Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) based data are used to evaluate the effects of the COVID-19 lockdown on the concentrations of pollutants such as aerosol optical depth (AOD) and tropospheric columns of nitrogen dioxide (NO) along with sulfur dioxide (SO) respectively for the period of January 2017 to September 2021 over the capital city of Assam, Guwahati. In India lockdown due to COVID-19 was first imposed from 24th March to 14th April as phase I and then it extended from 15th April to 3rd May as phase II in the year 2020. The concentration of all pollutants was usually fall during the lockdown period as compared to their average during the 5-year period over the study area. The results showed that Pre-monsoon (March-May) seasonal AOD for the pandemic year 2020 was decreased by ∼ 23% after lockdown as compared to same season of normal years over the study location. The seasonally averaged AOD reached its peak value in pre-monsoon (0.78 ± 0.09), followed by winter (0.59 ± 0.10) and monsoon (0.52 ± 0.05), with the minimum taking place in post-monsoon (0.38 ± 0.08) season. The monthly average AOD varies from its highest value (0.82 ± 0.18) in May to its lowest value (0.36 ± 0.10) in October for the study period over Guwahati. Tropospheric column NO exhibits same seasonality as AOD with highest value (0.21 × 10 molecules cm) in pre-monsoon and lowest value (0.13 × 10 molecules cm) in post-monsoon season which may be due to same source of origination of both NO and AOD. Conversely, SO does not vary much from the five-year average value during the lockdown period. Significant reduction in PM mass concentration value during Covid-19 lockdown months has been observed which indicates short term improvement of air quality over Guwahati.
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http://dx.doi.org/10.1016/j.matpr.2022.06.218 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
View Article and Find Full Text PDFACS Nano
January 2025
NOVA Medical School|Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, Lisbon 1169-056, Portugal.
The "" under this Perspective underline the importance of interdisciplinary collaboration and partnerships across several disciplines, such as medical science and technology, medicine, bioengineering, and computational approaches, in bridging the gap between research, manufacturing, and clinical applications. Effective communication is key to bridging team gaps, enhancing trust, and resolving conflicts, thereby fostering teamwork and individual growth toward shared goals. Drawing from the success of the COVID-19 vaccine development, we advocate the application of similar collaborative models in other complex health areas such as nanomedicine and biomedