFStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Diamond Light Source (DLS), Harwell Science and Innovation Campus , Didcot OX11 0DE, U.K.Department of Biochemistry, University of Johannesburg , Auckland Park 2006, South Africa.OppermannUdoUStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Botnar Research Centre, NIHR Oxford Biomedical Research Unit , Oxford OX3 7LD, U.K.WaltersZoeZDivisions of Molecular Pathology and Cancer Therapeutics, The Institute of Cancer Research , London SM2 5NG, U.K.ShipleyJanetJDivisions of Molecular Pathology and Cancer Therapeutics, The Institute of Cancer Research , London SM2 5NG, U.K.RaynaudFlorence IFICancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.WestawaySusan MSMEpinova Discovery Performance Unit, Medicines Research Centre, GlaxoSmithKline R&D , Stevenage SG1 2NY, U.K.PrinjhaRab KRKEpinova Discovery Performance Unit, Medicines Research Centre, GlaxoSmithKline R&D , Stevenage SG1 2NY, U.K.FedorovOlegOStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Target Discovery Institute (TDI), Nuffield Department of Medicine, University of Oxford , NDMRB Roosevelt Drive, Oxford OX3 7FZ, U.K.BurkeRosemaryRCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.SchofieldChristopher JCJChemistry Research Laboratory, University of Oxford , Mansfield Road, Oxford OX1 3TA, U.K.WestwoodIsaac MIMCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.BountraChasCStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.MüllerSusanneSStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Target Discovery Institute (TDI), Nuffield Department of Medicine, University of Oxford , NDMRB Roosevelt Drive, Oxford OX3 7FZ, U.K.van MontfortRob L MRLCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.BrennanPaul EPEStructural Genomics Consortium (SGC), University of Oxford , ORCRB Roosevelt Drive, Oxford OX3 7DQ, U.K.Target Discovery Institute (TDI), Nuffield Department of Medicine, University of Oxford , NDMRB Roosevelt Drive, Oxford OX3 7FZ, U.K.BlaggJulianJCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research , 15 Cotswold Road, London SM2 5NG, U.K.engC309/A11566CRUK_Cancer Research UKUnited Kingdom092809/Z/10/ZWT_Wellcome TrustUnited Kingdom106169WT_Wellcome TrustUnited Kingdom11566CRUK_Cancer Research UKUnited Kingdom18245CRUK_Cancer Research UKUnited KingdomWT_Wellcome TrustUnited KingdomJournal ArticleResearch Support, Non-U.S. Gov't20160107
United StatesJ Med Chem97165310022-26230Enzyme Inhibitors0Nuclear Proteins0Pyrimidinones0Repressor ProteinsEC 1.14.11.-Jumonji Domain-Containing Histone DemethylasesEC 1.14.11.-KDM4D protein, humanEC 1.14.11.-KDM5B protein, humanEC 1.5.-KDM4A protein, humanIMCaco-2 CellsCell Membrane PermeabilityEnzyme InhibitorschemistrypharmacokineticspharmacologyHumansJumonji Domain-Containing Histone Demethylasesantagonists & inhibitorschemistrymetabolismNuclear Proteinsantagonists & inhibitorschemistrymetabolismPyrimidinoneschemistrypharmacokineticspharmacologyRepressor Proteinsantagonists & inhibitorschemistrymetabolismThe authors declare no competing financial interest.
20161860201618602016728602016229ppublish26741168PMC477032410.1021/acs.jmedchem.5b01635Pedersen M. T.; Helin K. Histone Demethylases in Development and Disease. Trends Cell Biol. 2010, 20, 662–671. 10.1016/j.tcb.2010.08.011.10.1016/j.tcb.2010.08.01120863703Shi Y.; Whetstine J. R. Dynamic Regulation of Histone Lysine Methylation by Demethylases. Mol. Cell 2007, 25, 1–14. 10.1016/j.molcel.2006.12.010.10.1016/j.molcel.2006.12.01017218267Shiau C.; Trnka M. J.; Bozicevic A.; Torres I. O.; Al-Sady B.; Burlingame A. L.; Narlikar G. J.; Fujimori D. G. Reconstitution of Nucleosome Demethylation and Catalytic Proporties of a Jumonji Histone Demethylase. Chem. Biol. 2013, 20, 494–499. 10.1016/j.chembiol.2013.03.008.10.1016/j.chembiol.2013.03.008PMC370422923601638Kooistra S. M.; Helin K. Molecular Mechanisms and Potential Functions of Histone Demethylases. Nat. Rev. Mol. Cell Biol. 2012, 13, 297–311. 10.1038/nrm3327.10.1038/nrm332722473470Young L. C.; Hendzel M. J. The Oncogenic Potential of Jumonji D2 (JMJD2/KDM4) Histone Demethylase Overexpression. Biochem. Cell Biol. 2013, 91, 369–377. 10.1139/bcb-2012-0054.10.1139/bcb-2012-005424219278Hillringhaus L.; Yue W. W.; Rose N. R.; Ng S. S.; Gileadi C.; Loenarz C.; Bello S. H.; Bray J. E.; Schofield C. J.; Oppermann U. Structural and Evolutionary Basis for the Dual Substrate Selectivity of Human KDM4 Histone Demethylase Family. J. Biol. Chem. 2011, 286, 41616–41625. 10.1074/jbc.M111.283689.10.1074/jbc.M111.283689PMC330887121914792Berry W. L.; Janknecht R. KDM4/JMJD2 Histone Demethylases: Epigenetic Regulators in Cancer Cells. Cancer Res. 2013, 73, 2936–2942. 10.1158/0008-5472.CAN-12-4300.10.1158/0008-5472.CAN-12-4300PMC365515423644528Williams S. T.; Walport L. J.; Hopkinson R. J.; Madden S. K.; Chowdhury R.; Schofield C. J.; Kawamura A. Studies on the Catalytic Domains of Multiple JmjC Oxygenases Using Peptide Substrates. Epigenetics 2014, 9, 1596–1603. 10.4161/15592294.2014.983381.10.4161/15592294.2014.983381PMC462301825625844Trojer P.; Zhang J.; Yonezawa M.; Schmidt A.; Zheng H.; Jenuwein T.; Reinberg D. Dynamic Histone H1 Isotype 4 Methylation and Demethylation by Histone Lysine Methyltransferase G9a/KMT1C and the Jumonji Domain-containing JMJ/KDM4 Proteins. J. Biol. Chem. 2009, 284, 8395–8405. 10.1074/jbc.M807818200.10.1074/jbc.M807818200PMC265919719144645Liu G.; Bollig-Fischer A.; Kreike B.; van de Vijver M. J.; Abrams J.; Ethier S. P.; Yang Z.-Q. Genomic Amplification and Oncogenic Properties of the GASC1 Histone Demethylases Gene in Breast Cancer. Oncogene 2009, 28, 4491–4500. 10.1038/onc.2009.297.10.1038/onc.2009.297PMC279579819784073Kawazu M.; Saso K.; Tong K. I.; McQuire T.; Goto K.; Son D.-O.; Wakeham A.; Miyagishi M.; Mak T. W.; Okada H. Histone Demethylase JMJD2B Functions as a Co-Factor of Estrogen Receptor in Breast Cancer Proliferation and Mammary Gland Development. PLoS One 2011, 6, e17830.10.1371/journal.pone.0017830.10.1371/journal.pone.0017830PMC306087421445275Li W.; Zhao L.; Zang W.; Liu Z.; Chen L.; Liu T.; Xu D.; Jia J. Histone Demethylase JMJD2B is Required for Tumour Cell Proliferation and Survival and is Overexpressed in Gastric Cancer. Biochem. Biophys. Res. Commun. 2011, 416, 372–378. 10.1016/j.bbrc.2011.11.045.10.1016/j.bbrc.2011.11.04522133676Walters Z. S.; Villarejo-Balcells B.; Olmos D.; Buist T. W. S.; Missiaglia E.; Allen R.; Al-Lazikani B.; Garrett M. D.; Blagg J.; Shipley J. JARID2 is a Direct Target of the PAX3-FOXO1 Fusion Protein and Inhibits Myogenic Differentiation of Rhabdomyosarcoma Cells. Oncogene 2014, 33, 1148–1157. 10.1038/onc.2013.46.10.1038/onc.2013.46PMC398212423435416Yang J.; Al Tahan A. M.; Hu D.; Wang Y.; Cheng P.-H.; Morton C. L.; Qu C.; Nathwani A. C.; Shohet J. M.; Fotsis T.; Koster J.; Versteeg R.; Okada H.; Harris A. L.; Davidoff A. M. The Role of Histone Demethylase KDM4B in Myc Signaling in Neuroblastoma. J. Natl. Cancer Inst. 2015, 107, djv080.10.1093/jnci/djv080.10.1093/jnci/djv080PMC455563825925418Black J. C.; Manning A. L.; Van Rechem C.; Kim J.; Ladd B.; Cho J.; Pineda C. M.; Murphy N.; Daniels D. L.; Montagna C.; Lewis P. W.; Glass K.; Allis D. C.; Dyson N. J.; Getz G.; Whetstine J. R. KDM4A Lysine Demethylase Induces Site-Specific Copy Gain and Rereplication of Regions Amplified in Tumors. Cell 2013, 154, 541–555. 10.1016/j.cell.2013.06.051.10.1016/j.cell.2013.06.051PMC383205323871696Van Rechem C.; Black J. C.; Greninger P.; Zhao Y.; Donado C.; Burrowes P. D.; Ladd B.; Christiani D. C.; Benes C. H.; Whetstine J. R. A Coding Single-Nucleotide Polymorphism in Lysine Demethylase KDM4A Associates with Increased Sensitivity to mTOR Inhibitors. Cancer Discovery 2015, 5, 245–254. 10.1158/2159-8290.CD-14-1159.10.1158/2159-8290.CD-14-1159PMC435522625564517Van Rechem C.; Black J. C.; Boukhali M.; Aryee M. J.; Graslund S.; Haas W.; Benes C. H.; Whetstine J. R. Lysine Demethylase KDM4A Associates with Translation Machinery and Regulates Protein Synthesis. Cancer Discovery 2015, 5, 255–263. 10.1158/2159-8290.CD-14-1326.10.1158/2159-8290.CD-14-1326PMC435532825564516Blair L. P.; Cao J.; Zou M. R.; Sayegh J.; Yan Q. Epigenetic Regulation by Lysine Demethylase 5 (KDM5) Enzymes in Cancer. Cancers 2011, 3, 1383–1404. 10.3390/cancers3011383.10.3390/cancers3011383PMC308545621544224You J. S.; Jones P. A. Cancer Genetics and Epigenetics: Two Sides of the Same Coin?. Cancer Cell 2012, 22, 9–20. 10.1016/j.ccr.2012.06.008.10.1016/j.ccr.2012.06.008PMC339688122789535Klein B. J.; Piao L.; Xi Y.; Rincon-Arano H.; Rothbart S. B.; Peng D.; Wen H.; Larson C.; Zhang X.; Zheng X.; Cortazar M. A.; Pena P. V.; Mangan A.; Bentley D. L.; Strahl B. D.; Groudine M.; Li W.; Shi X.; Kutateladze T. G. The Histone-H3K4-specific Demethylase KDM5B Binds to Its Substrate and Product Through Distinct PHD Fingers. Cell Rep. 2014, 6, 325–335. 10.1016/j.celrep.2013.12.021.10.1016/j.celrep.2013.12.021PMC391844124412361Yamamoto S.; Wu Z.; Russnes H. G.; Takagi S.; Peluffo G.; Vaske C.; Zhao X.; Moen Vollan H. K.; Maruyama R.; Ekram M. B.; Sun H.; Kim J. H.; Carver K.; Zucca M.; Feng J.; Almendro V.; Bessarabova M.; Rueda O. M.; Nikolsky Y.; Caldas C.; Liu X. S.; Polyak K. JARID1B Is a Luminal Lineage-Driving Oncogene in Breast Cancer. Cancer Cell 2014, 25, 762–777. 10.1016/j.ccr.2014.04.024.10.1016/j.ccr.2014.04.024PMC407903924937458Stein J.; Majores M.; Rohde M.; Lim S.; Schneider S.; Krappe E.; Ellinger J.; Dietel M.; Stephan C.; Jung K.; Perner S.; Kristiansen G.; Kirfel J. KDM5C Is Overexpressed in Prostate Cancer and Is a Prognostic Marker for Prostate-Specific Antigen-Relapse Following Radical Prostatectomy. Am. J. Pathol. 2014, 184, 2430–2437. 10.1016/j.ajpath.2014.05.022.10.1016/j.ajpath.2014.05.02225016185Helin K.; Dhanak D. Chromatin Proteins and Modifications as Drug Targets. Nature 2013, 502, 480–488. 10.1038/nature12751.10.1038/nature1275124153301Kruidenier L.; Chung C.-W.; Cheng Z.; Liddle J.; Che K.; Joberty G.; Bantscheff M.; Bountra C.; Bridges A.; Diallo H.; Eberhard D.; Hutchinson S.; Jones E.; Katso R.; Leveridge M.; Mander P. K.; Mosley J.; Ramirez-Molina C.; Rowland P.; Schofield C. J.; Sheppard R. J.; Smith J. E.; Swales C.; Tanner R.; Thomas P.; Tumber A.; Drewes G.; Oppermann U.; Patel D. J.; Lee K.; Wilson D. M. A Selective Jumonji H3K27 Demethylase Inhibitor Modulates the Proinflammatory Macrophage Response. Nature 2012, 488, 404–408. 10.1038/nature11262.10.1038/nature11262PMC469184822842901Labbe R. M.; Holowatyj A.; Yang Z.-Q. Histone Lysine Demethylase (KDM) Subfamily 4: Structures, Functions and Therapeutic Potential. Am. J. Transl Res. 2014, 6, 1–15.PMC385342024349617Heightman T. D. Chemical Biology of Lysine Demethylases. Curr. Chem. Genomics 2011, 5, 62–71. 10.2174/1875397301005010062.10.2174/1875397301005010062PMC317887521966346Suzuki T.; Miyata N. Lysine Demethylases Inhibitors. J. Med. Chem. 2011, 54, 8236–8250. 10.1021/jm201048w.10.1021/jm201048w21955276Lohse B.; Kristensen J. L.; Kristensen L. H.; Agger K.; Helin K.; Gajhede M.; Clausen R. P. Inhibitors of Histone Demethylases. Bioorg. Med. Chem. 2011, 19, 3625–3636. 10.1016/j.bmc.2011.01.046.10.1016/j.bmc.2011.01.04621596573Hoffmann I.; Roatsch M.; Schmitt M. L.; Carlino L.; Pippel M.; Sippl W.; Jung M. The Role of Histone Demethylases in Cancer Therapy. Mol. Oncol. 2012, 6, 683–703. 10.1016/j.molonc.2012.07.004.10.1016/j.molonc.2012.07.004PMC552834822902149Zheng W.; Huang Y. The Chemistry and Biology of the α-Ketoglutarate-Dependent Histone Nε-Methyl-Lysine Demethylases. Med. MedChemComm 2014, 5, 297–313. 10.1039/c3md00325f.10.1039/c3md00325fChin Y.-W.; Han S.-Y. KDM4 Histone Demethylase Inhibitors for Anti-cancer Agents: A Patent Review. Expert Opin. Ther. Pat. 2015, 25, 135–144. 10.1517/13543776.2014.991310.10.1517/13543776.2014.99131025468267Rose N. R.; Ng S. S.; Mecinovic J.; Lienard B. M. R.; Bello S. H.; Sun Z.; McDonough M. A.; Oppermann U.; Schofield C. J. Inhibitor Scaffolds for 2-Oxoglutarate-Dependent Histone Lysine Demethylases. J. Med. Chem. 2008, 51, 7053–7056. 10.1021/jm800936s.10.1021/jm800936s18942826Chang K.-H.; King O. N. F.; Tumber A.; Woon E. C. Y.; Heightman T. D.; McDonough M. A.; Schofield C. J.; Rose N. R. Inhibition of Histone Demethylases by 4-Carboxy-2,2′ – Bipyridyl Compounds. ChemMedChem 2011, 6, 759–764. 10.1002/cmdc.201100026.10.1002/cmdc.201100026PMC469615921412984England K. S.; Tumber A.; Krojer T.; Scozzafava G.; Ng S. S.; Daniel M.; Szykowska A.; Che K.; von Delft F.; Burgess-Brown N. A.; Kawamura A.; Schofield C. J.; Brennan P. E. Optimisation of a Triazolopyridine Based Histone Demethylase Inhibitor Yields a Potent and Selective KDM2A (FBXL11) Inhibitor. MedChemComm 2014, 5, 1879–1886. 10.1039/C4MD00291A.10.1039/C4MD00291APMC467857626682034Mjambili F.; Njoroge M.; Naran K.; De Kock C.; Smith P. J.; Mizrahi V.; Warner D.; Chibale K. Synthesis and Biological Evaluation of 2-Aminothiazole Derivatives as Antimycobacterial and Antiplasmodial Agents. Bioorg. Med. Chem. Lett. 2014, 24, 560–564. 10.1016/j.bmcl.2013.12.022.10.1016/j.bmcl.2013.12.02224373723Labelle M.; Boesen T.; Mehrotra M.; Khan Q.; Ullah F.. Inhibitors of Histone Demethylases. WO2014/053491, 2014.Ng S. S.; Kavanagh K. L.; McDonough M. A.; Butler D.; Pilka E. S.; Lienard B. M. R.; Bray J. E.; Savitsky P.; Gileadi O.; von Delft F.; Rose N. R.; Offer J.; Scheinost J. C.; Borowski T.; Sundstrom M.; Schofield C. J.; Oppermann U. Crystal Structures of Histone Demethylase JMJD2A Reveal Basis for Substrate Specificity. Nature 2007, 448, 87–91. 10.1038/nature05971.10.1038/nature0597117589501Chu C.-H.; Wang L.-Y.; Hsu K.-S.; Chen C.-C.; Cheng H.-H.; Wang S.-M.; Wu C.-M.; Chen T.-J.; Li L.-T.; Liu R.; Hung C.-L.; Yang J.-M.; Kung H.-J.; Wang W.-C. KDM4B as a Target for Prostate Cancer: Structural Analysis and Selective Inhibition by a Novel Inhibitor. J. Med. Chem. 2014, 57, 5975–5985. 10.1021/jm500249n.10.1021/jm500249nPMC421621624971742Kanouni T.; Stafford J. A.; Veal J. M.; Wallace M. B.. Histone Demethylase Inhibitors. WO2014/151106, 2014.pKa Measurements were performed by Pharmorphix Solid State Services, Member of the Sigma-Aldrich Group, Cambridge, UK (Currently known as: Johnson Matthey Plc, Fine Chemicals Division (Pharmorphix), Cambridge, UK).Pryde D. C.; Dalvie D.; Hu Q.; Jones P.; Obach R. S.; Tran T.-D. Aldehyde Oxidase: An Enzyme of Emerging Importance in Drug Discovery. J. Med. Chem. 2010, 53, 8441–8460. 10.1021/jm100888d.10.1021/jm100888d20853847Hopkinson R. J.; Tumber A.; Yapp C.; Chowdhury R.; Aik W.; Che K. H.; Li X. S.; Kristensen J. B. L.; King O. N. F.; Chan M. C.; Yeoh K. H.; Choi H.; Walport L. J.; Thinnes C. C.; Bush J. T.; Lejeune C.; Rydzik A. M.; Rose N. R.; Bagg E. A.; McDonough M. A.; Krojer T. J.; Yue W. W.; Ng S. S.; Olsen L.; Brennan P. E.; Oppermann U.; Müller S.; Klose R. J.; Ratcliffe P. J.; Schofield C. J.; Kawamura A. 5-Carboxy-8-Hydroxyquinoline is a Broad Spectrum 2-Oxoglutarate Oxygenase Inhibitor Which Causes Iron Translocation. Chem. Sci. 2013, 4, 3110–3117. 10.1039/c3sc51122g.10.1039/c3sc51122gPMC467860026682036Kawamura A.; Tumber A.; Rose N. R.; King O. N. F.; Daniel M.; Oppermann U.; Heightman T. D.; Schofield C. Development of Homogeneous Luminesence Assays for Histone Demethylase Catalysis and Binding. Anal. Biochem. 2010, 404, 86–93. 10.1016/j.ab.2010.04.030.10.1016/j.ab.2010.04.030PMC467389920435012Maestro, version 9.3; Schrödinger, LLC: New York, 2012.Glide, version 5.8; Schrödinger, LLC: New York, 2012.LigPrep, version 2.5; Schrödinger, LLC: New York, 2011.Savitsky P.; Bray J.; Cooper C. D.; Marsden B. D.; Mahajan P.; Burgess-Brown N. A.; Gileadi O. High-throughput Production of Human Proteins for Crystallization: The SGC Experience. J. Struct. Biol. 2010, 172, 3–13. 10.1016/j.jsb.2010.06.008.10.1016/j.jsb.2010.06.008PMC293858620541610Kabsch W. XDS. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2010, 66, 125–132. 10.1107/S0907444909047337.10.1107/S0907444909047337PMC281566520124692Evans P. Scaling and Assessment of Data Quality. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2006, 62, 72–82. 10.1107/S0907444905036693.10.1107/S090744490503669316369096McCoy A. J.; Grosse-Kunstleve R. W.; Adams P. D.; Winn M. D.; Storoni L. C.; Read R. J. Phaser Crystallographic Software. J. Appl. Crystallogr. 2007, 40, 658–674. 10.1107/S0021889807021206.10.1107/S0021889807021206PMC248347219461840Winn M. D.; Ballard C. C.; Cowtan K. D.; Dodson E. J.; Emsley P.; Evans P. R.; Keegan R. M.; Krissinel E. B.; Leslie A. G.; McCoy A.; McNicholas S. J.; Murshudov G. N.; Pannu N. S.; Potterton E. A.; Powell H. R.; Read R. J.; Vagin A.; Wilson K. S. Overview of the CCP4 Suite and Current Developments. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2011, 67, 235–242. 10.1107/S0907444910045749.10.1107/S0907444910045749PMC306973821460441Emsley P.; Cowtan K. Coot: Model-building Tools for Molecular Graphics. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2004, 60, 2126–2132. 10.1107/S0907444904019158.10.1107/S090744490401915815572765Bricogne G.; Blanc E.; Brandl M.; Flensburg C.; Keller P.; Paciorek W.; Roversi P.; Sharff A.; Smart O. S.; Vonrhein C.; Womack T. O.. BUSTER, version 2.10.2; Global Phasing Ltd.: Cambridge, UK, 2015.Adams P. D.; Afonine P. V.; Bunkóczi G.; Chen V. B.; Davis I. W.; Echols N.; Headd J. J.; Hung L.-W.; Kapral G. J.; Grosse-Kunstleve R. W.; McCoy A. J.; Moriarty N. W.; Oeffner R.; Read R. J.; Richardson D. C.; Richardson J. S.; Terwilliger T. C.; Zwart P. H. PHENIX: a Comprehensive Python-based System for Macromolecular Structure Solution. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2010, 66, 213–221. 10.1107/S0907444909052925.10.1107/S0907444909052925PMC281567020124702Murshudov G. N.; Vagin A. A.; Dodson E. J. Refinement of Macromolecular Structures by the Maximum-Likelihood Method. Acta Crystallogr., Sect. D: Biol. Crystallogr. 1997, 53, 240–255. 10.1107/S0907444996012255.10.1107/S090744499601225515299926Smart O. S.; Womack T. O.; Sharff A.; Flensburg C.; Keller P.; Paciorek W.; Vonrhein C.; Bricogne G.. Grade, version 1.2.9; Global Phasing Ltd.: Cambridge, UK, 2014.Bruno I. J.; Cole J. C.; Lommerse R. S.; Rowland R.; Taylor R.; Verdonk M. L. Isostar: A Library of Information About Non-bonded Interactions. J. Comput.-Aided Mol. Des. 1997, 11, 525–537. 10.1023/A:1007934413448.10.1023/A:10079344134489491345Chen V. B.; Arendall W. B. III; Headd J. J.; Keedy D. A.; Immormino R. M.; Kapral G. J.; Murray L. W.; Richardson J. S.; Richardson D. C. MolProbity: All-atom Structure Validation for Macromolecular Crystallography. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2010, 66, 12–21. 10.1107/S0907444909042073.10.1107/S0907444909042073PMC280312620057044Davis I. W.; Leaver-Fay A.; Chen V. B.; Block J. N.; Kapral G. J.; Wang X.; Murray L. W.; Arendall W. B. 3rd; Snoeyink J.; Richardson J. S.; Richardson D. C. MolProbity: All-atom Contacts and Structure Validation for Proteins and Nucleic Acids. Nucleic Acids Res. 2007, 35, W375–W383. 10.1093/nar/gkm216.10.1093/nar/gkm216PMC193316217452350Obach R. S. Potent Inhibition of Human Liver Aldehyde Oxidase by Raloxifene. Drug Metab. Dispos. 2004, 32, 89–97. 10.1124/dmd.32.1.89.10.1124/dmd.32.1.8914709625
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2045-232252015Dec21Scientific reportsSci RepT cell responses are elicited against Respiratory Syncytial Virus in the absence of signalling through TLRs, RLRs and IL-1R/IL-18R.18533185331853310.1038/srep18533Pattern recognition receptors (PRRs) and cytokine receptors are key players in the initiation of immune responses to infection. PRRs detecting viral RNA, such as toll like receptor (TLR)-3, -7/8, and RIG-I like receptors (RLRs; RIG-I and MDA-5), as well as cytokine receptors such as interleukin 1 receptor (IL-1R), have been implicated in responses to RNA viruses that infect the airways. The latter includes respiratory syncytial virus (RSV), a human pathogen that can cause severe lower respiratory tract infections, especially in infants. To evaluate the collective contribution of PRRs and IL-1R signalling to RSV immunity, we generated Myd88/Trif/Mavs(-/-) mice that are deficient in signalling by all TLRs, RLRs and IL-1R, as well as other cytokine receptors such as IL-18 receptor. Early production of pro-inflammatory mediators and lung infiltration by immune cells were completely abrogated in infected Myd88/Trif/Mavs(-/-) mice. However, RSV-specific CD8(+) T cells were elicited and recruited into the lungs and airways. Consistent with these findings, Myd88/Trif/Mavs(-/-) mice survived RSV infection but displayed higher viral load and weight loss. These data highlight an unappreciated level of redundancy in pathways that couple innate virus sensing to adaptive immunity, providing the host with remarkable resilience to infection.GoritzkaMichelleMCentre for Respiratory Infections, Respiratory Infections Section, St Mary's campus, National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, W2 1PG, UK.PereiraCatherineCCentre for Respiratory Infections, Respiratory Infections Section, St Mary's campus, National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, W2 1PG, UK.MakrisSpyridonSCentre for Respiratory Infections, Respiratory Infections Section, St Mary's campus, National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, W2 1PG, UK.DurantLydia RLRCentre for Respiratory Infections, Respiratory Infections Section, St Mary's campus, National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, W2 1PG, UK.JohanssonCeciliaCCentre for Respiratory Infections, Respiratory Infections Section, St Mary's campus, National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, W2 1PG, UK.engG0800311Medical Research CouncilUnited KingdomJournal ArticleResearch Support, Non-U.S. Gov't20151221
EnglandSci Rep1015632882045-23220Adaptor Proteins, Signal Transducing0Adaptor Proteins, Vesicular Transport0IL1R2 protein, human0IPS-1 protein, mouse0Myd88 protein, mouse0Myeloid Differentiation Factor 880Receptors, Cytokine0Receptors, Interleukin-1 Type II0Receptors, Interleukin-180Receptors, Pattern Recognition0TICAM-1 protein, mouseIMAdaptor Proteins, Signal TransducinggeneticsAdaptor Proteins, Vesicular TransportgeneticsAnimalsHumansInfectionsgeneticsimmunologyvirologyMiceMice, TransgenicMyeloid Differentiation Factor 88geneticsReceptors, CytokinegeneticsimmunologyReceptors, Interleukin-1 Type IIgeneticsReceptors, Interleukin-18geneticsimmunologyReceptors, Pattern RecognitiongeneticsimmunologyRespiratory Syncytial Virus InfectionsgeneticsimmunologypathologyRespiratory Syncytial VirusesimmunologypathogenicityRespiratory Tract InfectionsgeneticsimmunologyvirologySignal TransductionT-LymphocytesimmunologypathologyViral Load
2015422015111820151222602015122260201612156020151221epublish26688048PMC468524610.1038/srep18533srep18533Nair H. et al. Global burden of acute lower respiratory infections due to respiratory syncytial virus in young children: a systematic review and meta-analysis. Lancet 375, 1545–1555 (2010).PMC286440420399493Chiu C. & Openshaw P. J. Antiviral B cell and T cell immunity in thelungs. Nat Immunol 16, 18–26 (2015).PMC709712825521681Borchers A. T., Chang C., Gershwin M. E. & Gershwin L. J. Respiratory Syncytial Virus–A Comprehensive Review. Clinic Rev Allerg Immunol, doi: 10.1007–s12016–013–8368–9 (2013).PMC709064323575961Graham B. S., Bunton L. A., Wright P. F. & Karzon D. T. Role of T lymphocyte subsets in the pathogenesis of primary infection and rechallenge with respiratory syncytial virus in mice. J Clin Invest 88, 1026–1033 (1991).PMC2955111909350Neyt K. & Lambrecht B. N. The role of lung dendritic cell subsets in immunity to respiratory viruses. Immunol Rev 255, 57–67 (2013).23947347Braciale T. J., Sun J. & Kim T. S. Regulating the adaptive immune response to respiratory virus infection. Nat Rev Immunol 12, 295–305 (2012).PMC336402522402670Durbin R. K., Kotenko S. V. & Durbin J. E. Interferon induction and function at the mucosal surface. Immunol Rev 255, 25–39 (2013).PMC597237023947345Yoo J.-K., Kim T. S., Hufford M. M. & Braciale T. J. Viral infection of the lung: Host response and sequelae. J Allergy Clin Immunol 132, 1263–1276 (2013).PMC384406223915713Goritzka M. et al. Alveolar macrophage-derived type I interferons orchestrate innate immunity to RSV through recruitment of antiviral monocytes. J. Exp. Med. 212, 699–714 (2015).PMC441933925897172Kumar H., Kawai T. & Akira S. Pathogen recognition by the innate immune system. Int. Rev. Immunol. 30, 16–34 (2011).21235323Sabbah A. et al. Activation of innate immune antiviral responses by Nod2. Nat Immunol 10, 1073–1080 (2009).PMC275234519701189Goubau D., Deddouche S. & Reis e Sousa C. Cytosolic Sensing of Viruses. Immunity 38, 855–869 (2013).PMC711111323706667Pang I. K., Ichinohe T. & Iwasaki A. IL-1R signaling in dendritic cells replaces pattern-recognition receptors in promoting CD8(+) T cell responses to influenza A virus. Nat Immunol 14, 246–253 (2013).PMC357794723314004Deguine J. & Barton G. M. MyD88: a central player in innate immune signaling. F1000Prime Rep 6, 97, doi: 10.12703/P6-97 (2014).10.12703/P6-97PMC422972625580251Marr N., Turvey S. E. & Grandvaux N. Pathogen recognition receptor crosstalk in respiratory syncytial virus sensing: a host and cell type perspective. Trends Microbiol. 21, 568–574 (2013).PMC484803224119913Murawski M. R. et al. Respiratory syncytial virus activates innate immunity through Toll-like receptor 2. J Virol 83, 1492–1500 (2009).PMC262089819019963Kurt-Jones E. A. et al. Pattern recognition receptors TLR4 and CD14 mediate response to respiratory syncytial virus. Nat Immunol 1, 398–401 (2000).11062499Haynes L. M. et al. Involvement of toll-like receptor 4 in innate immunity to respiratory syncytial virus. J Virol 75, 10730–10737 (2001).PMC11465411602714Rudd B. D. et al. Deletion of TLR3 alters the pulmonary immune environment and mucus production during respiratory syncytial virus infection. J Immunol 176, 1937–1942 (2006).16424225Lukacs N. W. et al. Respiratory virus-induced TLR7 activation controls IL-17-associated increased mucus via IL-23 regulation. J Immunol 185, 2231–2239 (2010).PMC300645420624950Demoor T. et al. IPS-1 Signaling Has a Nonredundant Role in Mediating Antiviral Responses and the Clearance of Respiratory Syncytial Virus. J Immunol 189, 5942–5953 (2012).PMC388896523136205Bhoj V. G. et al. MAVS and MyD88 are essential for innate immunity but not cytotoxic T lymphocyte response against respiratory syncytial virus. Proc Natl Acad Sci USA 105, 14046–14051 (2008).PMC253297418780793Goritzka M. et al. Interferon-α/β receptor signaling amplifies early pro-inflammatory cytokine production in the lung during Respiratory Syncytial Virus infection. J Virol 88, 6128–6136 (2014).PMC409389724648449Zaiss D. M. W., Gause W. C., Osborne L. C. & Artis D. Emerging Functions of Amphiregulin in Orchestrating Immunity, Inflammation, and Tissue Repair. Immunity 42, 216–226 (2015).PMC479203525692699Pribul P. K. et al. Alveolar macrophages are a major determinant of early responses to viral lung infection but do not influence subsequent disease development. J Virol 82, 4441–4448 (2008).PMC229304918287232Loebbermann J. et al. Regulatory T cells expressing granzyme B play a critical role in controlling lung inflammation during acute viral infection. Mucosal immunology 5, 161–172 (2012).PMC328243422236998Sokol C. L. & Luster A. D. The Chemokine System in Innate Immunity. Cold Spring Harb Perspect Biol, doi: 10.1101/cshperspect.a016303 (2015).10.1101/cshperspect.a016303PMC444861925635046Zelenay S. & Sousa C. R. E. Adaptive immunity after cell death. Trends Immunol 34, 329–335 (2013).23608152Sancho D. et al. Identification of a dendritic cell receptor that couples sensing of necrosis to immunity. Nature 458, 899–903 (2009).PMC267148919219027Zelenay S. et al. The dendritic cell receptor DNGR-1 controls endocytic handling of necrotic cell antigens to favor cross-priming of CTLs in virus-infected mice. J Clin Invest 122, 1615–1627 (2012).PMC333698422505458Iborra S. et al. The DC receptor DNGR-1 mediates cross-priming of CTLs during vaccinia virus infection in mice. J Clin Invest 122, 1628–1643 (2012).PMC333698522505455Durant L. R. et al. DNGR-1 is dispensable for CD8(+) T-cell priming during respiratory syncytial virus infection. Eur J Immunol 44, 2340–2348 (2014).24777856Marichal T. et al. DNA released from dying host cells mediates aluminum adjuvant activity. Nat Med 17, 996–1002 (2011).21765404Cai X., Chiu Y.-H. & Chen Z. J. The cGAS-cGAMP-STING pathway of cytosolic DNA sensing and signaling. Mol. Cell 54, 289–296 (2014).24766893Tal G. et al. Association between common Toll-like receptor 4 mutations and severe respiratory syncytial virus disease. J Infect Dis 189, 2057–2063 (2004).15143473Tulic M. K. et al. TLR4 polymorphisms mediate impaired responses to respiratory syncytial virus and lipopolysaccharide. J Immunol 179, 132–140 (2007).17579031Janssen R. et al. Genetic susceptibility to respiratory syncytial virus bronchiolitis is predominantly associated with innate immune genes. J Infect Dis 196, 826–834 (2007).17703412Awomoyi A. A. et al. Association of TLR4 polymorphisms with symptomatic respiratory syncytial virus infection in high-risk infants and young children. J Immunol 179, 3171–3177 (2007).17709532Siezen C. L. E. et al. Genetic susceptibility to respiratory syncytial virus bronchiolitis in preterm children is associated with airway remodeling genes and innate immune genes. Pediatr Infect Dis J 28, 333–335 (2009).19258923Durbin J. E. et al. The role of IFN in respiratory syncytial virus pathogenesis. J Immunol 168, 2944–2952 (2002).11884466Monticelli L. A. et al. Innate lymphoid cells promote lung-tissue homeostasis after infection with influenza virus. Nat Immunol 12, 1045–1054 (2011).PMC332004221946417Adachi O. et al. Targeted disruption of the MyD88 gene results in loss of IL-1- and IL-18-mediated function. Immunity 9, 143–150 (1998).9697844Yamamoto M. et al. Role of adaptor TRIF in the MyD88-independent toll-like receptor signaling pathway. Science (New York, NY) 301, 640–643 (2003).12855817Kumar H. et al. Essential role of IPS-1 in innate immune responses against RNA viruses. J. Exp. Med. 203, 1795–1803 (2006).PMC211835016785313Lee D. C. P. et al. CD25+ natural regulatory T cells are critical in limiting innate and adaptive immunity and resolving disease following respiratory syncytial virus infection. J Virol 84, 8790–8798 (2010).PMC291903020573822Asselin-Paturel C. et al. Mouse type I IFN-producing cells are immature APCs with plasmacytoid morphology. Nat Immunol 2, 1144–1150 (2001).11713464
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1095-86301652016Jan01Journal of environmental managementJ Environ ManageSimultaneous biosorption of selenium, arsenic and molybdenum with modified algal-based biochars.117123117-12310.1016/j.jenvman.2015.09.021S0301-4797(15)30273-5Ash disposal waters from coal-fired power stations present a challenging water treatment scenario as they contain high concentrations of the oxyanions Se, As and Mo which are difficult to remove through conventional techniques. In an innovative process, macroalgae can be treated with Fe and processed through slow pyrolysis into Fe-biochar which has a high affinity for oxyanions. However, the effect of production conditions on the efficacy of Fe-biochar is poorly understood. We produced Fe-biochar from two algal sources; "Gracilaria waste" (organic remnants after agar is extracted from cultivated Gracilaria) and the freshwater macroalgae Oedogonium. Pyrolysis experiments tested the effects of the concentration of Fe(3+) in pre-treatment, and pyrolysis temperatures, on the efficacy of the Fe-biochar. The efficacy of Fe-biochar increased with increasing concentrations of Fe(3+) in the pre-treatment solutions, and decreased with increasing pyrolysis temperatures. The optimized Fe-biochar for each biomass was produced by treatment with a 12.5% w/v Fe(3+) solution, followed by slow pyrolysis at 300 °C. The Fe-biochar produced in this way had higher a biosorption capacity for As and Mo (62.5-80.7 and 67.4-78.5 mg g(-1) respectively) than Se (14.9-38.8 mg g(-1)) in single-element mock effluents, and the Fe-biochar produced from Oedogonium had a higher capacity for all elements than the Fe-biochar produced from Gracilaria waste. Regardless, the optimal Fe-biochars from both biomass sources were able to effectively treat Se, As and Mo simultaneously in an ash disposal effluent from a power station. The production of Fe-biochar from macroalgae is a promising technique for treatment of complex effluents containing oxyanions.Copyright © 2015 Elsevier Ltd. All rights reserved.JohanssonCharlotte LCLMACRO - the Centre for Macroalgal Resources and Biotechnology, College of Marine and Environmental Sciences, James Cook University, Townsville 4811, Australia. Electronic address: charlotte.johansson@my.jcu.edu.au.PaulNicholas ANAMACRO - the Centre for Macroalgal Resources and Biotechnology, College of Marine and Environmental Sciences, James Cook University, Townsville 4811, Australia.de NysRockyRMACRO - the Centre for Macroalgal Resources and Biotechnology, College of Marine and Environmental Sciences, James Cook University, Townsville 4811, Australia.RobertsDavid ADAMACRO - the Centre for Macroalgal Resources and Biotechnology, College of Marine and Environmental Sciences, James Cook University, Townsville 4811, Australia.engJournal ArticleResearch Support, Non-U.S. Gov't20150927
EnglandJ Environ Manage04016640301-47970Coal0Coal Ash0Water Pollutants, Chemical0biochar16291-96-6Charcoal81AH48963UMolybdenumE1UOL152H7IronH6241UJ22BSeleniumN712M78A8GArsenicIMArsenicchemistryBiomassCharcoalchemistryChlorophytachemistryCoalCoal AshchemistryFresh WaterGracilariachemistryIronchemistryMolybdenumchemistrySeaweedchemistrySeleniumchemistryWater Pollutants, ChemicalchemistryWater PurificationmethodsBiocharBioremediationBiosorptionGracilariaMacroalgaeOedogoniumPyrolysis
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1878-16321652016MayThe spine journal : official journal of the North American Spine SocietySpine JThe long-term outcome of lumbar fusion in the Swedish lumbar spine study.579587579-8710.1016/j.spinee.2015.08.065S1529-9430(15)01371-6Current literature suggests that in the long-term, fusion of the lumbar spine in chronic low back pain (CLBP) does not result in an outcome clearly better than structured conservative treatment modes.This study aimed to assess the long-term outcome of lumbar fusion in CLBP, and also to assess methodological problems in long-term randomized controlled trials (RCTs).A prospective randomized study was carried out.A total of 294 patients (144 women and 150 men) with CLBP of at least 2 years' duration were randomized to lumbar fusion or non-specific physiotherapy. The mean follow-up time was 12.8 years (range 9-22). The follow up rate was 85%; exclusion of deceased patients resulted in a follow-up rate of 92%.Global Assessment (GA) of back pain, Oswestry Disability Index (ODI), visual analogue scale (VAS) for back and leg pain, Zung depression scale were determined. Work status, pain medication, and pain frequency were also documented.Standardized outcome questionnaires were obtained before treatment and at long-term follow-up. To optimize control for group changers, four models of data analysis were used according to (1) intention to treat (ITT), (2) "as treated" (AT), (3) per protocol (PP), and (4) if the conservative group automatically classify group changers as unchanged or worse in GA (GCAC). The initial study was sponsored by Acromed (US$50,000-US$100,000).Except for the ITT model, the GA, the primary outcome measure, was significantly better for fusion. The proportion of patients much better or better in the fusion group was 66%, 65%, and 65% in the AT, PP, and GCAC models, respectively. In the conservative group, the same proportions were 31%, 37%, and 22%, respectively. However, the ODI, VAS back pain, work status, pain medication, and pain frequency were similar between the two groups.One can conclude that from the patient's perspective, reflected by the GA, lumbar fusion surgery is a valid treatment option in CLBP. On the other hand, secondary outcome measures such as ODI and work status, best analyzed by the PP model, indicated that substantial disability remained at long-term after fusion as well as after conservative treatment. The lack of objective outcome measures in CLBP and the cross-over problem transforms an RCT to an observational study, that is, Level 2 evidence. The discrepancy between the primary and secondary outcome measures prevents a strong conclusion on whether to recommend fusion in non-specific low back pain.Copyright © 2015 Elsevier Inc. All rights reserved.HedlundRuneRDepartment of Orthopaedics, Salhgrenska University Hospital, Bruna stråket 11, Gothenburg, SE 413 45, Sweden. Electronic address: rune.hedlund@vgregion.se.JohanssonChristerCDepartment of Orthopaedics, Salhgrenska University Hospital, Bruna stråket 11, Gothenburg, SE 413 45, Sweden.HäggOlleOGöteborg Spine Center, Gruvgatan 8, Västra Frölunda, SE 421 30, Sweden.FritzellPeterPDepartment of Orthopedics, Länssjukhuset, Ryhov, SE 551 85 Ryhov, Sweden.TullbergTychoTStockholm Spine Center AB, Löwenströmska Sjukhuset, Upplands Väsby, SE 194 89, Sweden.Swedish Lumbar Spine Study GroupengJournal ArticleRandomized Controlled Trial20150909
United StatesSpine J1011307321529-9430IMSpine J. 2016 May;16(5):588-90. doi: 10.1016/j.spinee.2015.12.00127261844Spine J. 2017 May;17(5):754. doi: 10.1016/j.spinee.2016.12.00628431682AdultFemaleHumansLow Back PainsurgeryLumbar VertebraesurgeryMaleMiddle AgedPhysical Therapy Modalitiesadverse effectsPostoperative ComplicationsProspective StudiesRandomized Controlled Trials as TopicSpinal Fusionadverse effectsSurveys and QuestionnairesSwedenTreatment OutcomeChronic low back painConservative treatmentLong-term outcomeLumbar fusionPhysical therapyRandomized trial
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1422-00671692015Aug27International journal of molecular sciencesInt J Mol SciClassification of Magnetic Nanoparticle Systems--Synthesis, Standardization and Analysis Methods in the NanoMag Project.203082032520308-2510.3390/ijms160920308This study presents classification of different magnetic single- and multi-core particle systems using their measured dynamic magnetic properties together with their nanocrystal and particle sizes. The dynamic magnetic properties are measured with AC (dynamical) susceptometry and magnetorelaxometry and the size parameters are determined from electron microscopy and dynamic light scattering. Using these methods, we also show that the nanocrystal size and particle morphology determines the dynamic magnetic properties for both single- and multi-core particles. The presented results are obtained from the four year EU NMP FP7 project, NanoMag, which is focused on standardization of analysis methods for magnetic nanoparticles.BogrenSaraSAcreo Swedish ICT AB, Arvid Hedvalls Backe 4, Box 53071, SE-400 14 Göteborg, Sweden. sara.bogren@acreo.se.FornaraAndreaASP Technical Research Institute of Sweden, Box 5607, SE-114 86 Stockholm, Sweden. andrea.fornara@sp.se.LudwigFrankFInstitute of Electrical Measurement and Fundamental Electrical Engineering, TU Braunschweig D-38106, Germany. f.ludwig@tu-bs.de.Del Puerto MoralesMariaMInstituto de Ciencia de Materiales de Madrid, ICMM-CSIC, Cantoblanco, 28049 Madrid, Spain. puerto@icmm.csic.es.SteinhoffUweUPhysikalisch-Technische Bundesanstalt, D-10587 Berlin, Germany. Uwe.Steinhoff@ptb.de.HansenMikkel FougtMFDepartment of Micro and Nanotechnology, Technical University of Denmark, DTU Nanotech, Building 345 East, Kgs. Lyngby DK-2800, Denmark. mikkel.hansen@nanotech.dtu.dk.KazakovaOlgaONational Physical Laboratory, TW11 0LW Teddington, UK. olga.kazakova@npl.co.uk.JohanssonChristerCAcreo Swedish ICT AB, Arvid Hedvalls Backe 4, Box 53071, SE-400 14 Göteborg, Sweden. christer.johansson@acreo.se.engJournal ArticleResearch Support, Non-U.S. Gov't20150827
SwitzerlandInt J Mol Sci1010927911422-00670Magnetite NanoparticlesIMAlgorithmsMagnetite NanoparticleschemistryclassificationModels, Theoreticalmagnetic analysismagnetic materialmagnetic nanoparticlesmagnetic synthesisnanostructuresstandardization
201573201581420158192015986020159860201652560201591epublish26343639PMC461320510.3390/ijms160920308ijms160920308Pankhurst Q.A., Connolly J., Jones S.K., Dobson J. Applications of magnetic nanoparticles in biomedicine. J. Phys. D Appl. Phys. 2003;36:R167–R181. doi: 10.1088/0022-3727/36/13/201.10.1088/0022-3727/36/13/201Krishnan K.M. Biomedical nanomagnetics: A spin through possibilities in imaging, diagnostics, and therapy. IEEE Trans. Magn. 2010;46:2523–2558. doi: 10.1109/TMAG.2010.2046907.10.1109/TMAG.2010.2046907PMC294996920930943NanoMag-Project. [(accessed on 1 November 2013)]. Available online: www.nanomag-project.eu.Gutiérrez L., Costo R., Grüttner C., Westphal F., Gehrke N., Heinke D., Fornara A., Pankhurst Q.A., Johansson C., Veintemillas-Verdaguer S., et al. Synthesis methods to prepare single- and multi-core iron oxide nanoparticles for biomedical applications. Dalton Trans. 2015;44:2943–2951. doi: 10.1039/C4DT03013C.10.1039/C4DT03013C25564784Ahrentorp F., Astalan A., Blomgren J., Jonasson C., Wetterskog E., Svedlindh P., Lak A., Ludwig F., van IJzendoorn L.J., Westphal F., et al. Effective particle magnetic moment of multi-core particles. J. Magn. Magn. Mater. 2015;380 doi: 10.1016/j.jmmm.2014.09.070.10.1016/j.jmmm.2014.09.070Ludwig F., Kazakova O., Barquín L.F., Fornara A., Trahms L., Steinhoff U., Svedlindh P., Wetterskog E., Pankhurst Q.A., Southern P., et al. Magnetic, structural, and particle size analysis of single- and multi-core magnetic nanoparticles. IEEE Trans. Mag. 2014;50 doi: 10.1109/TMAG.2014.2321456.10.1109/TMAG.2014.2321456Ahrentorp F., Astalan A.P., Jonasson C., Blomgren J., Qi B., Mefford O.T., Yan M., Courtois J., Berret J.F., Fresnais J., et al. Sensitive high frequency AC suceptometry in magnetic nanoparticle applications. AIP Conf. Proc. 2010;1311:213–223.Öisjöen F., Schneiderman J.F., Astalan A.P., Kalabukhov A., Johansson C., Winkler D. A new approach for bioassays based on frequency- and time-domain measurements of magnetic nanoparticles. Biosens. Bioelectron. 2010;25:1008–1013. doi: 10.1016/j.bios.2009.09.013.10.1016/j.bios.2009.09.01319822413Ferguson R.M., Khandhar A., Jonasson C., Blomgren J., Johansson C., Krishnan K.M. Size-dependent relaxation properties of monodisperse magnetite nanoparticles measured over seven decades of frequency by AC susceptometry. IEEE Trans. Magn. 2013;49:3441–3444. doi: 10.1109/TMAG.2013.2239621.10.1109/TMAG.2013.2239621PMC424860325473124Chung S.H., Hoffmann A., Guslienko K., Bader S.D., Liu C., Kay B., Makowski L., Chen L. J. Appl. Phys. Vol. 97. H; 2005. Biological sensing with magnetic nanoparticles using Brownian relaxation.10.1063/1.1853694Ludwig F. Characterization of Magnetic Core-Shell Nanoparticle Suspensions Using ac Susceptibility for Frequencies up to 1 MHz. AIP Conf. Proc. 2010;1311 doi: 10.1063/1.3530020.10.1063/1.3530020Ludwig F., Heim E., Schilling M. Characterization of magnetic core-shell nanoparticles by fluxgate magnetorelaxometry, ac susceptibility, transmission electron microscopy and photon correlation spectroscopy—A comparative study. J. Magn. Magn. Mater. 2009;321:1644–1647. doi: 10.1016/j.jmmm.2009.02.105.10.1016/j.jmmm.2009.02.105Ludwig F., Mäuselein S., Heim E., Schilling M. Magnetorelaxometry of magnetic nanoparticles in magnetically unshielded environment utilizing a differential fluxgate arrangement. Rev. Sci. Instrum. 2005;76:106102-1–106102-3. doi: 10.1063/1.2069776.10.1063/1.2069776Ludwig F., Remmer H., Kuhlmann C., Wawrzik T., Arami H., Ferguson R.M., Krishnan K.M. Self-consistent magnetic properties of magnetite tracers optimized for magnetic particle imaging measured by ac susceptometry, magnetorelaxometry and magnetic particle spectroscopy. J. Magn. Magn. Mater. 2014;360:169–173. doi: 10.1016/j.jmmm.2014.02.020.10.1016/j.jmmm.2014.02.020PMC433891325729125Ludwig F., Guillaume A., Schilling M., Frickel N., Schmidt A.M. Determination of core and hydrodynamic size distributions of CoFe2O4 nanoparticle suspensions using ac susceptibility measurements. J. Appl. Phys. 2010;108:033918-1–033918-5. doi: 10.1063/1.3463350.10.1063/1.3463350Andrés Vergés M., Costo R., Roca A.G., Marco J.F., Goya G.F., Serna C.J., Morales M.P. Uniform water stable magnetite nanoparticles with diameters around the monodomain—multidomain limit. J. Phys. D: Appl. Phys. 2008;41 doi: 10.1088/0022-3727/41/13/134003.10.1088/0022-3727/41/13/134003Costo R., Bello V., Robic C., Port M., Marco J.F., Morales M.P., Veintemillas-Verdaguer S. Ultrasmall iron oxide nanoparticles for biomedical applications: Improving the colloidal and magnetic properties. Langmuir. 2012;28:178–185. doi: 10.1021/la203428z.10.1021/la203428z22103685
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1422-00671682015Aug20International journal of molecular sciencesInt J Mol SciPolymer/Iron Oxide Nanoparticle Composites--A Straight Forward and Scalable Synthesis Approach.197521976819752-6810.3390/ijms160819752Magnetic nanoparticle systems can be divided into single-core nanoparticles (with only one magnetic core per particle) and magnetic multi-core nanoparticles (with several magnetic cores per particle). Here, we report multi-core nanoparticle synthesis based on a controlled precipitation process within a well-defined oil in water emulsion to trap the superparamagnetic iron oxide nanoparticles (SPION) in a range of polymer matrices of choice, such as poly(styrene), poly(lactid acid), poly(methyl methacrylate), and poly(caprolactone). Multi-core particles were obtained within the Z-average size range of 130 to 340 nm. With the aim to combine the fast room temperature magnetic relaxation of small individual cores with high magnetization of the ensemble of SPIONs, we used small (<10 nm) core nanoparticles. The performed synthesis is highly flexible with respect to the choice of polymer and SPION loading and gives rise to multi-core particles with interesting magnetic properties and magnetic resonance imaging (MRI) contrast efficacy.SommertuneJensJSP, Technical Research Institute of Sweden, Box 5607, SE-114 86 Stockholm, Sweden. jens.sommertune@sp.se.SugunanAbhilashASP, Technical Research Institute of Sweden, Box 5607, SE-114 86 Stockholm, Sweden. abhilash.sugunan@sp.se.AhniyazAnwarASP, Technical Research Institute of Sweden, Box 5607, SE-114 86 Stockholm, Sweden. anwar.ahniyaz@sp.se.BejhedRebecca StjernbergRSDepartment of Engineering Sciences, Solid State Physics, Uppsala University, SE-751 21 Uppsala, Sweden. Rebecca.Bejhed@angstrom.uu.se.SarweAnnaAAcreo Swedish ICT AB, Box 53071, SE-400 14 Göteborg, Sweden. Anna.Sarwe@acreo.se.JohanssonChristerCAcreo Swedish ICT AB, Box 53071, SE-400 14 Göteborg, Sweden. Christer.Johansson@acreo.se.BalcerisChristophCInstitute of Electrical Measurement and Fundamental Electrical Engineering, TU Braunschweig, D-38106 Braunschweig, Germany. c.balceris@tu-bs.de.LudwigFrankFInstitute of Electrical Measurement and Fundamental Electrical Engineering, TU Braunschweig, D-38106 Braunschweig, Germany. f.ludwig@tu-bs.de.PosthOliverOPhysikalisch-Technische Bundesanstalt, 10587 Berlin, Germany. oliver.posth@ptb.de.FornaraAndreaASP, Technical Research Institute of Sweden, Box 5607, SE-114 86 Stockholm, Sweden. andrea.fornara@sp.se.engJournal ArticleResearch Support, Non-U.S. Gov't20150820
SwitzerlandInt J Mol Sci1010927911422-00670Ferric Compounds0Magnetite Nanoparticles0Polymers1K09F3G675ferric oxideIMFerric CompoundschemistryMagnetic Resonance ImagingmethodsMagnetite NanoparticleschemistryParticle SizePolymerschemistryiron oxide nanoparticlemulti corenanocompositepolymer encapsulationsingle core
2015732015872015814201582760201582760201651860201581epublish26307966PMC458132310.3390/ijms160819752ijms160819752Laurent S., Forge D., Port M., Roch A., Robic C., Vander Elst L., Muller R.N. Magnetic iron oxide nanoparticles: Synthesis, stabilization, vectorization, physicochemical characterizations, and biological applications. Chem. Rev. 2008;108:2064–2110. doi: 10.1021/cr068445e.10.1021/cr068445e18543879Gutierrez L., Costo R., Gruttner C., Westphal F., Gehrke N., Heinke D., Fornara A., Pankhurst Q.A., Johansson C., Veintemillas-Verdaguer S., et al. Synthesis methods to prepare single- and multi-core iron oxide nanoparticles for biomedical applications. Dalton Trans. 2015;44:2943–2952. doi: 10.1039/C4DT03013C.10.1039/C4DT03013C25564784Colombo M., Carregal-Romero S., Casula M.F., Gutierrez L., Morales M.P., Bohm I.B., Heverhagen J.T., Prosperi D., Parak W.J. Biological applications of magnetic nanoparticles. Chem. Soc. Rev. 2012;41:4306–4334. doi: 10.1039/c2cs15337h.10.1039/c2cs15337h22481569Li S., Qin J., Fornara A., Toprak M., Muhammed M., Kim D.K. Synthesis and magnetic properties of bulk transparent pmma/fe-oxide nanocomposites. Nanotechnology. 2009;20 doi: 10.1088/0957-4484/20/18/185607.10.1088/0957-4484/20/18/18560719420623Thaler M., Roy S., Fornara A., Bitsche M., Qin J., Muhammed M., Salvenmoser W., Rieger G., Fischer A.S., Glueckert R. Visualization and analysis of superparamagnetic iron oxide nanoparticles in the inner ear by light microscopy and energy filtered tem. Nanomed. Nanotechnol. Biol. Med. 2011;7:360–369. doi: 10.1016/j.nano.2010.11.005.10.1016/j.nano.2010.11.00521146633Fornara A., Johansson P., Petersson K., Gustafsson S., Qin J., Olsson E., Ilver D., Krozer A., Muhammed M., Johansson C. Tailored magnetic nanoparticles for direct and sensitive detection of biomolecules in biological samples. Nano Lett. 2008;8:3423–3428. doi: 10.1021/nl8022498.10.1021/nl802249818754596Qin J., Asempah I., Laurent S., Fornara A., Muller R.N., Muhammed M. Injectable superparamagnetic ferrogels for controlled release of hydrophobic drugs. Adv. Mater. 2009;21:1354–1357. doi: 10.1002/adma.200800764.10.1002/adma.200800764Zou J., Zhang W., Poe D., Qin J., Fornara A., Zhang Y., Ramadan U.A., Muhammed M., Pyykkö I. MRI manifestation of novel superparamagnetic iron oxide nanoparticles in the rat inner ear. Nanomedicine. 2010;5:739–754. doi: 10.2217/nnm.10.45.10.2217/nnm.10.4520662645Okoli C., Fornara A., Qin J., Toprak M.S., Dalhammar G., Muhammed M., Rajarao G.K. Characterization of superparamagnetic iron oxide nanoparticles and its application in protein purification. J. Nanosci. Nanotechnol. 2011;11:10201–10206. doi: 10.1166/jnn.2011.5007.10.1166/jnn.2011.500722413365Fornara A., Recalenda A., Qin J., Sugunan A., Ye F., Laurent S., Muller R.N., Zou J., Usama A.-R., Toprak M.S., et al. Polymeric/inorganic multifunctional nanoparticles for simultaneous drug delivery and visualization. MRS Proc. 2010;1257 doi: 10.1557/PROC-1257-O04-03.10.1557/PROC-1257-O04-03Gustafsson S., Fornara A., Petersson K., Johansson C., Muhammed M., Olsson E. Evolution of structural and magnetic properties of magnetite nanoparticles for biomedical applications. Cryst. Growth Des. 2010;10:2278–2284. doi: 10.1021/cg901602w.10.1021/cg901602wLudwig F., Kazakova O., Barquín L.F., Fornara A., Trahms L., Steinhoff U., Svedlindh P., Wetterskog E., Pankhurst Q.A., Southern P., et al. Magnetic, structural, and particle size analysis of single- and multi-core magnetic nanoparticles. IEEE Trans. Magn. 2014;50 doi: 10.1109/TMAG.2014.2321456.10.1109/TMAG.2014.2321456Ye F., Laurent S., Fornara A., Astolfi L., Qin J., Roch A., Martini A., Toprak M.S., Muller R.N., Muhammed M. Uniform mesoporous silica coated iron oxide nanoparticles as a highly efficient, nontoxic MRI T2 contrast agent with tunable proton relaxivities. Contrast Media Mol. Imaging. 2012;7:460–468. doi: 10.1002/cmmi.1473.10.1002/cmmi.1473PMC453052422821880Rahman M.M., Elaissari A. Organic-inorganic hybrid magnetic latex. Adv. Polym. Sci. 2010;233:237–281.Tanyolaç D., Özdural A.R. New low cost magnetic material: Magnetic polyvinylbutyral microbeads. React Funct. Polym. 2000;43:279–286. doi: 10.1016/S1381-5148(99)00054-1.10.1016/S1381-5148(99)00054-1Hamoudeh M., Faraj A.A., Canet-Soulas E., Bessueille F., Léonard D., Fessi H. Elaboration of plla-based superparamagnetic nanoparticles: Characterization, magnetic behaviour study and in vitro relaxivity evaluation. Int. J. Pharm. 2007;338:248–257. doi: 10.1016/j.ijpharm.2007.01.023.10.1016/j.ijpharm.2007.01.02317317054Hamoudeh M., Fessi H. Preparation, characterization and surface study of poly-epsilon caprolactone magnetic microparticles. J. Colloid Interface Sci. 2006;300:584–590. doi: 10.1016/j.jcis.2006.04.024.10.1016/j.jcis.2006.04.02416756986Lee S.J., Jeong J.R., Shin S.C., Kim J.C., Chang Y.H., Lee K.H., Kim J.D. Magnetic enhancement of iron oxide nanoparticles encapsulated with poly(d,l-latide-co-glycolide) Colloid Surf. A. 2005;255:19–25. doi: 10.1016/j.colsurfa.2004.12.019.10.1016/j.colsurfa.2004.12.019Demortiere A., Panissod P., Pichon B.P., Pourroy G., Guillon D., Donnio B., Begin-Colin S. Size-dependent properties of magnetic iron oxide nanocrystals. Nanoscale. 2011;3:225–232. doi: 10.1039/C0NR00521E.10.1039/C0NR00521E21060937Ferguson R.M., Khandhar A.P., Jonasson C., Blomgren J., Johansson C., Krishnan K.M. Size-dependent relaxation properties of monodisperse magnetite nanoparticles measured over seven decades of frequency by ac susceptometry. IEEE Trans. Magn. 2013;49:3441–3444. doi: 10.1109/TMAG.2013.2239621.10.1109/TMAG.2013.2239621PMC424860325473124Öisjöen F., Schneiderman J.F., Astalan A.P., Kalabukhov A., Johansson C., Winkler D. A new approach for bioassays based on frequency- and time-domain measurements of magnetic nanoparticles. Biosens. Bioelectron. 2010;25:1008–1013. doi: 10.1016/j.bios.2009.09.013.10.1016/j.bios.2009.09.01319822413Ahrentorp F., Astalan A.P., Jonasson C., Blomgren J., Qi B., Mefford O.T., Yan M., Courtois J., Berret J.F., Fresnais J., et al. Sensitive high frequency ac susceptometry in magnetic nanoparticle applications. AIP Conf. Proc. 2010;1311:213–223.Ludwig F., Guillaume A., Schilling M., Frickel N., Schmidt A.M. Determination of core and hydrodynamic size distributions of coFe2O4 nanoparticle suspensions using ac susceptibility measurements. J Appl. Phys. 2010;108 doi: 10.1063/1.3463350.10.1063/1.3463350Ahrentorp F., Astalan A., Blomgren J., Jonasson C., Wetterskog E., Svedlindh P., Lak A., Ludwig F., van Ijzendoorn L.J., Westphal F., et al. Effective particle magnetic moment of multi-core particles. J. Magn. Magn. Mater. 2015;380:221–226. doi: 10.1016/j.jmmm.2014.09.070.10.1016/j.jmmm.2014.09.070Wiekhorst F., Steinhoff U., Eberbeck D., Trahms L. Magnetorelaxometry assisting biomedical applications of magnetic nanoparticles. Pharm. Res. 2012;29:1189–1202. doi: 10.1007/s11095-011-0630-3.10.1007/s11095-011-0630-3PMC333234422161287Ludwig F., Mäuselein S., Heim E., Schilling M. Magnetorelaxometry of magnetic nanoparticles in magnetically unshielded environment utilizing a differential fluxgate arrangement. Rev. Sci. Instrum. 2005;76:106102. doi: 10.1063/1.2069776.10.1063/1.2069776Liu G., Wang Z., Lu J., Xia C., Gao F., Gong Q., Song B., Zhao X., Shuai X., Chen X., et al. Low molecular weight alkyl-polycation wrapped magnetite nanoparticle clusters as MRI probes for stem cell labeling and in vivo imaging. Biomaterials. 2011;32:528–537. doi: 10.1016/j.biomaterials.2010.08.099.10.1016/j.biomaterials.2010.08.09920869767Reimer P., Balzer T. Ferucarbotran (resovist): A new clinically approved res-specific contrast agent for contrast-enhanced mri of the liver: Properties, clinical development, and applications. Eur. Radiol. 2003;13:1266–1276.12764641Yathindranath V., Rebbouh L., Moore D.F., Miller D.W., Lierop J.V., Hegmann T. A versatile method for the reductive, one-pot synthesis of bare, hydrophilic and hydrophobic magnetite nanoparticles. Adv. Mater. 2011;21:1457–1464.Chantrell R.W., Hoon S.H., Tanner B.K. Time-dependent magnetization in fine-particle ferromagnetic systems. J. Magn. Magn. Mater. 1983;38:133–141. doi: 10.1016/0304-8853(83)90037-9.10.1016/0304-8853(83)90037-9Romanus E., Berkov D.V., Prass S., Groβ C., Weitschies W., Weber P. Determination of energy barrier distributions of magnetic nanoparticles by temperature dependent magnetorelaxometry. Nanotechnology. 2003;14:1251–1254. doi: 10.1088/0957-4484/14/12/003.10.1088/0957-4484/14/12/00321444978Eberbeck D., Wiekhorst F., Steinhoff U., Trahms L. Aggregation behaviour of magnetic nanoparticle suspensions investigated by magnetorelaxometry. J. Phys. Condens. Matter. 2006;18:S2829–S2846. doi: 10.1088/0953-8984/18/38/S20.10.1088/0953-8984/18/38/S20Ludwig F., Heim E., Schilling M. Characterization of superparamagnetic nanoparticles by analyzing the magnetization and relaxation dynamics using fluxgate magnetometers. J. Appl. Phys. 2007;101 doi: 10.1063/1.2738416.10.1063/1.2738416
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2052-1707332015JunPharmacology research & perspectivesPharmacol Res PerspectThe discovery of a selective and potent A2a agonist with extended lung retention.e00134e00134e0013410.1002/prp2.134Although the anti-inflammatory role of the A2a receptor is well established, controversy remains with regard to the therapeutic value for A2a agonists in treatment of inflammatory lung diseases, also as a result of unwanted A2a-mediated cardiovascular effects. In this paper, we describe the discovery and characterization of a new, potent and selective A2a agonist (compound 2) with prolonged lung retention and limited systemic exposure following local administration. To support the lead optimization chemistry program with compound selection and profiling, multiple in vitro and in vivo assays were used, characterizing compound properties, pharmacodynamics (PD), and drug concentrations. Particularly, pharmacokinetic-PD modeling was applied to quantify the effects on the cardiovascular system, and an investigative toxicology study in rats was performed to explore potential myocardial toxicities. Compound 2, in comparison to a reference A2a agonist, UK-432,097, demonstrated higher solubility, lower lipophilicity, lower plasma protein binding, high rat lung retention (28% remaining after 24 h), and was efficacious in a lung inflammatory rat model following intratracheal dosing. Despite these properties, compound 2 did not provide a sufficient therapeutic index, that is, separation of local anti-inflammatory efficacy in the lung from systemic side effects in the cardiovascular system. The plasma concentration that resulted in induction of hypotension (half maximal effective concentration; EC50 0.5 nmol/L) correlated to the in vitro A2a potency (rIC50 0.6 nmol/L). Histopathological lesions in the heart were observed at a dose level which is threefold above the efficacious dose level in the inflammatory rat lung model. In conclusion, compound 2 is a highly potent and selective A2a agonist with significant lung retention after intratracheal administration. Despite its local anti-inflammatory efficacy in rat lung, small margins to the cardiovascular effects suggested limited therapeutic value of this compound for treatment of inflammatory lung disease by the inhaled route.ÅstrandAnnika B MABRIA iMed, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.Lamm BergströmEvaERIA iMed, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.ZhangHuiHDrug Safety & Metabolism, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.BörjessonLenaLRIA iMed, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.SöderdahlThereseTDrug Safety & Metabolism, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.WingrenCeciliaCRIA iMed, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.JanssonAnne-HeleneAHRIA iMed, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.SmailagicAmirARIA iMed, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.JohanssonCamillaCDrug Safety & Metabolism, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.BladhHåkanHAstraZeneca R&D Lund SE-221 87, Lund, Sweden.ShamovskyIgorIRIA iMed, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.TunekAndersARIA iMed, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.DrmotaTomasTRIA iMed, AstraZeneca R&D Mölndal SE-431 59, Mölndal, Sweden.engJournal Article20150504
United StatesPharmacol Res Perspect1016263692052-1707A2aAdenosine receptorblood pressureheart rateinflammationlung retentionpharmacokinetic-pharmacodynamictherapeutic window
2015252015219201584602015846020158461201554ppublish26236482PMC449275010.1002/prp2.134Al Jaroudi W, Iskandrian AE. Regadenoson: a new myocardial stress agent. J Am Coll Cardiol. 2012;54(13):1123–1130. doi: 10.1016/j.jacc.2009.04.089.10.1016/j.jacc.2009.04.08919761931Albassam MA, Smith GS, Macallum GE. Arteriopathy induced by an adenosine agonist-antihypertensive in monkeys. Toxicol Pathol. 1998;26(3):375–80. May-Jun;. doi: 10.1177/019262339802600311.10.1177/0192623398026003119608643Caruso M, Alamo A, Crisafulli E, Raciti C, Fisichella A, Polosa R. Adenosine signaling pathways as potential therapeutic targets in respiratory disease. Expert Opin Ther Targets. 2013;17(7):761–772. doi: 10.1517/14728222.2013.795220.10.1517/14728222.2013.79522023642090Chen JF, Eltzschig HK, Fredholm BB. Adenosine receptors as drug targets–what are the challenges? Nat Rev Drug Discov. 2013;12(4):265–286. doi: 10.1038/nrd3955.10.1038/nrd3955PMC393007423535933Cooper AE, Ferguson D, Grime K. Optimisation of DMPK by the inhaled route: challenges and approaches. Curr Drug Metab. 2012;13:457–473. doi: 10.2174/138920012800166571.10.2174/13892001280016657122299825Deflorian F, Kumar TS, Phan K, Gao ZG, Xu F, Wu H, et al. Evaluation of molecular modeling of agonist binding in light of the crystallographic structure of an agonist-bound A₂A adenosine receptor. J Med Chem. 2012;55(1):538–552. doi: 10.1021/jm201461q.10.1021/jm201461qPMC326178522104008Dhalla AK, Wong MY, Wang WQ, Biaggioni I, Belardinelli L. Tachycardia caused by A2A adenosine receptor agonists is mediated by direct sympathoexcitation in awake rats. J Pharmacol Exp Ther. 2006;316:695–702. doi: 10.1124/jpet.105.095323.10.1124/jpet.105.09532316227469Fozard JR, Ellis KM, Villela Dantas MF, Tigani B, Mazzoni L. Effects of CGS 21680, a selective adenosine A2A receptor agonist, on allergic airways inflammation in the rat. Eur J Pharmacol. 2002;438(3):183–188. doi: 10.1016/S0014-2999(02)01305-5.10.1016/S0014-2999(02)01305-511909610Fredholm BB, Cunha RA, Svenningsson P. Pharmacology of adenosine A2A receptors and therapeutic applications. Curr Top Med Chem. 2002;3:1349–1364. doi: 10.2174/1568026033392200.10.2174/156802603339220012570759Gessi S, Merighi S, Varani K, Borea PA. Adenosine receptors in health and disease. Adv Pharmacol. 2011;61:41–75. doi: 10.1016/B978-0-12-385526-8.00002-3.10.1016/B978-0-12-385526-8.00002-321586355Harada N, Okajima K, Murakami K, Usune S, Sato C, Ohshima K, et al. Adenosine and selective A(2A) receptor agonists reduce ischemia/reperfusion injury of rat liver mainly by inhibiting leukocyte activation. J Pharmacol Exp Ther. 2000;294:1034–1042.10945856Hasko G, Kuhel DG, Chen JF, Schwarzschild MA, Deitch EA, Mabley JG, et al. Adenosine inhibits IL-12 and TNF-[alpha] production via adenosine A2A receptor-dependent and independent mechanisms. FASEB J. 2000;14:2065–2074. doi: 10.1096/fj.99-0508com.10.1096/fj.99-0508com11023991Johnson SG, Peters S. Advances in pharmacologic stress agents: focus on regadenoson. J Nucl Med Technol. 2010;38(3):163–171. doi: 10.2967/jnmt.109.065581.10.2967/jnmt.109.06558120724531Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL. Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B. 2001;105:6474–6487. doi: 10.1021/jp003919d.10.1021/jp003919dLebon G, Warne T, Edwards PC, Bennett K, Langmead CJ, Leslie AG, et al. Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation. Nature. 2011;474(7352):521–525. doi: 10.1038/nature10136.10.1038/nature10136PMC314609621593763Ledent C, Vaugeois JM, Schiffmann SN, Pedrazzini T, El Yacoubi M, Vanderhaeghen JJ, et al. Aggressiveness, hypoalgesia and high blood pressure in mice lacking the adenosine A2a receptor. Nature. 1997;388:674–678.9262401Linden J. Molecular approach to adenosine receptors: receptor-mediated mechanisms of tissue protection. Annu Rev Pharmacol Toxicol. 2001;41:775–787. doi: 10.1146/annurev.pharmtox.41.1.775.10.1146/annurev.pharmtox.41.1.77511264476Macallum GE, Walker RM, Barsoum NJ, Smith GS. Preclinical toxicity studies of an adenosine agonist, N-(2,2-diphenylethyl) adenosine. Toxicology. 1991;68:21–35.1871777Mantell SJ, Stephenson PT, Monaghan SM, Maw GN, Trevethick MA, Yeadon M, et al. Inhaled adenosine A(2A) receptor agonists for the treatment of chronic obstructive pulmonary disease. Bioorg Med Chem Lett. 2008;18(4):1284–1287. doi: 10.1016/j.bmcl.2008.01.033.10.1016/j.bmcl.2008.01.03318243699Mantell SJ, Stephenson PT, Monaghan SM, Maw GN, Trevethick MA, Yeadon M, et al. SAR of a series of inhaled A(2A) agonists and comparison of inhaled pharmacokinetics in a preclinical model with clinical pharmacokinetic data. Bioorg Med Chem Lett. 2009;19(15):4471–4475. doi: 10.1016/j.bmcl.2009.05.027.10.1016/j.bmcl.2009.05.02719501510Mantell S, Jones R, Trevethick M. Design and application of locally delivered agonists of the adenosine A(2A) receptor. Expert Rev Clin Pharmacol. 2010;3(1):55–72. doi: 10.1586/ecp.09.57.10.1586/ecp.09.5722111533Mathôt RA, Cleton A, Soudijn W, IJzerman AP, Danhof M. Pharmacokinetic modelling of the haemodynamic effects of the A2a adenosine receptor agonist CGS 21680C in conscious normotensive rats. Br J Pharmacol. 1995;114:761–768.PMC15101967773536Monopoli A, Casati C, Lozza G, Forlani A, Ongini E. Cardiovascular pharmacology of the A2A adenosine receptor antagonist, SCH 58261, in the rat. J Pharmacol Exp Ther. 1998;285:9–15.9535988Müller CE, Jacobson KA. Recent developments in adenosine receptor ligands and their potential as novel drugs. Biochim Biophys Acta. 2011;1808(5):1290–1308. doi: 10.1016/j.bbamem.2010.12.017.10.1016/j.bbamem.2010.12.017PMC343732821185259Nekooeian AA, Tabrizchi R. Effects of adenosine A2A receptor agonist, CGS 21680, on blood pressure, cardiac index and arterial conductance in anaesthetized rats. Eur J Pharmacol. 1996;307(2):163–169. doi: 10.1016/0014-2999(96)00250-6.10.1016/0014-2999(96)00250-68832218Ohta A, Sitkovsky M. Role of G-protein-coupled adenosine receptors in downregulation of inflammation and protection from tissue damage. Nature. 2001;414:916–920. doi: 10.1038/414916a.10.1038/414916a11780065Stepp DW, Van Bibber R, Kroll K, Feigl EO. Quantitative relation between interstitial adenosine concentration and coronary blood flow. Circ Res. 1996;79(3):601–610. doi: 10.1161/01.RES.79.3.601.10.1161/01.RES.79.3.6018781493Still WC, Tempczyk A, Hawlely RC, Hendrickson TA. General treatment of solvation for molecular mechanics. J Am Chem Soc. 1990;112:6127–6129. doi: 10.1021/ja00172a038.10.1021/ja00172a038Trevethick MA, Mantell SJ, Stuart EF, Barnard A, Wright KN, Yeadon M. Treating lung inflammation with agonists of the adenosine A2A receptor: promises, problems and potential solutions. Br J Pharmacol. 2008;155(4):463–474. doi: 10.1038/bjp.2008.329.10.1038/bjp.2008.329PMC257967118846036Webb RL, McNeal RB, Jr, Barclay BW, Yasay GD. Hemodynamic effects of adenosine agonists in the conscious spontaneously hypertensive rat. J Pharmacol Exp Ther. 1990;254:1090–1099.2203898Webb RL, Barclay BW, Graybill SC. Cardiovascular effects of adenosine A2 agonists in the conscious spontaneously hypertensive rat: a comparative study of three structurally distinct ligands. J Pharmacol Exp Ther. 1991;259(3):1203–1212.1684818Xu FX, Wu H, Katritch V, Han GW, Jacobson KA, Gao ZG, et al. Structure of an agonist-bound human A2A adenosine receptor. Science. 2011;332:322–327. doi: 10.1126/science.1202793.10.1126/science.1202793PMC308681121393508
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2192-5682542015AugGlobal spine journalGlobal Spine JA Randomized Controlled Trial Comparing Transforaminal Lumbar Interbody Fusion and Uninstrumented Posterolateral Fusion in the Degenerative Lumbar Spine.322328322-810.1055/s-0035-1549033Study Design Randomized controlled trial. Objective Despite a large number of publications of outcomes after spinal fusion surgery, there is still no consensus on the efficacy of the several different fusion methods. The aim of this study was to determine whether transforaminal lumbar interbody fusion (TLIF) results in an improved clinical outcome compared with uninstrumented posterolateral fusion (PLF) in the surgical treatment for chronic low back pain. Methods This study included 135 patients with degenerative disk disease (n = 96) or postdiskectomy syndrome (n = 39). Inclusion criteria were at least 1 year of back pain with or without leg pain in patients aged 20 to 65 with one- or two-level disease. Exclusion criteria were sequestration of disk hernia, psychosocial instability, isthmic spondylolisthesis, drug abuse, and previous spine surgery other than diskectomy. Pain was assessed by visual analog scale (pain index). Functional disability was quantified by the disability rating index and Oswestry Disability Index. The global outcome was assessed by the patient and classified as much better, better, unchanged, or worse. The patients were randomized to conventional uninstrumented PLF (n = 67) or TLIF (n = 68). PLF was performed in a standardized fashion using autograft. TLIF was performed with pedicle titanium screw fixation and a porous tantalum interbody spacer with interbody and posterolateral autograft. The clinical outcome measurements were obtained preoperatively and at 12 and 24 months postoperatively. The 2-year follow-up rate was 98%. Results The two treatment groups improved significantly from preoperatively to 2 years' follow-up. At final follow-up, the results in the TLIF group were significantly superior to those in the PLF group in pain index (2.0 versus 3.9, p = 0.007) and in disability rating index (22 versus 36, p = 0.003). The Oswestry Disability Index was better in the TLIF group (20 versus 28, p = 0.110, not significant). The global assessment was clearly superior in the TLIF group: 63% of patients scored "much better" in the TLIF group as compared with 48% in the PLF group (p = 0.017). Conclusions The results of the current study support the use of TLIF rather than uninstrumented PLF in the surgical treatment of the degenerative lumbar spine. The less optimal outcome after uninstrumented PLF may be explained by the much higher reoperation rate.JalalpourKouroshKDivision of Orthopedics, Department of Clinical Science, Karolinska University Hospital Huddinge, Karolinska Institutet, Stockholm, Sweden.NeumannPavelPDepartment of Orthopaedics, Institute of Clinical Sciences, University of Gothenburg, Gothenburg, Sweden.JohanssonChristerCDepartment of Orthopaedics, Institute of Clinical Sciences, University of Gothenburg, Gothenburg, Sweden.HedlundRuneRDepartment of Orthopaedics, Institute of Clinical Sciences, University of Gothenburg, Gothenburg, Sweden.engJournal Article20150325
EnglandGlobal Spine J1015961562192-5682chronic low back painposterolateral noninstrumented fusiontransforaminal lumbar interbody fusionDisclosures Kourosh Jalalpour, none Pavel Neumann, none Christer Johansson, none Rune Hedlund, Grant: Zimmer Spine; Consulting: Depuy
201411262015242015731602015816020158161201581ppublish26225282PMC451675510.1055/s-0035-15490331400159Fritzell P Hägg O Wessberg P Nordwall A; Swedish Lumbar Spine Study Group. 2001 Volvo Award Winner in Clinical Studies: Lumbar fusion versus nonsurgical treatment for chronic low back pain: a multicenter randomized controlled trial from the Swedish Lumbar Spine Study Group Spine (Phila Pa 1976) 200126232521–2532., discussion 2532–253411725230Babu M A, Coumans J V, Carter B S. et al.A review of lumbar spinal instrumentation: evidence and controversy. J Neurol Neurosurg Psychiatry. 2011;82(9):948–951.21602520Frymoyer J W, Selby D K. Segmental instability. Rationale for treatment. Spine (Phila Pa 1976) 1985;10(3):280–286.3992349Adams M A, Roughley P J. What is intervertebral disc degeneration, and what causes it? Spine (Phila Pa 1976) 2006;31(18):2151–2161.16915105Edgar M A. The nerve supply of the lumbar intervertebral disc. J Bone Joint Surg Br. 2007;89(9):1135–1139.17905946Christensen F B, Hansen E S, Eiskjaer S P. et al.Circumferential lumbar spinal fusion with Brantigan cage versus posterolateral fusion with titanium Cotrel-Dubousset instrumentation: a prospective, randomized clinical study of 146 patients. Spine (Phila Pa 1976) 2002;27(23):2674–2683.12461393Høy K, Bünger C, Niederman B. et al.Transforaminal lumbar interbody fusion (TLIF) versus posterolateral instrumented fusion (PLF) in degenerative lumbar disorders: a randomized clinical trial with 2-year follow-up. Eur Spine J. 2013;22(9):2022–2029.PMC377706523584162Anjarwalla N K, Morcom R K, Fraser R D. Supplementary stabilization with anterior lumbar intervertebral fusion—a radiologic review. Spine (Phila Pa 1976) 2006;31(11):1281–1287.16688045Harms J G, Jeszenszky D. Die posteriore, lumbale, interkorporelle Fusion in unilateraler transforaminaler Technik. Orthop Traumatol. 1998;10:90–102.17332991Zou X, Li H, Bünger M, Egund N, Lind M, Bünger C. Bone ingrowth characteristics of porous tantalum and carbon fiber interbody devices: an experimental study in pigs. Spine J. 2004;4(1):99–105.14749198Salén B A, Spangfort E V, Nygren A L, Nordemar R. The Disability Rating Index: an instrument for the assessment of disability in clinical settings. J Clin Epidemiol. 1994;47(12):1423–1435.7730851Fairbank J C, Couper J, Davies J B, O'Brien J P. The Oswestry low back pain disability questionnaire. Physiotherapy. 1980;66(8):271–273.6450426Lenke L G, Bridwell K H, Bullis D, Betz R R, Baldus C, Schoenecker P L. Results of in situ fusion for isthmic spondylolisthesis. J Spinal Disord. 1992;5(4):433–442.1490041Ekman P, Möller H, Tullberg T, Neumann P, Hedlund R. Posterior lumbar interbody fusion versus posterolateral fusion in adult isthmic spondylolisthesis. Spine (Phila Pa 1976) 2007;32(20):2178–2183.17873808Hägg O Fritzell P Nordwall A; Swedish Lumbar Spine Study Group. The clinical importance of changes in outcome scores after treatment for chronic low back pain Eur Spine J 200312112–20.12592542Glassman S D, Copay A G, Berven S H, Polly D W, Subach B R, Carreon L Y. Defining substantial clinical benefit following lumbar spine arthrodesis. J Bone Joint Surg Am. 2008;90(9):1839–1847.18762642Ekman P, Möller H, Hedlund R. The long-term effect of posterolateral fusion in adult isthmic spondylolisthesis: a randomized controlled study. Spine J. 2005;5(1):36–44.15653083Carragee E J. The rise and fall of the “minimum clinically important difference.”. Spine J. 2010;10(4):283–284.20362245Gatchel R J, Mayer T G, Chou R. What does/should the minimum clinically important difference measure? A reconsideration of its clinical value in evaluating efficacy of lumbar fusion surgery. Clin J Pain. 2012;28(5):387–397.22395333Copay A G, Subach B R, Glassman S D, Polly D W Jr, Schuler T C. Understanding the minimum clinically important difference: a review of concepts and methods. Spine J. 2007;7(5):541–546.17448732Fritzell P Hägg O Wessberg P Nordwall A; Swedish Lumbar Spine Study Group. Chronic low back pain and fusion: a comparison of three surgical techniques: a prospective multicenter randomized study from the Swedish lumbar spine study group Spine (Phila Pa 1976) 200227111131–1141.12045508Videbaek T S, Christensen F B, Soegaard R. et al.Circumferential fusion improves outcome in comparison with instrumented posterolateral fusion: long-term results of a randomized clinical trial. Spine (Phila Pa 1976) 2006;31(25):2875–2880.17139217Zou X, Li H, Teng X. et al.Pedicle screw fixation enhances anterior lumbar interbody fusion with porous tantalum cages: an experimental study in pigs. Spine (Phila Pa 1976) 2005;30(14):E392–E399.16025015Lowe T G, Tahernia A D, O'Brien M F, Smith D A. Unilateral transforaminal posterior lumbar interbody fusion (TLIF): indications, technique, and 2-year results. J Spinal Disord Tech. 2002;15(1):31–38.11891448Humphreys S C, Hodges S D, Patwardhan A G, Eck J C, Murphy R B, Covington L A. Comparison of posterior and transforaminal approaches to lumbar interbody fusion. Spine (Phila Pa 1976) 2001;26(5):567–571.11242386Wu R H, Fraser J F, Härtl R. Minimal access versus open transforaminal lumbar interbody fusion: meta-analysis of fusion rates. Spine (Phila Pa 1976) 2010;35(26):2273–2281.20581757Lee C S, Hwang C J, Lee D H, Kim Y T, Lee H S. Fusion rates of instrumented lumbar spinal arthrodesis according to surgical approach: a systematic review of randomized trials. Clin Orthop Surg. 2011;3(1):39–47.PMC304216821369477Bjarke Christensen F, Stender Hansen E, Laursen M, Thomsen K, Bünger C E. Long-term functional outcome of pedicle screw instrumentation as a support for posterolateral spinal fusion: randomized clinical study with a 5-year follow-up. Spine (Phila Pa 1976) 2002;27(12):1269–1277.12065973Andersen T, Videbaek T S, Hansen E S, Bünger C, Christensen F B. The positive effect of posterolateral lumbar spinal fusion is preserved at long-term follow-up: a RCT with 11–13 year follow-up. Eur Spine J. 2008;17(2):272–280.PMC236554717851701Fischgrund J S, Mackay M, Herkowitz H N, Brower R, Montgomery D M, Kurz L T. 1997 Volvo Award winner in clinical studies. Degenerative lumbar spondylolisthesis with spinal stenosis: a prospective, randomized study comparing decompressive laminectomy and arthrodesis with and without spinal instrumentation. Spine (Phila Pa 1976) 1997;22(24):2807–2812.9431616France J C, Yaszemski M J, Lauerman W C. et al.A randomized prospective study of posterolateral lumbar fusion. Outcomes with and without pedicle screw instrumentation. Spine (Phila Pa 1976) 1999;24(6):553–560.10101819Mardjetko S M Connolly P J Shott S Degenerative lumbar spondylolisthesis. A meta-analysis of literature 1970–1993 Spine (Phila Pa 1976) 199419(20, Suppl):2256S–2265S.7817240Fritzell P Hägg O Wessberg P Nordwall A; Swedish Lumbar Spine Study Group. Chronic low back pain and fusion: a comparison of three surgical techniques: a prospective multicenter randomized study from the Swedish lumbar spine study group Spine (Phila Pa 1976) 200227111131–1141.12045508Zdeblick T A. A prospective, randomized study of lumbar fusion. Preliminary results. Spine (Phila Pa 1976) 1993;18(8):983–991.8367786
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1932-62031072015PloS onePLoS OneCell Fusion along the Anterior-Posterior Neuroaxis in Mice with Experimental Autoimmune Encephalomyelitis.e0133903e0133903e013390310.1371/journal.pone.0133903It is well documented that bone marrow-derived cells can fuse with a diverse range of cells, including brain cells, under normal or pathological conditions. Inflammation leads to robust fusion of bone marrow-derived cells with Purkinje cells and the formation of binucleate heterokaryons in the cerebellum. Heterokaryons form through the fusion of two developmentally differential cells and as a result contain two distinct nuclei without subsequent nuclear or chromosome loss.In the brain, fusion of bone marrow-derived cells appears to be restricted to the complex and large Purkinje cells, raising the question whether the size of the recipient cell is important for cell fusion in the central nervous system. Purkinje cells are among the largest neurons in the central nervous system and accordingly can harbor two nuclei.Using a well-characterized model for heterokaryon formation in the cerebellum (experimental autoimmune encephalomyelitis - a mouse model of multiple sclerosis), we report for the first time that green fluorescent protein-labeled bone marrow-derived cells can fuse and form heterokaryons with spinal cord motor neurons. These spinal cord heterokaryons are predominantly located in or adjacent to an active or previously active inflammation site, demonstrating that inflammation and infiltration of immune cells are key for cell fusion in the central nervous system. While some motor neurons were found to contain two nuclei, co-expressing green fluorescent protein and the neuronal marker, neuron-specific nuclear protein, a number of small interneurons also co-expressed green fluorescent protein and the neuronal marker, neuron-specific nuclear protein. These small heterokaryons were scattered in the gray matter of the spinal cord.This novel finding expands the repertoire of neurons that can form heterokaryons with bone marrow-derived cells in the central nervous system, albeit in low numbers, possibly leading to a novel therapy for spinal cord motor neurons or other neurons that are compromised in the central nervous system.SankavaramSreenivasa RSRCenter for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.SvenssonMikael AMADepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.OlssonTomasTCenter for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.BrundinLouLCenter for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.JohanssonClas BCBCenter for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Public Dental Service at Gällö, Jämtland Härjedalen County Council, Gällö, Sweden.engJournal ArticleResearch Support, Non-U.S. Gov't20150724
United StatesPLoS One1012850811932-62030DNA-Binding Proteins0Nerve Tissue Proteins0NeuN protein, mouse0Nuclear ProteinsIMAnimalsBrainpathologyCentral Nervous SystempathologyCerebellummetabolismpathologyDNA-Binding ProteinsDisease Models, AnimalEncephalomyelitis, Autoimmune, ExperimentalpathologyFemaleGene ExpressionGenes, ReporterGiant CellspathologyInterneuronsmetabolismpathologyMiceMotor NeuronsmetabolismpathologyMultiple SclerosispathologyNerve Tissue ProteinsmetabolismNuclear ProteinsmetabolismSpinal CordmetabolismpathologyCompeting Interests: The authors have declared that no competing interests exist.
20141231201572201572560201572560201656602015724epublish26207625PMC451479110.1371/journal.pone.0133903PONE-D-14-58576Kang H, Kerloc'h A, Rotival M, Xu X, Zhang Q, et al. (2014) Kcnn4 is a regulator of macrophage multinucleation in bone homeostasis and inflammatory disease. Cell Rep 8: 1210–1224. 10.1016/j.celrep.2014.07.03210.1016/j.celrep.2014.07.032PMC447181325131209Kaji K, Kudo A (2004) The mechanism of sperm-oocyte fusion in mammals. Reproduction 127: 423–429.15047933Huppertz B, Bartz C, Kokozidou M (2006) Trophoblast fusion: fusogenic proteins, syncytins and ADAMs, and other prerequisites for syncytial fusion. Micron 37: 509–517.16497505Pajcini KV, Pomerantz JH, Alkan O, Doyonnas R, Blau HM (2008) Myoblasts and macrophages share molecular components that contribute to cell-cell fusion. J Cell Biol 180: 1005–1019. 10.1083/jcb.20070719110.1083/jcb.200707191PMC226540818332221Alvarez-Dolado M, Pardal R, Garcia-Verdugo JM, Fike JR, Lee HO, et al. (2003) Fusion of bone-marrow-derived cells with Purkinje neurons, cardiomyocytes and hepatocytes. Nature 425: 968–973.14555960Camargo FD, Green R, Capetanaki Y, Jackson KA, Goodell MA (2003) Single hematopoietic stem cells generate skeletal muscle through myeloid intermediates. Nat Med 9: 1520–1527.14625546Corbel SY, Lee A, Yi L, Duenas J, Brazelton TR, et al. (2003) Contribution of hematopoietic stem cells to skeletal muscle. Nat Med 9: 1528–1532.14625543Ferrari G, Cusella-De Angelis G, Coletta M, Paolucci E, Stornaiuolo A, et al. (1998) Muscle regeneration by bone marrow-derived myogenic progenitors. Science 279: 1528–1530.9488650Fukada S, Miyagoe-Suzuki Y, Tsukihara H, Yuasa K, Higuchi S, et al. (2002) Muscle regeneration by reconstitution with bone marrow or fetal liver cells from green fluorescent protein-gene transgenic mice. J Cell Sci 115: 1285–1293.11884527LaBarge MA, Blau HM (2002) Biological progression from adult bone marrow to mononucleate muscle stem cell to multinucleate muscle fiber in response to injury. Cell 111: 589–601.12437931Lagasse E, Connors H, Al-Dhalimy M, Reitsma M, Dohse M, et al. (2000) Purified hematopoietic stem cells can differentiate into hepatocytes in vivo. Nat Med 6: 1229–1234.11062533Rizvi AZ, Swain JR, Davies PS, Bailey AS, Decker AD, et al. (2006) Bone marrow-derived cells fuse with normal and transformed intestinal stem cells. Proc Natl Acad Sci U S A 103: 6321–6325.PMC143536516606845Weimann JM, Charlton CA, Brazelton TR, Hackman RC, Blau HM (2003) Contribution of transplanted bone marrow cells to Purkinje neurons in human adult brains. Proc Natl Acad Sci U S A 100: 2088–2093.PMC14996312576546Weimann JM, Johansson CB, Trejo A, Blau HM (2003) Stable reprogrammed heterokaryons form spontaneously in Purkinje neurons after bone marrow transplant. Nat Cell Biol 5: 959–966.14562057Johansson CB, Youssef S, Koleckar K, Holbrook C, Doyonnas R, et al. (2008) Extensive fusion of haematopoietic cells with Purkinje neurons in response to chronic inflammation. Nat Cell Biol 10: 575–583. 10.1038/ncb172010.1038/ncb1720PMC423043718425116Magrassi L, Grimaldi P, Ibatici A, Corselli M, Ciardelli L, et al. (2007) Induction and survival of binucleated Purkinje neurons by selective damage and aging. J Neurosci 27: 9885–9892.PMC667263917855603Nern C, Wolff I, Macas J, von Randow J, Scharenberg C, et al. (2009) Fusion of hematopoietic cells with Purkinje neurons does not lead to stable heterokaryon formation under noninvasive conditions. J Neurosci 29: 3799–3807. 10.1523/JNEUROSCI.5848-08.200910.1523/JNEUROSCI.5848-08.2009PMC666502719321776Nygren JM, Liuba K, Breitbach M, Stott S, Thoren L, et al. (2008) Myeloid and lymphoid contribution to non-haematopoietic lineages through irradiation-induced heterotypic cell fusion. Nat Cell Biol 10: 584–592. 10.1038/ncb172110.1038/ncb172118425115Kemp K, Gray E, Wilkins A, Scolding N (2012) Purkinje cell fusion and binucleate heterokaryon formation in multiple sclerosis cerebellum. Brain 135: 2962–2972. 10.1093/brain/aws22610.1093/brain/aws22622975392Sullivan S, Eggan K (2006) The potential of cell fusion for human therapy. Stem Cell Rev 2: 341–349.17848721Willenbring H (2005) Therapeutic cell fusion. Br J Surg 92: 923–924.16034848Gibson AJ, Karasinski J, Relvas J, Moss J, Sherratt TG, et al. (1995) Dermal fibroblasts convert to a myogenic lineage in mdx mouse muscle. Journal of cell science 108 (Pt 1): 207–214.7738097Vassilopoulos G, Wang PR, Russell DW (2003) Transplanted bone marrow regenerates liver by cell fusion. Nature 422: 901–904.12665833Bae JS, Furuya S, Shinoda Y, Endo S, Schuchman EH, et al. (2005) Neurodegeneration augments the ability of bone marrow-derived mesenchymal stem cells to fuse with Purkinje neurons in Niemann-Pick type C mice. Hum Gene Ther 16: 1006–1011.16076258Bae JS, Han HS, Youn DH, Carter JE, Modo M, et al. (2007) Bone marrow-derived mesenchymal stem cells promote neuronal networks with functional synaptic transmission after transplantation into mice with neurodegeneration. Stem Cells 25: 1307–1316.17470534Johansson CB, Momma S, Clarke DL, Risling M, Lendahl U, et al. (1999) Identification of a neural stem cell in the adult mammalian central nervous system. Cell 96: 25–34.9989494Youssef S, Stuve O, Patarroyo JC, Ruiz PJ, Radosevich JL, et al. (2002) The HMG-CoA reductase inhibitor, atorvastatin, promotes a Th2 bias and reverses paralysis in central nervous system autoimmune disease. Nature 420: 78–84.12422218Andersen BB, Gundersen HJ, Pakkenberg B (2003) Aging of the human cerebellum: a stereological study. J Comp Neurol 466: 356–365.14556293Sanges D, Romo N, Simonte G, Di Vicino U, Tahoces AD, et al. (2013) Wnt/beta-catenin signaling triggers neuron reprogramming and regeneration in the mouse retina. Cell Rep 4: 271–286. 10.1016/j.celrep.2013.06.01510.1016/j.celrep.2013.06.01523850287Ridder K, Keller S, Dams M, Rupp AK, Schlaudraff J, et al. (2014) Extracellular vesicle-mediated transfer of genetic information between the hematopoietic system and the brain in response to inflammation. PLoS Biol 12: e1001874 10.1371/journal.pbio.100187410.1371/journal.pbio.1001874PMC404348524893313
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1559-064X2552015Sep-OctJournal of exposure science & environmental epidemiologyJ Expo Sci Environ EpidemiolPotential health impacts of changes in air pollution exposure associated with moving traffic into a road tunnel.524531524-3110.1038/jes.2015.24A planned 21 km bypass (18 km within a tunnel) in Stockholm is expected to reduce ambient air exposure to traffic emissions, but same time tunnel users could be exposed to high concentrations of pollutants. For the health impacts calculations in 2030, the change in annual ambient NOX and PM10 exposure of the general population was modelled in 100 × 100 m(2) grids for Greater Stockholm area. The tunnel exposure was estimated based on calculated annual average NOX concentrations, time spent in tunnel and number of tunnel users. For the general population, we estimate annually 23.7 (95% CI: 17.7-32.3) fewer premature deaths as ambient concentrations are reduced. At the same time, tunnel users will be exposed to NOX levels up to 2000 μg/m(-3). Passing through the whole tunnel two times on working days would correspond to an additional annual NOX exposure of 9.6 μg/m(3). Assuming that there will be ~55,000 vehicles daily each way and 1.3 persons of 30-74 years of age in each vehicle, we estimate the tunnel exposure to result in 20.6 (95% CI: 14.1-25.6) premature deaths annually. If there were more persons per vehicle, or older and vulnerable people travelling, or tunnel dispersion conditions worsen, the adverse effect would become larger.OrruHansH1] Department of Public Health and Clinical Medicine, Umea University, Umea, Sweden [2] Department of Public Health, University of Tartu, Tartu, Estonia.LövenheimBoelBStockholm Environment and Health Administration, Stockholm, Sweden.JohanssonChristerC1] Stockholm Environment and Health Administration, Stockholm, Sweden [2] Department of Applied Environmental Science and Analytical Chemistry, Stockholm University, Stockholm, Sweden.ForsbergBertilBDepartment of Public Health and Clinical Medicine, Umea University, Umea, Sweden.engJournal ArticleResearch Support, Non-U.S. Gov't20150429
United StatesJ Expo Sci Environ Epidemiol1012627961559-06310Air Pollutants0Nitrogen Oxides0Particulate Matter0Vehicle EmissionsIMAir Pollutantsadverse effectsanalysisAir Pollutionadverse effectsanalysisEnvironmental MonitoringmethodsHealth StatusHumansModels, TheoreticalMortalityNitrogen Oxidesadverse effectsanalysisParticle SizeParticulate Matteradverse effectsanalysisRisk AssessmentmethodsSwedenUrban PopulationVehicle Emissionsanalysis
2014432014121820141227201543060201543060201662160ppublish2592108010.1038/jes.2015.24jes201524JAMA. 2002 Mar 6;287(9):1132-4111879110Lancet. 2002 Oct 19;360(9341):1203-912401246Eur Respir J. 2000 Apr;15(4):716-2410780764Atmos Environ (1994). 2012 Nov 1;59:578-58623888122Epidemiology. 2006 Sep;17(5):545-5116755270JAMA. 1998 Nov 18;280(19):1690-19832001Int J Epidemiol. 1999 Aug;28(4):640-410480690Environ Int. 2010 Jan;36(1):36-4519878999Allergy. 2010 Jan;65(1):48-5519796226Lancet. 2000 Sep 2;356(9232):795-80111022926Environ Health Perspect. 2007 Apr;115(4):507-1217450216Am J Respir Crit Care Med. 2011 Jan 1;183(1):73-820656944Sci Total Environ. 2004 Apr 5;321(1-3):71-8515050386Lancet. 2009 Dec 19;374(9707):2091-10319942276Int Arch Occup Environ Health. 2007 Nov;81(2):159-6417492462Part Fibre Toxicol. 2010 Oct 07;7:2920929559Swiss Med Wkly. 2012 May 31;142:w1359722653425Occup Environ Med. 1998 Feb;55(2):115-89614396Environ Health Perspect. 2010 Sep;118(9):1189-9520382579Environ Health Perspect. 2011 Oct;119(10):1373-821672679Environ Health. 2010 Jun 15;9:2620550697Environ Health Perspect. 2009 May;117(5):772-719479020Sci Total Environ. 2013 Apr 1;449:390-40023454700Occup Environ Med. 2005 Jul;62(7):453-6015961621Eur Respir J. 2005 Aug;26(2):309-1816055881J Expo Sci Environ Epidemiol. 2013 Sep-Oct;23(5):506-1223321863Environ Health. 2009 Mar 03;8:719257892J Occup Environ Med. 2010 Mar;52(3):324-3120190650Epidemiology. 2007 Jan;18(1):95-10317149139Int J Occup Med Environ Health. 1998;11(1):37-579637994Int J Occup Environ Health. 2001 Jan-Mar;7(1):23-3011210009Environ Health Perspect. 2012 Mar;120(3):367-7222389220Epidemiology. 2010 Nov;21(6):892-90220811287Am J Respir Crit Care Med. 2009 Apr 1;179(7):579-8719151198N Engl J Med. 2007 Dec 6;357(23):2348-5818057337Inhal Toxicol. 2008 Apr;20(6):533-4518444007Environ Health Perspect. 2004 Apr;112(5):610-515064169Eur Respir J. 2009 Jun;33(6):1261-719251785Epidemiology. 2005 Nov;16(6):727-3616222161Eur J Epidemiol. 2006;21(6):449-5816826453Occup Environ Med. 2013 Mar;70(3):179-8623220504Environ Monit Assess. 2007 Apr;127(1-3):477-8716983585Inhal Toxicol. 2007 Feb;19(2):133-4017169860Environ Health Perspect. 2012 Mar;120(3):431-622182596Environ Health Perspect. 2009 Jul;117(7):1089-9419654918Eur Respir J. 2007 Apr;29(4):699-70517251238Respir Med. 2010 Dec;104(12):1912-820621461
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1552-86183492015SepEnvironmental toxicology and chemistryEnviron Toxicol ChemLong-term effects of the antibacterial agent triclosan on marine periphyton communities.206720772067-7710.1002/etc.3030Triclosan is a widely used antibacterial agent that has become a ubiquitous contaminant in freshwater, estuary, and marine environments. Concerns about potential adverse effects of triclosan have been described in several recent risk assessments. Its effects on freshwater microbial communities have been well studied, but studies addressing effects on marine microbial communities are scarce. In the present study, the authors describe short- and long-term effects of triclosan on marine periphyton (microbial biofilm) communities. Short-term effects on photosynthesis were estimated after 60 min to 210 min of exposure. Long-term effects on photosynthesis, chlorophyll a fluorescence, pigment content, community tolerance, and bacterial carbon utilization were studied after exposing periphyton for 17 d in flow-through microcosms to 0.316 nM to 10,000 nM triclosan. Results from the short-term studies show that triclosan is toxic to periphyton photosynthesis. Half maximal effective concentration (EC50) values of 1080 nM and 3000 nM were estimated using (14)CO2-incorporation and pulse amplitude modulation (PAM) fluorescence measurements, respectively. After long-term triclosan exposure in flow-through microcosms, photosynthesis estimated using PAM fluorometry was not inhibited by triclosan concentrations up to 1000 nM but instead increased with increasing triclosan concentration. Similarly, at exposure concentrations of 31.6 nM and higher, triclosan caused an increase in photosynthetic pigments. At 316 nM triclosan, the pigment amounts were increased by a factor of 1.4 to 1.9 compared with the control level. Pollution-induced community tolerance was observed for algae and cyanobacteria at 100 nM triclosan and higher. Despite the widespread use of triclosan as an antibacterial agent, the compound did not have any effects on bacterial carbon utilization after long-term exposure.© 2015 SETAC.ErikssonK MartinKMDepartment of Shipping and Marine Technology, Chalmers University of Technology, Gothenburg, Sweden.JohanssonC HenrikCHDepartment of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.FihlmanViktorVDepartment of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.GrehnAlexanderADepartment of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.SanliKemalKDepartment of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.AnderssonMats XMXDepartment of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.BlanckHansHDepartment of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.ArrheniusÅsaÅDepartment of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.SircarTrirantaTDepartment of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.BackhausThomasTDepartment of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.engJournal ArticleResearch Support, Non-U.S. Gov't20150624
United StatesEnviron Toxicol Chem83089580730-72680Anti-Bacterial Agents0Carbon Radioisotopes0Water Pollutants, Chemical1406-65-1Chlorophyll142M471B3JCarbon Dioxide4NM5039Y5XTriclosanYF5Q9EJC8YChlorophyll AIMAnti-Bacterial AgentschemistrytoxicityBiofilmsdrug effectsCarbon DioxidemetabolismCarbon RadioisotopeschemistryChlorophyllmetabolismChlorophyll AChlorophytadrug effectsmetabolismChromatography, High Pressure LiquidCyanobacteriadrug effectsphysiologyDrug ResistanceFluorometryPhotosynthesisdrug effectsTime FactorsTriclosanchemistrytoxicityWater Pollutants, ChemicalchemistrytoxicityBiofilmIrgasanMicrobial toxicologyMode of actionPersonal care productsPollution-induced community tolerance (PICT)
2014812014925201541920154246020154246020164560ppublish2590416410.1002/etc.3030
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1096-03414822015AugVirologyVirologyAcetylation of intragenic histones on HPV16 correlates with enhanced HPV16 gene expression.244259244-5910.1016/j.virol.2015.02.053S0042-6822(15)00187-7We report that many histone modifications are unevenly distributed over the HPV16 genome in cervical cancer cells as well as in HPV16-immortalized keratinocytes. For example, H3K36me3 and H3K9Ac that are common in highly expressed cellular genes and over exons, were more common in the early than in the late region of the HPV16 genome. In contrast, H3K9me3, H4K20me3, H2BK5me1 and H4K16Ac were more frequent in the HPV16 late region. Furthermore, a region encompassing the HPV16 early polyadenylation signal pAE displayed high levels of histone H3 acetylation. Histone deacetylase (HDAC) inhibitors caused a 2- to 8-fold induction of HPV16 early and late mRNAs in cervical cancer cells and in immortalized keratinocytes, while at the same time increasing the levels of acetylated histones in the cells and on the HPV16 genome specifically. We concluded that increased histone acetylation on the HPV16 genome correlates with increased HPV16 gene expression.Copyright © 2015 Elsevier Inc. All rights reserved.JohanssonCeciliaCDepartment of Laboratory Medicine, Lund University, 221 84 Lund, Sweden.Jamal FattahTavanTDepartment of Laboratory Medicine, Lund University, 221 84 Lund, Sweden.YuHaoranHDepartment of Laboratory Medicine, Lund University, 221 84 Lund, Sweden.NygrenJakobJDepartment of Laboratory Medicine, Lund University, 221 84 Lund, Sweden.MossbergAnn-KristinAKDepartment of Laboratory Medicine, Lund University, 221 84 Lund, Sweden.SchwartzStefanSDepartment of Laboratory Medicine, Lund University, 221 84 Lund, Sweden. Electronic address: Stefan.Schwartz@med.lu.se.engJournal ArticleResearch Support, Non-U.S. Gov't20150417
United StatesVirology01106740042-68220HistonesIMAcetylationGene Expression Regulation, ViralHistonesmetabolismHost-Pathogen InteractionsHuman papillomavirus 16geneticsHumansProtein Processing, Post-TranslationalEpigeneticsH2BK5me1H3K36me3H3K9AcH3K9me3H4K20me3HDAC inhibitorHPV16Histone marksHnrnpPanobinostatPapillomavirusPolyadenylationQuisinostatSR proteinsSplicing
2014121020151232015225201542360201542360201582560ppublish2590088610.1016/j.virol.2015.02.053S0042-6822(15)00187-7
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1540-953821252015May04The Journal of experimental medicineJ Exp MedAlveolar macrophage-derived type I interferons orchestrate innate immunity to RSV through recruitment of antiviral monocytes.699714699-71410.1084/jem.20140825Type I interferons (IFNs) are important for host defense from viral infections, acting to restrict viral production in infected cells and to promote antiviral immune responses. However, the type I IFN system has also been associated with severe lung inflammatory disease in response to respiratory syncytial virus (RSV). Which cells produce type I IFNs upon RSV infection and how this directs immune responses to the virus, and potentially results in pathological inflammation, is unclear. Here, we show that alveolar macrophages (AMs) are the major source of type I IFNs upon RSV infection in mice. AMs detect RSV via mitochondrial antiviral signaling protein (MAVS)-coupled retinoic acid-inducible gene 1 (RIG-I)-like receptors (RLRs), and loss of MAVS greatly compromises innate immune restriction of RSV. This is largely attributable to loss of type I IFN-dependent induction of monocyte chemoattractants and subsequent reduced recruitment of inflammatory monocytes (infMo) to the lungs. Notably, the latter have potent antiviral activity and are essential to control infection and lessen disease severity. Thus, infMo recruitment constitutes an important and hitherto underappreciated, cell-extrinsic mechanism of type I IFN-mediated antiviral activity. Dysregulation of this system of host antiviral defense may underlie the development of RSV-induced severe lung inflammation.© 2015 Goritzka et al.GoritzkaMichelleMCentre for Respiratory Infection, Respiratory Infections Section, National Heart and Lung Institute, Imperial College London, London W2 1PG, England, UK.MakrisSpyridonSCentre for Respiratory Infection, Respiratory Infections Section, National Heart and Lung Institute, Imperial College London, London W2 1PG, England, UK.KausarFahimaFCentre for Respiratory Infection, Respiratory Infections Section, National Heart and Lung Institute, Imperial College London, London W2 1PG, England, UK.DurantLydia RLRCentre for Respiratory Infection, Respiratory Infections Section, National Heart and Lung Institute, Imperial College London, London W2 1PG, England, UK.PereiraCatherineCCentre for Respiratory Infection, Respiratory Infections Section, National Heart and Lung Institute, Imperial College London, London W2 1PG, England, UK.KumagaiYutaroYLaboratory of Host Defense, World Premier International Immunology Frontier Research Center, Osaka University, Suita, Osaka 565-0871, Japan.CulleyFiona JFJCentre for Respiratory Infection, Respiratory Infections Section, National Heart and Lung Institute, Imperial College London, London W2 1PG, England, UK.MackMatthiasMUniversity Hospital Regensburg, 93042 Regensburg, Germany.AkiraShizuoSLaboratory of Host Defense, World Premier International Immunology Frontier Research Center, Osaka University, Suita, Osaka 565-0871, Japan.JohanssonCeciliaCCentre for Respiratory Infection, Respiratory Infections Section, National Heart and Lung Institute, Imperial College London, London W2 1PG, England, UK c.johansson@imperial.ac.uk.engG0800311Medical Research CouncilUnited KingdomJournal ArticleResearch Support, Non-U.S. Gov't20150420
United StatesJ Exp Med2985109R0022-10070Adaptor Proteins, Signal Transducing0IPS-1 protein, mouse0Interferon Type I0Membrane Proteins0Nerve Tissue Proteins0Receptors, Cell Surface0Robo3 protein, mouseIMAdaptor Proteins, Signal TransducinggeneticsimmunologyAnimalsImmunity, InnateInterferon Type IgeneticsimmunologyMacrophages, AlveolarimmunologypathologyMembrane ProteinsgeneticsimmunologyMiceMice, KnockoutMonocytesimmunologypathologyNerve Tissue ProteinsgeneticsimmunologyPneumonia, ViralgeneticsimmunologypathologyReceptors, Cell SurfaceRespiratory Syncytial Virus InfectionsgeneticsimmunologypathologyRespiratory Syncytial Virusesimmunology
201443020153242015422602015422602015715602015114ppublish25897172PMC441933910.1084/jem.20140825jem.20140825Asselin-Paturel C., Boonstra A., Dalod M., Durand I., Yessaad N., Dezutter-Dambuyant C., Vicari A., O’Garra A., Biron C., Brière F., and Trinchieri G.. 2001. Mouse type I IFN-producing cells are immature APCs with plasmacytoid morphology. Nat. Immunol. 2:1144–1150 10.1038/ni73610.1038/ni73611713464Awomoyi A.A., Rallabhandi P., Pollin T.I., Lorenz E., Sztein M.B., Boukhvalova M.S., Hemming V.G., Blanco J.C.G., and Vogel S.N.. 2007. Association of TLR4 polymorphisms with symptomatic respiratory syncytial virus infection in high-risk infants and young children. J. Immunol. 179:3171–3177 10.4049/jimmunol.179.5.317110.4049/jimmunol.179.5.317117709532Bartlett N.W., Slater L., Glanville N., Haas J.J., Caramori G., Casolari P., Clarke D.L., Message S.D., Aniscenko J., Kebadze T., et al. . 2012. Defining critical roles for NF-κB p65 and type I interferon in innate immunity to rhinovirus. EMBO Mol. Med. 4:1244–1260 10.1002/emmm.20120165010.1002/emmm.201201650PMC353160123165884Behera A.K., Kumar M., Lockey R.F., and Mohapatra S.S.. 2002. 2′-5′ Oligoadenylate synthetase plays a critical role in interferon-γ inhibition of respiratory syncytial virus infection of human epithelial cells. J. Biol. Chem. 277:25601–25608 10.1074/jbc.M20021120010.1074/jbc.M20021120011980899Bhoj V.G., Sun Q., Bhoj E.J., Somers C., Chen X., Torres J.P., Mejias A., Gomez A.M., Jafri H., Ramilo O., and Chen Z.J.. 2008. MAVS and MyD88 are essential for innate immunity but not cytotoxic T lymphocyte response against respiratory syncytial virus. Proc. Natl. Acad. Sci. USA. 105:14046–14051 10.1073/pnas.080471710510.1073/pnas.0804717105PMC253297418780793Borchers A.T., Chang C., Gershwin M.E., and Gershwin L.J.. 2013. Respiratory syncytial virus—a comprehensive review. Clin. Rev. Allergy Immunol. 45:331–379 10.1007/s12016-013-8368-910.1007/s12016-013-8368-9PMC709064323575961Boyapalle S., Wong T., Garay J., Teng M., San Juan-Vergara H., Mohapatra S., and Mohapatra S.. 2012. Respiratory syncytial virus NS1 protein colocalizes with mitochondrial antiviral signaling protein MAVS following infection. PLoS ONE. 7:e29386 10.1371/journal.pone.002938610.1371/journal.pone.0029386PMC328800522383950Conrady C.D., Zheng M., Mandal N.A., van Rooijen N., and Carr D.J.J.. 2013. IFN-α-driven CCL2 production recruits inflammatory monocytes to infection site in mice. Mucosal Immunol. 6:45–55 10.1038/mi.2012.4610.1038/mi.2012.46PMC344902622692455Crane M.J., Hokeness-Antonelli K.L., and Salazar-Mather T.P.. 2009. Regulation of inflammatory monocyte/macrophage recruitment from the bone marrow during murine cytomegalovirus infection: role for type I interferons in localized induction of CCR2 ligands. J. Immunol. 183:2810–2817 10.4049/jimmunol.090020510.4049/jimmunol.0900205PMC291102319620305Culley F.J., Pollott J., and Openshaw P.J.M.. 2002. Age at first viral infection determines the pattern of T cell–mediated disease during reinfection in adulthood. J. Exp. Med. 196:1381–1386 10.1084/jem.2002094310.1084/jem.20020943PMC219399112438429Culley F.J., Pennycook A.M.J., Tregoning J.S., Hussell T., and Openshaw P.J.M.. 2006. Differential chemokine expression following respiratory virus infection reflects Th1- or Th2-biased immunopathology. J. Virol. 80:4521–4527 10.1128/JVI.80.9.4521-4527.200610.1128/JVI.80.9.4521-4527.2006PMC147201216611912Davidson S., Crotta S., McCabe T.M., and Wack A.. 2014. Pathogenic potential of interferon αβ in acute influenza infection. Nat. Commun. 5:3864 10.1038/ncomms486410.1038/ncomms4864PMC403379224844667Demoor T., Petersen B.C., Morris S., Mukherjee S., Ptaschinski C., De Almeida Nagata D.E., Kawai T., Ito T., Akira S., Kunkel S.L., et al. . 2012. IPS-1 signaling has a nonredundant role in mediating antiviral responses and the clearance of respiratory syncytial virus. J. Immunol. 189:5942–5953 10.4049/jimmunol.120176310.4049/jimmunol.1201763PMC388896523136205Deshmane S.L., Kremlev S., Amini S., and Sawaya B.E.. 2009. Monocyte chemoattractant protein-1 (MCP-1): an overview. J. Interferon Cytokine Res. 29:313–326 10.1089/jir.2008.002710.1089/jir.2008.0027PMC275509119441883Durant L.R., Pereira C., Boakye A., Makris S., Kausar F., Goritzka M., and Johansson C.. 2014. DNGR-1 is dispensable for CD8+ T-cell priming during respiratory syncytial virus infection. Eur. J. Immunol. 44:2340–2348 10.1002/eji.20144445410.1002/eji.20144445424777856El Saleeby C.M., and Devincenzo J.P.. 2011. Respiratory syncytial virus load and disease severity in the community. J. Med. Virol. 83:904–905 10.1002/jmv.2203910.1002/jmv.2203921412798Everitt A.R., Clare S., McDonald J.U., Kane L., Harcourt K., Ahras M., Lall A., Hale C., Rodgers A., Young D.B., et al. . 2013. Defining the range of pathogens susceptible to Ifitm3 restriction using a knockout mouse model. PLoS ONE. 8:e80723 10.1371/journal.pone.008072310.1371/journal.pone.0080723PMC383675624278312Garofalo R.P., Patti J., Hintz K.A., Hill V., Ogra P.L., and Welliver R.C.. 2001. Macrophage inflammatory protein-1alpha (not T helper type 2 cytokines) is associated with severe forms of respiratory syncytial virus bronchiolitis. J. Infect. Dis. 184:393–399 10.1086/32278810.1086/32278811471095Goritzka M., Durant L.R., Pereira C., Salek-Ardakani S., Openshaw P.J.M., and Johansson C.. 2014. Alpha/beta interferon receptor signaling amplifies early proinflammatory cytokine production in the lung during respiratory syncytial virus infection. J. Virol. 88:6128–6136 10.1128/JVI.00333-1410.1128/JVI.00333-14PMC409389724648449Goubau D., Deddouche S., and Reis e Sousa C.. 2013. Cytosolic sensing of viruses. Immunity. 38:855–869 10.1016/j.immuni.2013.05.00710.1016/j.immuni.2013.05.007PMC711111323706667Grainger J.R., Wohlfert E.A., Fuss I.J., Bouladoux N., Askenase M.H., Legrand F., Koo L.Y., Brenchley J.M., Fraser I.D.C., and Belkaid Y.. 2013. Inflammatory monocytes regulate pathologic responses to commensals during acute gastrointestinal infection. Nat. Med. 19:713–721 10.1038/nm.318910.1038/nm.3189PMC375547823708291Haeberle H.A., Kuziel W.A., Dieterich H.J., Casola A., Gatalica Z., and Garofalo R.P.. 2001. Inducible expression of inflammatory chemokines in respiratory syncytial virus-infected mice: role of MIP-1α in lung pathology. J. Virol. 75:878–890 10.1128/JVI.75.2.878-890.200110.1128/JVI.75.2.878-890.2001PMC11398411134301Helft J., Manicassamy B., Guermonprez P., Hashimoto D., Silvin A., Agudo J., Brown B.D., Schmolke M., Miller J.C., Leboeuf M., et al. . 2012. Cross-presenting CD103+ dendritic cells are protected from influenza virus infection. J. Clin. Invest. 122:4037–4047 10.1172/JCI6065910.1172/JCI60659PMC348443323041628Herold S., Steinmueller M., von Wulffen W., Cakarova L., Pinto R., Pleschka S., Mack M., Kuziel W.A., Corazza N., Brunner T., et al. . 2008. Lung epithelial apoptosis in influenza virus pneumonia: the role of macrophage-expressed TNF-related apoptosis-inducing ligand. J. Exp. Med. 205:3065–3077 10.1084/jem.2008020110.1084/jem.20080201PMC260523119064696Hussell T., and Bell T.J.. 2014. Alveolar macrophages: plasticity in a tissue-specific context. Nat. Rev. Immunol. 14:81–93 10.1038/nri360010.1038/nri360024445666Iijima N., Mattei L.M., and Iwasaki A.. 2011. Recruited inflammatory monocytes stimulate antiviral Th1 immunity in infected tissue. Proc. Natl. Acad. Sci. USA. 108:284–289 10.1073/pnas.100520110810.1073/pnas.1005201108PMC301717721173243Janssen R., Bont L., Siezen C.L.E., Hodemaekers H.M., Ermers M.J., Doornbos G., van ’t Slot R., Wijmenga C., Goeman J.J., Kimpen J.L., et al. . 2007. Genetic susceptibility to respiratory syncytial virus bronchiolitis is predominantly associated with innate immune genes. J. Infect. Dis. 196:826–834 10.1086/52088610.1086/52088617703412Jewell N.A., Vaghefi N., Mertz S.E., Akter P., Peebles R.S. Jr, Bakaletz L.O., Durbin R.K., Flaño E., and Durbin J.E.. 2007. Differential type I interferon induction by respiratory syncytial virus and influenza a virus in vivo. J. Virol. 81:9790–9800 10.1128/JVI.00530-0710.1128/JVI.00530-07PMC204539417626092Kumagai Y., Takeuchi O., Kato H., Kumar H., Matsui K., Morii E., Aozasa K., Kawai T., and Akira S.. 2007. Alveolar macrophages are the primary interferon-alpha producer in pulmonary infection with RNA viruses. Immunity. 27:240–252 10.1016/j.immuni.2007.07.01310.1016/j.immuni.2007.07.01317723216Lee D.C.P., Harker J.A.E., Tregoning J.S., Atabani S.F., Johansson C., Schwarze J., and Openshaw P.J.M.. 2010. CD25+ natural regulatory T cells are critical in limiting innate and adaptive immunity and resolving disease following respiratory syncytial virus infection. J. Virol. 84:8790–8798 10.1128/JVI.00796-1010.1128/JVI.00796-10PMC291903020573822Lin K.L., Suzuki Y., Nakano H., Ramsburg E., and Gunn M.D.. 2008. CCR2+ monocyte-derived dendritic cells and exudate macrophages produce influenza-induced pulmonary immune pathology and mortality. J. Immunol. 180:2562–2572 10.4049/jimmunol.180.4.256210.4049/jimmunol.180.4.256218250467Ling Z., Tran K.C., and Teng M.N.. 2009. Human respiratory syncytial virus nonstructural protein NS2 antagonizes the activation of beta interferon transcription by interacting with RIG-I. J. Virol. 83:3734–3742 10.1128/JVI.02434-0810.1128/JVI.02434-08PMC266325119193793Liu P., Jamaluddin M., Li K., Garofalo R.P., Casola A., and Brasier A.R.. 2007. Retinoic acid-inducible gene I mediates early antiviral response and Toll-like receptor 3 expression in respiratory syncytial virus-infected airway epithelial cells. J. Virol. 81:1401–1411 10.1128/JVI.01740-0610.1128/JVI.01740-06PMC179749417108032Loebbermann J., Thornton H., Durant L., Sparwasser T., Webster K.E., Sprent J., Culley F.J., Johansson C., and Openshaw P.J.. 2012. Regulatory T cells expressing granzyme B play a critical role in controlling lung inflammation during acute viral infection. Mucosal Immunol. 5:161–172 10.1038/mi.2011.6210.1038/mi.2011.62PMC328243422236998Loo Y.-M., Fornek J., Crochet N., Bajwa G., Perwitasari O., Martinez-Sobrido L., Akira S., Gill M.A., García-Sastre A., Katze M.G., and Gale M. Jr. 2008. Distinct RIG-I and MDA5 signaling by RNA viruses in innate immunity. J. Virol. 82:335–345 10.1128/JVI.01080-0710.1128/JVI.01080-07PMC222440417942531Mack M., Cihak J., Simonis C., Luckow B., Proudfoot A.E., Plachý J., Brühl H., Frink M., Anders H.J., Vielhauer V., et al. . 2001. Expression and characterization of the chemokine receptors CCR2 and CCR5 in mice. J. Immunol. 166:4697–4704 10.4049/jimmunol.166.7.469710.4049/jimmunol.166.7.469711254730Majer O., Bourgeois C., Zwolanek F., Lassnig C., Kerjaschki D., Mack M., Müller M., and Kuchler K.. 2012. Type I interferons promote fatal immunopathology by regulating inflammatory monocytes and neutrophils during Candida infections. PLoS Pathog. 8:e1002811 10.1371/journal.ppat.100281110.1371/journal.ppat.1002811PMC340609522911155Marr N., Turvey S.E., and Grandvaux N.. 2013. Pathogen recognition receptor crosstalk in respiratory syncytial virus sensing: a host and cell type perspective. Trends Microbiol. 21:568–574 10.1016/j.tim.2013.08.00610.1016/j.tim.2013.08.006PMC484803224119913McNamara P.S., Flanagan B.F., Hart C.A., and Smyth R.L.. 2005. Production of chemokines in the lungs of infants with severe respiratory syncytial virus bronchiolitis. J. Infect. Dis. 191:1225–1232 10.1086/42885510.1086/42885515776367Merolla R., Rebert N.A., Tsiviste P.T., Hoffmann S.P., and Panuska J.R.. 1995. Respiratory syncytial virus replication in human lung epithelial cells: inhibition by tumor necrosis factor alpha and interferon beta. Am. J. Respir. Crit. Care Med. 152:1358–1366 10.1164/ajrccm.152.4.755139510.1164/ajrccm.152.4.75513957551395Messier E.M., Mason R.J., and Kosmider B.. 2012. Efficient and rapid isolation and purification of mouse alveolar type II epithelial cells. Exp. Lung Res. 38:363–373 10.3109/01902148.2012.71307710.3109/01902148.2012.71307722888851Miller A.L., Bowlin T.L., and Lukacs N.W.. 2004. Respiratory syncytial virus-induced chemokine production: linking viral replication to chemokine production in vitro and in vivo. J. Infect. Dis. 189:1419–1430 10.1086/38295810.1086/38295815073679Narasaraju T., Ng H.H., Phoon M.C., and Chow V.T.K.. 2010. MCP-1 antibody treatment enhances damage and impedes repair of the alveolar epithelium in influenza pneumonitis. Am. J. Respir. Cell Mol. Biol. 42:732–743 10.1165/rcmb.2008-0423OC10.1165/rcmb.2008-0423OCPMC289149919617401Perkins S.M., Webb D.L., Torrance S.A., El Saleeby C., Harrison L.M., Aitken J.A., Patel A., and DeVincenzo J.P.. 2005. Comparison of a real-time reverse transcriptase PCR assay and a culture technique for quantitative assessment of viral load in children naturally infected with respiratory syncytial virus. J. Clin. Microbiol. 43:2356–2362 10.1128/JCM.43.5.2356-2362.200510.1128/JCM.43.5.2356-2362.2005PMC115376715872266Plantinga M., Guilliams M., Vanheerswynghels M., Deswarte K., Branco-Madeira F., Toussaint W., Vanhoutte L., Neyt K., Killeen N., Malissen B., et al. . 2013. Conventional and monocyte-derived CD11b+ dendritic cells initiate and maintain T helper 2 cell-mediated immunity to house dust mite allergen. Immunity. 38:322–335 10.1016/j.immuni.2012.10.01610.1016/j.immuni.2012.10.01623352232Pribul P.K., Harker J., Wang B., Wang H., Tregoning J.S., Schwarze J., and Openshaw P.J.M.. 2008. Alveolar macrophages are a major determinant of early responses to viral lung infection but do not influence subsequent disease development. J. Virol. 82:4441–4448 10.1128/JVI.02541-0710.1128/JVI.02541-07PMC229304918287232Ravi L.I., Li L., Sutejo R., Chen H., Wong P.S., Tan B.H., and Sugrue R.J.. 2013. A systems-based approach to analyse the host response in murine lung macrophages challenged with respiratory syncytial virus. BMC Genomics. 14:190 10.1186/1471-2164-14-19010.1186/1471-2164-14-190PMC361826023506210Rutigliano J.A., and Graham B.S.. 2004. Prolonged production of TNF-α exacerbates illness during respiratory syncytial virus infection. J. Immunol. 173:3408–3417 10.4049/jimmunol.173.5.340810.4049/jimmunol.173.5.340815322205Samstein M., Schreiber H.A., Leiner I.M., Susac B., Glickman M.S., and Pamer E.G.. 2013. Essential yet limited role for CCR2+ inflammatory monocytes during Mycobacterium tuberculosis-specific T cell priming. eLife. 2:e01086.PMC382097124220507Scagnolari C., Midulla F., Selvaggi C., Monteleone K., Bonci E., Papoff P., Cangiano G., Di Marco P., Moretti C., Pierangeli A., and Antonelli G.. 2012. Evaluation of viral load in infants hospitalized with bronchiolitis caused by respiratory syncytial virus. Med. Microbiol. Immunol. (Berl.). 201:311–317 10.1007/s00430-012-0233-610.1007/s00430-012-0233-6PMC708688322406873Schijf M.A., Lukens M.V., Kruijsen D., van Uden N.O.P., Garssen J., Coenjaerts F.E.J., Van’t Land B., and van Bleek G.M.. 2013. Respiratory syncytial virus induced type I IFN production by pDC is regulated by RSV-infected airway epithelial cells, RSV-exposed monocytes and virus specific antibodies. PLoS ONE. 8:e81695 10.1371/journal.pone.008169510.1371/journal.pone.0081695PMC384112424303065Schoggins J.W., MacDuff D.A., Imanaka N., Gainey M.D., Shrestha B., Eitson J.L., Mar K.B., Richardson R.B., Ratushny A.V., Litvak V., et al. . 2014. Pan-viral specificity of IFN-induced genes reveals new roles for cGAS in innate immunity. Nature. 505:691–695 10.1038/nature1286210.1038/nature12862PMC407772124284630Segura E., Touzot M., Bohineust A., Cappuccio A., Chiocchia G., Hosmalin A., Dalod M., Soumelis V., and Amigorena S.. 2013. Human inflammatory dendritic cells induce Th17 cell differentiation. Immunity. 38:336–348 10.1016/j.immuni.2012.10.01810.1016/j.immuni.2012.10.01823352235Seo S.-U., Kwon H.-J., Ko H.-J., Byun Y.-H., Seong B.L., Uematsu S., Akira S., and Kweon M.-N.. 2011. Type I interferon signaling regulates Ly6Chi monocytes and neutrophils during acute viral pneumonia in mice. PLoS Pathog. 7:e1001304 10.1371/journal.ppat.100130410.1371/journal.ppat.1001304PMC304470221383977Serbina N.V., and Pamer E.G.. 2006. Monocyte emigration from bone marrow during bacterial infection requires signals mediated by chemokine receptor CCR2. Nat. Immunol. 7:311–317 10.1038/ni130910.1038/ni130916462739Serbina N.V., Salazar-Mather T.P., Biron C.A., Kuziel W.A., and Pamer E.G.. 2003. TNF/iNOS-producing dendritic cells mediate innate immune defense against bacterial infection. Immunity. 19:59–70 10.1016/S1074-7613(03)00171-710.1016/S1074-7613(03)00171-712871639Shi C., and Pamer E.G.. 2011. Monocyte recruitment during infection and inflammation. Nat. Rev. Immunol. 11:762–774 10.1038/nri307010.1038/nri3070PMC394778021984070Siezen C.L.E., Bont L., Hodemaekers H.M., Ermers M.J., Doornbos G., Van’t Slot R., Wijmenga C., Houwelingen H.C., Kimpen J.L.L., Kimman T.G., et al. . 2009. Genetic susceptibility to respiratory syncytial virus bronchiolitis in preterm children is associated with airway remodeling genes and innate immune genes. Pediatr. Infect. Dis. J. 28:333–335 10.1097/INF.0b013e31818e2aa910.1097/INF.0b013e31818e2aa919258923Slater L., Bartlett N.W., Haas J.J., Zhu J., Message S.D., Walton R.P., Sykes A., Dahdaleh S., Clarke D.L., Belvisi M.G., et al. . 2010. Co-ordinated role of TLR3, RIG-I and MDA5 in the innate response to rhinovirus in bronchial epithelium. PLoS Pathog. 6:e1001178 10.1371/journal.ppat.100117810.1371/journal.ppat.1001178PMC297383121079690Smit J.J., Rudd B.D., and Lukacs N.W.. 2006. Plasmacytoid dendritic cells inhibit pulmonary immunopathology and promote clearance of respiratory syncytial virus. J. Exp. Med. 203:1153–1159 10.1084/jem.2005235910.1084/jem.20052359PMC212119916682497Spann K.M., Tran K.C., Chi B., Rabin R.L., and Collins P.L.. 2004. Suppression of the induction of alpha, beta, and lambda interferons by the NS1 and NS2 proteins of human respiratory syncytial virus in human epithelial cells and macrophages [corrected]. J. Virol. 78:4363–4369 10.1128/JVI.78.8.4363-4369.200410.1128/JVI.78.8.4363-4369.2004PMC37427615047850Stark J.M., Khan A.M., Chiappetta C.L., Xue H., Alcorn J.L., and Colasurdo G.N.. 2005. Immune and functional role of nitric oxide in a mouse model of respiratory syncytial virus infection. J. Infect. Dis. 191:387–395 10.1086/42724110.1086/42724115633098Tal G., Mandelberg A., Dalal I., Cesar K., Somekh E., Tal A., Oron A., Itskovich S., Ballin A., Houri S., et al. . 2004. Association between common Toll-like receptor 4 mutations and severe respiratory syncytial virus disease. J. Infect. Dis. 189:2057–2063 10.1086/42083010.1086/42083015143473Tulic M.K., Hurrelbrink R.J., Prêle C.M., Laing I.A., Upham J.W., Le Souef P., Sly P.D., and Holt P.G.. 2007. TLR4 polymorphisms mediate impaired responses to respiratory syncytial virus and lipopolysaccharide. J. Immunol. 179:132–140 10.4049/jimmunol.179.1.13210.4049/jimmunol.179.1.13217579031Welliver R.C., Garofalo R.P., and Ogra P.L.. 2002. Beta-chemokines, but neither T helper type 1 nor T helper type 2 cytokines, correlate with severity of illness during respiratory syncytial virus infection. Pediatr. Infect. Dis. J. 21:457–461 10.1097/00006454-200205000-0003310.1097/00006454-200205000-0003312150192Yoboua F., Martel A., Duval A., Mukawera E., and Grandvaux N.. 2010. Respiratory syncytial virus-mediated NF-κB p65 phosphorylation at serine 536 is dependent on RIG-I, TRAF6, and IKKβ. J. Virol. 84:7267–7277 10.1128/JVI.00142-1010.1128/JVI.00142-10PMC289824720410276
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1083-351X290212015May22The Journal of biological chemistryJ Biol ChemHeterogeneous Nuclear Ribonucleoprotein C Proteins Interact with the Human Papillomavirus Type 16 (HPV16) Early 3'-Untranslated Region and Alleviate Suppression of HPV16 Late L1 mRNA Splicing.133541337113354-7110.1074/jbc.M115.638098In order to identify cellular factors that regulate human papillomavirus type 16 (HPV16) gene expression, cervical cancer cells permissive for HPV16 late gene expression were identified and characterized. These cells either contained a novel spliced variant of the L1 mRNAs that bypassed the suppressed HPV16 late, 5'-splice site SD3632; produced elevated levels of RNA-binding proteins SRSF1 (ASF/SF2), SRSF9 (SRp30c), and HuR that are known to regulate HPV16 late gene expression; or were shown by a gene expression array analysis to overexpress the RALYL RNA-binding protein of the heterogeneous nuclear ribonucleoprotein C (hnRNP C) family. Overexpression of RALYL or hnRNP C1 induced HPV16 late gene expression from HPV16 subgenomic plasmids and from episomal forms of the full-length HPV16 genome. This induction was dependent on the HPV16 early untranslated region. Binding of hnRNP C1 to the HPV16 early, untranslated region activated HPV16 late 5'-splice site SD3632 and resulted in production of HPV16 L1 mRNAs. Our results suggested that hnRNP C1 controls HPV16 late gene expression.© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.DhanjalSoniyaSFrom the Department of Laboratory Medicine, Lund University, 221 84 Lund, Sweden.KajitaniNaokoNFrom the Department of Laboratory Medicine, Lund University, 221 84 Lund, Sweden.GlahderJacobJFrom the Department of Laboratory Medicine, Lund University, 221 84 Lund, Sweden.MossbergAnn-KristinAKFrom the Department of Laboratory Medicine, Lund University, 221 84 Lund, Sweden.JohanssonCeciliaCFrom the Department of Laboratory Medicine, Lund University, 221 84 Lund, Sweden.SchwartzStefanSFrom the Department of Laboratory Medicine, Lund University, 221 84 Lund, Sweden Stefan.Schwartz@med.lu.se.engJournal ArticleResearch Support, Non-U.S. Gov't20150415
United StatesJ Biol Chem2985121R0021-925803' Untranslated Regions0C1 HNRNP0Capsid Proteins0Heterogeneous-Nuclear Ribonucleoprotein Group C0Oncogene Proteins, Viral0RNA, Messenger0RNA, Viral6LTE2DNX63L1 protein, Human papillomavirus type 16IM3' Untranslated RegionsgeneticsBlotting, WesternCapsid ProteinsgeneticsmetabolismEpidermal CellsEpidermismetabolismvirologyFemaleFluorescent Antibody TechniqueGene Expression Regulation, ViralHeterogeneous-Nuclear Ribonucleoprotein Group CgeneticsmetabolismHuman papillomavirus 16physiologyHumansImmunoprecipitationKeratinocytescytologymetabolismvirologyMicroarray AnalysisOncogene Proteins, ViralgeneticsmetabolismRNA SplicinggeneticsRNA, MessengergeneticsRNA, ViralgeneticsReal-Time Polymerase Chain ReactionReverse Transcriptase Polymerase Chain ReactionTumor Cells, CulturedUterine Cervical NeoplasmsmetabolismpathologyvirologyDNA virusesHPVRNA processingRNA splicingRNA-binding proteintumor virus
2015112201541760201541760201592602016522ppublish25878250PMC450558510.1074/jbc.M115.638098S0021-9258(20)33695-4Walboomers J. M., Jacobs M. V., Manos M. M., Bosch F. X., Kummer J. A., Shah K. V., Snijders P. J., Peto J., Meijer C. J., Muñoz N. (1999) Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J. Pathol. 189, 12–1910451482Bouvard V., Baan R., Straif K., Grosse Y., Secretan B., El Ghissassi F., Benbrahim-Tallaa L., Guha N., Freeman C., Galichet L., Cogliano V., and WHO International Agency for Research on Cancer Monograph Working Group (2009) A review of human carcinogens: part B: biological agents. Lancet Oncol. 10, 321–32219350698Bosch F. X., Lorincz A., Muñoz N., Meijer C. J., Shah K. V. (2002) The causal relation between human papillomavirus and cervical cancer. J. Clin. Pathol. 55, 244–265PMC176962911919208zur Hausen H. (2002) Papillomaviruses and cancer: from basic studies to clinical application. Nat. Rev. Cancer 2, 342–35012044010Howley P. M., Lowy D. R. (2006) in Virology, 5th Ed (Knipe D. M., Howley P. M., eds) pp 2299–2354, Lippincott, PhiladelphiaDoorbar J. (2005) The papillomavirus life cycle. J. Clin. Virol. 32, S7–S1515753007Chow L. T., Broker T. R., Steinberg B. M. (2010) The natural history of human papillomavirus infections of the mucosal epithelia. APMIS 118, 422–44920553526Bodily J., Laimins L. A. (2011) Persistence of human papillomavirus infection: keys to malignant progression. Trends Microbiol. 19, 33–39PMC305972521050765Johansson C., Schwartz S. (2013) Regulation of human papillomavirus gene expression by splicing and polyadenylation. Nat. Rev. Microbiol. 11, 239–25123474685Thierry F. (2009) Transcriptional regulation of the papillomavirus oncogenes by cellular and viral transcription factors in cervical carcinoma. Virology 384, 375–37919064276McBride A. A. (2013) The papillomavirus E2 proteins. Virology 445, 57–79PMC378356323849793Bernard H. U. (2013) Regulatory elements in the viral genome. Virology 445, 197–20423725692Graham S. V. (2008) Papillomavirus 3′ UTR regulatory elements. Front. Biosci. 13, 5646–566318508613Jia R., Zheng Z. M. (2009) Regulation of bovine papillomavirus type 1 gene expression by RNA processing. Front. Biosci. 14, 1270–1282PMC265460219273129Schwartz S. (2013) Papillomavirus transcripts and posttranscriptional regulation. Virology 445, 187–19623706315Baker C., Calef C. (1997) in Human Papillomaviruses: A Compilation and Analysis of Nucleic Acid and Amino Acid Sequences (Billakanti S. R., Calef C. E., Farmer A. D., Halpern A. L., Myers G. L., eds) Los Alamos National Laboratory, Los Almos, NMJeon S., Lambert P. F. (1995) Integration of human papillomavirus type 16 DNA into the human genome leads to increased stability of E6 and E7 mRNAs: implications for cervical carcinogenesis. Proc. Natl. Acad. Sci. U.S.A. 92, 1654–1658PMC425787878034Zhao X., Oberg D., Rush M., Fay J., Lambkin H., Schwartz S. (2005) A 57 nucleotide upstream early polyadenylation element in human papillomavirus type 16 interacts with hFip1, CstF-64, hnRNP C1/C2 and PTB. J. Virol. 79, 4270–4288PMC106155415767428Terhune S. S., Hubert W. G., Thomas J. T., Laimins L. A. (2001) Early polyadenylation signals of human papillomavirus type 31 negatively regulate capsid gene expression. J. Virol. 75, 8147–8157PMC11505911483760Johansson C., Somberg M., Li X., Backström Winquist E., Fay J., Ryan F., Pim D., Banks L., Schwartz S. (2012) HPV-16 E2 contributes to induction of HPV-16 late gene expression by inhibiting early polyadenylation. EMBO J. 31, 3212–3227PMC340001122617423Oberg D., Fay J., Lambkin H., Schwartz S. (2005) A downstream polyadenylation element in human papillomavirus type 16 encodes multiple GGG-motifs and interacts with hnRNP H. J. Virol. 79, 9254–9269PMC116873415994820Rush M., Zhao X., Schwartz S. (2005) A splicing enhancer in the E4 coding region of human papillomavirus type 16 is required for early mRNA splicing and polyadenylation as well as inhibition of premature late gene expression. J. Virol. 79, 12002–12015PMC121264516140776Somberg M., Li X., Johansson C., Orru B., Chang R., Rush M., Fay J., Ryan F., Schwartz S. (2011) SRp30c activates human papillomavirus type 16 L1 mRNA expression via a bimodal mechanism. J. Gen. Virol. 92, 2411–242121697349Somberg M., Schwartz S. (2010) Multiple ASF/SF2 sites in the HPV-16 E4-coding region promote splicing to the most commonly used 3′-splice site on the HPV-16 genome. J. Virol. 84, 8219–8230PMC291653620519389Li X., Johansson C., Cardoso Palacios C., Mossberg A., Dhanjal S., Bergvall M., Schwartz S. (2013) Eight nucleotide substitutions inhibit splicing to HPV-16 3′-splice site SA3358 and reduce the efficiency by which HPV-16 increases the life span of primary human keratinocytes. PLoS One 8, e72776.PMC376765824039800Jia R., Li C., McCoy J. P., Deng C. X., Zheng Z. M. (2010) SRp20 is a proto-oncogene critical for cell proliferation and tumor induction and maintenance. Int. J. Biol. Sci. 6, 806–826PMC300534721179588Jia R., Liu X., Tao M., Kruhlak M., Guo M., Meyers C., Baker C. C., Zheng Z. M. (2009) Control of the papillomavirus early-to-late switch by differentially expressed SRp20. J. Virol. 83, 167–180PMC261233418945760Li X., Johansson C., Glahder J., Mossberg A. K., Schwartz S. (2013) Suppression of HPV-16 late L1 5′-splice site SD3632 by binding of hnRNP D proteins and hnRNP A2/B1 to upstream AUAGUA RNA motifs. Nucleic Acids Res. 22, 10488–10508PMC390590124013563Zhao X., Rush M., Schwartz S. (2004) Identification of an hnRNP A1 dependent splicing silencer in the HPV-16 L1 coding region that prevents premature expression of the late L1 gene. J. Virol. 78, 10888–10905PMC52183715452209Zhao X., Schwartz S. (2008) Inhibition of HPV-16 L1 expression from L1 cDNAs correlates with the presence of hnRNP A1 binding sites in the L1 coding region. Virus Genes 36, 45–5318040766Zhao X., Fay J., Lambkin H., Schwartz S. (2007) Identification of a 17-nucleotide splicing enhancer in HPV-16 L1 that counteracts the effect of multiple hnRNP A1-binding splicing silencers. Virology 369, 351–36317869320Collier B., Oberg D., Zhao X., Schwartz S. (2002) Specific inactivation of inhibitory sequences in the 5′ end of the human papillomavirus type 16 L1 open reading frame results in production of high levels of L1 protein in human epithelial cells. J. Virol. 76, 2739–2752PMC13597011861841Nagy A. (2000) Cre recombinase: the universal reagent for genome tailoring. Genesis 26, 99–10910686599Irizarry R. A., Hobbs B., Collin F., Beazer-Barclay Y. D., Antonellis K. J., Scherf U., Speed T. P. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–26412925520Tusher V. G., Tibshirani R., Chu G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U.S.A. 98, 5116–5121PMC3317311309499Dignam J. D., Lebovitz R. M., Roeder R. G. (1983) Accurate transcription initiation by RNA polymerase II in a soluble extract from isolated mammalian nuclei. Nucleic Acids Res. 11, 1475–1489PMC3258096828386Somberg M., Zhao X., Fröhlich M., Evander M., Schwartz S. (2008) PTB induces HPV-16 late gene expression by interfering with splicing inhibitory elements at the major late 5′-splice site SD3632. J. Virol. 82, 3665–3678PMC226844518216120Orrù B., Cunniffe C., Ryan F., Schwartz S. (2012) Development and validation of a novel reporter assay for human papillomavirus type 16 late gene expression. J. Virol. Methods 183, 106–11622484615Ozbun M. A., Meyers C. (1997) Characterization of late gene transcripts expressed during vegetative replication of human papillomavirus type 31b. J. Virol. 71, 5161–5172PMC1917519188583Busch A., Hertel K. J. (2012) Evolution of SR protein and hnRNP splicing regulatory factors. Wiley Interdiscip. Rev. RNA 3, 1–12PMC323522421898828Sokolowski M., Zhao C., Tan W., Schwartz S. (1997) AU-rich mRNA instability elements on human papillomavirus type 1 late mRNAs and c-fos mRNAs interact with the same cellular factors. Oncogene 15, 2303–23199393875Sokolowski M., Schwartz S. (2001) Heterogeneous nuclear ribonucleoprotein C binds exclusively to the functionally important UUUUU-motifs in the human papillomavirus type-1 AU-rich inhibitory element. Virus Res. 73, 163–17511172920McCloskey A., Taniguchi I., Shinmyozu K., Ohno M. (2012) hnRNP C tetramer measures RNA length to classify RNA polymerase II transcripts for export. Science 335, 1643–164622461616Fay J., Kelehan P., Lambkin H., Schwartz S. (2009) Increased expression of cellular RNA-binding proteins in HPV-induced neoplasia and cervical cancer. J. Med. Virol. 81, 897–90719319956Kozak M. (1992) Regulation of translation inititaion in eucaryotic systems. Annu. Rev. Cell Biol. 8, 197–2251335743Tomita Y., Simizu B. (1993) Translational properties of the human papillomavirus type-6 L1-coding mRNA. Gene 133, 223–2258224910Rosenberger S., De-Castro Arce J., Langbein L., Steenbergen R. D. M., Rösl F. (2010) Alternative splicing of human papillomavirus type-16 E6/E6* early mRNA is coupled to EGF signaling via Erk1/2 activation. Proc. Natl. Acad. Sci. U.S.A. 107, 7006–7011PMC287246720351270Cheunim T., Zhang J., Milligan S. G., McPhillips M. G., Graham S. V. (2008) The alternative splicing factor hnRNP A1 is up-regulated during virus-infected epithelial cell differentiation and binds the human papillomavirus type 16 late regulatory element. Virus Res. 131, 189–198PMC263552717950949Collier B., Goobar-Larsson L., Sokolowski M., Schwartz S. (1998) Translational inhibition in vitro of human papillomavirus type 16 L2 mRNA mediated through interaction with heterogenous ribonucleoprotein K and poly(rC)-binding proteins 1 and 2. J. Biol. Chem. 273, 22648–226569712894Zarnack K., König J., Tajnik M., Martincorena I., Eustermann S., Stévant I., Reyes A., Anders S., Luscombe N. M., Ule J. (2013) Direct competition between hnRNP C and U2AF65 protects the transcriptome from the exonization of Alu elements. Cell 152, 453–466PMC362956423374342Izquierdo J. M. (2010) Heterogeneous ribonucleoprotein C displays a repressor activity mediated by T-cell intracellular antigen-1-related/like protein to modulate Fas exon 6 splicing through a mechanism involving Hu antigen R. Nucleic Acids Res. 38, 8001–8014PMC300107020699271Irimura S., Kitamura K., Kato N., Saiki K., Takeuchi A., Gunadi, Matsuo M., Nishio H., Lee M. J. (2009) HnRNP C1/C2 may regulate exon 7 splicing in the spinal muscular atrophy gene SMN1. Kobe J. Med. Sci. 54, E227–E23619628962Motta-Mena L. B., Heyd F., Lynch K. W. (2010) Context-dependent regulatory mechanism of the splicing factor hnRNP L. Mol Cell. 37, 223–234PMC281886820122404Tenzer S., Moro A., Kuharev J., Francis A. C., Vidalino L., Provenzani A., Macchi P. (2013) Proteome-wide characterization of the RNA-binding protein RALY-interactome using the in vivo-biotinylation-pulldown-quant (iBioPQ) approach. J. Proteome Res. 12, 2869–288423614458Dreyfuss G., Kim V. N., Kataoka N. (2002) Messenger-RNA-binding proteins and the messages they carry. Nat. Rev. Mol. Cell. Biol. 3, 195–20511994740Lee H.-H., Chien C.-L., Liao H.-K., Chen Y.-J., Chang Z.-F. (2004) Nuclear efflux of heterogeneous nuclear ribonucleoprotein C1/C2 in apoptotic cells: a novel nuclear export dependent on Rho-associated kinase activation. J. Cell Sci. 117, 5579–558915494373Piñol-Roma S., Dreyfuss G. (1993) Cell cycle-regulated phosphorylation of the pre-mRNA binding (heterogeneous nuclear ribonucleoprotein) C proteins. Mol. Cell. Biol. 13, 5762–5770PMC3603168395012Lee E. K., Kim H. H., Kuwano Y., Abdelmohsen K., Srikantan S., Subaran S. S., Gleichmann M., Mughal M. R., Martindale J. L., Yang X., Worley P. F., Mattson M. P., Gorospe M. (2010) hnRNP C promotes APP translation by competing with FMRP for APP mRNA recruitment to P bodies. Nat. Struct. Mol. Biol. 17, 732–739PMC290849220473314Spångberg K., Wiklund L., Schwartz S. (2000) HuR, a protein implicated in oncogene and growth factor mRNA decay, binds to the 3′ ends of hepatitis C virus RNA of both polarities. Virology 274, 378–39010964780Brunner J. E., Ertel K. J., Rozovics J. M., Semler B. L. (2010) Delayed kinetics of poliovirus RNA synthesis in a human cell line with reduced levels of hnRNP C proteins. Virology 400, 240–247PMC284448420189623Shabman R. S., Gulcicek E. E., Stone K. L., Basler C. F. (2011) The Ebola virus VP24 protein prevents hnRNP C1/C2 binding to karyopherin α1 and partially alters its nuclear import. J. Infect. Dis. 204, S904–S9910PMC318998521987768Casaca A., Fardilha M., da Cruz e Silva E., Cunha C. (2011) The heterogeneous ribonuclear protein C interacts with the hepatitis δ virus small antigen. Virol. J. 8, 358.PMC316040721774814
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Publications by Johansson C | LitMetric

Publications by authors named "Johansson C"

Objectives: The main aim of this study was to evaluate the performance of 10 individual sensors of the same make, using objective measures of key image quality parameters. A further aim was to compare 8 brands of sensors.

Study Design: Ten new sensors of 8 different models from 6 manufacturers (i.

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OBJECTIVE Several older studies report a low risk for parenchymal access to the CNS by surgical techniques. In more recent studies, including those with post-puncture CT scans, there are indications that the risk of bleeding might approach 8%. New therapies, such as those that use viral vectors, modified mRNA, or cell transplantation, will probably warrant more parenchymal access to the CNS.

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Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies.

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Combustion-related carbonaceous particles seem to be a better indicator of adverse health effects compared to PM2.5 and PM10. Historical studies are based on black smoke (BS), but more recent studies use absorbance (Abs), black carbon (BC) or elemental carbon (EC) as exposure indicators.

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The 14th International Congress on Combustion By-Products and Their Health Effects was held in Umeå, Sweden from June 14th to 17th, 2015. The Congress, mainly sponsored by the National Institute of Environmental Health Sciences Superfund Research Program and the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, focused on the "Origin, fate and health effects of combustion-related air pollutants in the coming era of bio-based energy sources". The international delegates included academic and government researchers, engineers, scientists, policymakers and representatives of industrial partners.

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Pregnancy Summit, Cineworld, The O2, London, UK, 29 September to 1 October 2015 The 2015 Pregnancy Summit was held over 3 days from 29 September to 1 October at Cineworld, The O2, London, UK. The event brings together a multidisciplinary faculty of international researchers and clinicians to discuss both scientific and clinical aspects of pregnancy-related issues in an informal setting. The goal of the meeting was to provide delegates with an update of recent advances in management of pregnancy-related conditions, to present research data and to discuss the current attitudes and practices in relevant topics.

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Soluble epoxide hydrolase (sEH) is involved in the regulation of many biological processes by metabolizing the key bioactive lipid mediator, epoxyeicosatrienoic acids. For the development of sEH inhibitors with improved physicochemical properties, we performed both a fragment screening and a high-throughput screening aiming at an integrated hit evaluation and lead generation. Followed by a joint dose-response analysis to confirm the hits, the identified actives were then effectively triaged by a structure-based hit-classification approach to three prioritized series.

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Study Question: What proteins are carried by extracellular vesicles (EVs) released from normal first trimester placentae?

Summary Answer: One thousand five hundred and eighty-five, 1656 and 1476 proteins were characterized in macro-, micro- and nano-vesicles, respectively, from first trimester placentae, with all EV fractions being enriched for proteins involved in vesicle transport and inflammation.

What Is Known Already: Placental EVs are being increasingly recognized as important mediators of both healthy and pathological pregnancies. However, current research has focused on detecting changes in specific proteins in particular fractions of vesicles during disease.

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Stress and anxiety may worsen atopic dermatitis (AD) through the serotonin system. Serotonergic expression was measured in 28 patients with AD in relation to extent of the disease (SCORing of Atopic Dermatitis; SCORAD), pruritus intensity (visual analogue scale; VAS), anxiety traits (Swedish Universities Scales of Personality; SSP) and depression (Montgomery-Åsberg Depression Rating Scale-Self assessment; MADRS-S). Biopsies were taken from lesional and non-lesional AD skin, and investigated for expression of serotonin, its receptors 5-HT1A and 5-HT2, and serotonin transporter protein (SERT), using immunohistochemistry.

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The hypothesis of a km-thick ice shelf covering the entire Arctic Ocean during peak glacial conditions was proposed nearly half a century ago. Floating ice shelves preserve few direct traces after their disappearance, making reconstructions difficult. Seafloor imprints of ice shelves should, however, exist where ice grounded along their flow paths.

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Rising atmospheric CO2 concentrations will significantly reduce ocean pH during the 21st century (ocean acidification, OA). This may hamper calcification in marine organisms such as corals and echinoderms, as shown in many laboratory-based experiments. Sea urchins are considered highly vulnerable to OA.

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We report the discovery of N-substituted 4-(pyridin-2-yl)thiazole-2-amine derivatives and their subsequent optimization, guided by structure-based design, to give 8-(1H-pyrazol-3-yl)pyrido[3,4-d]pyrimidin-4(3H)-ones, a series of potent JmjC histone N-methyl lysine demethylase (KDM) inhibitors which bind to Fe(II) in the active site. Substitution from C4 of the pyrazole moiety allows access to the histone peptide substrate binding site; incorporation of a conformationally constrained 4-phenylpiperidine linker gives derivatives such as 54j and 54k which demonstrate equipotent activity versus the KDM4 (JMJD2) and KDM5 (JARID1) subfamily demethylases, selectivity over representative exemplars of the KDM2, KDM3, and KDM6 subfamilies, cellular permeability in the Caco-2 assay, and, for 54k, inhibition of H3K9Me3 and H3K4Me3 demethylation in a cell-based assay.

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Pattern recognition receptors (PRRs) and cytokine receptors are key players in the initiation of immune responses to infection. PRRs detecting viral RNA, such as toll like receptor (TLR)-3, -7/8, and RIG-I like receptors (RLRs; RIG-I and MDA-5), as well as cytokine receptors such as interleukin 1 receptor (IL-1R), have been implicated in responses to RNA viruses that infect the airways. The latter includes respiratory syncytial virus (RSV), a human pathogen that can cause severe lower respiratory tract infections, especially in infants.

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Ash disposal waters from coal-fired power stations present a challenging water treatment scenario as they contain high concentrations of the oxyanions Se, As and Mo which are difficult to remove through conventional techniques. In an innovative process, macroalgae can be treated with Fe and processed through slow pyrolysis into Fe-biochar which has a high affinity for oxyanions. However, the effect of production conditions on the efficacy of Fe-biochar is poorly understood.

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Background Context: Current literature suggests that in the long-term, fusion of the lumbar spine in chronic low back pain (CLBP) does not result in an outcome clearly better than structured conservative treatment modes.

Purpose: This study aimed to assess the long-term outcome of lumbar fusion in CLBP, and also to assess methodological problems in long-term randomized controlled trials (RCTs).

Study Design: A prospective randomized study was carried out.

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This study presents classification of different magnetic single- and multi-core particle systems using their measured dynamic magnetic properties together with their nanocrystal and particle sizes. The dynamic magnetic properties are measured with AC (dynamical) susceptometry and magnetorelaxometry and the size parameters are determined from electron microscopy and dynamic light scattering. Using these methods, we also show that the nanocrystal size and particle morphology determines the dynamic magnetic properties for both single- and multi-core particles.

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Magnetic nanoparticle systems can be divided into single-core nanoparticles (with only one magnetic core per particle) and magnetic multi-core nanoparticles (with several magnetic cores per particle). Here, we report multi-core nanoparticle synthesis based on a controlled precipitation process within a well-defined oil in water emulsion to trap the superparamagnetic iron oxide nanoparticles (SPION) in a range of polymer matrices of choice, such as poly(styrene), poly(lactid acid), poly(methyl methacrylate), and poly(caprolactone). Multi-core particles were obtained within the Z-average size range of 130 to 340 nm.

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Although the anti-inflammatory role of the A2a receptor is well established, controversy remains with regard to the therapeutic value for A2a agonists in treatment of inflammatory lung diseases, also as a result of unwanted A2a-mediated cardiovascular effects. In this paper, we describe the discovery and characterization of a new, potent and selective A2a agonist (compound 2) with prolonged lung retention and limited systemic exposure following local administration. To support the lead optimization chemistry program with compound selection and profiling, multiple in vitro and in vivo assays were used, characterizing compound properties, pharmacodynamics (PD), and drug concentrations.

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Study Design Randomized controlled trial. Objective Despite a large number of publications of outcomes after spinal fusion surgery, there is still no consensus on the efficacy of the several different fusion methods. The aim of this study was to determine whether transforaminal lumbar interbody fusion (TLIF) results in an improved clinical outcome compared with uninstrumented posterolateral fusion (PLF) in the surgical treatment for chronic low back pain.

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Background: It is well documented that bone marrow-derived cells can fuse with a diverse range of cells, including brain cells, under normal or pathological conditions. Inflammation leads to robust fusion of bone marrow-derived cells with Purkinje cells and the formation of binucleate heterokaryons in the cerebellum. Heterokaryons form through the fusion of two developmentally differential cells and as a result contain two distinct nuclei without subsequent nuclear or chromosome loss.

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A planned 21 km bypass (18 km within a tunnel) in Stockholm is expected to reduce ambient air exposure to traffic emissions, but same time tunnel users could be exposed to high concentrations of pollutants. For the health impacts calculations in 2030, the change in annual ambient NOX and PM10 exposure of the general population was modelled in 100 × 100 m(2) grids for Greater Stockholm area. The tunnel exposure was estimated based on calculated annual average NOX concentrations, time spent in tunnel and number of tunnel users.

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Triclosan is a widely used antibacterial agent that has become a ubiquitous contaminant in freshwater, estuary, and marine environments. Concerns about potential adverse effects of triclosan have been described in several recent risk assessments. Its effects on freshwater microbial communities have been well studied, but studies addressing effects on marine microbial communities are scarce.

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We report that many histone modifications are unevenly distributed over the HPV16 genome in cervical cancer cells as well as in HPV16-immortalized keratinocytes. For example, H3K36me3 and H3K9Ac that are common in highly expressed cellular genes and over exons, were more common in the early than in the late region of the HPV16 genome. In contrast, H3K9me3, H4K20me3, H2BK5me1 and H4K16Ac were more frequent in the HPV16 late region.

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Type I interferons (IFNs) are important for host defense from viral infections, acting to restrict viral production in infected cells and to promote antiviral immune responses. However, the type I IFN system has also been associated with severe lung inflammatory disease in response to respiratory syncytial virus (RSV). Which cells produce type I IFNs upon RSV infection and how this directs immune responses to the virus, and potentially results in pathological inflammation, is unclear.

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In order to identify cellular factors that regulate human papillomavirus type 16 (HPV16) gene expression, cervical cancer cells permissive for HPV16 late gene expression were identified and characterized. These cells either contained a novel spliced variant of the L1 mRNAs that bypassed the suppressed HPV16 late, 5'-splice site SD3632; produced elevated levels of RNA-binding proteins SRSF1 (ASF/SF2), SRSF9 (SRp30c), and HuR that are known to regulate HPV16 late gene expression; or were shown by a gene expression array analysis to overexpress the RALYL RNA-binding protein of the heterogeneous nuclear ribonucleoprotein C (hnRNP C) family. Overexpression of RALYL or hnRNP C1 induced HPV16 late gene expression from HPV16 subgenomic plasmids and from episomal forms of the full-length HPV16 genome.

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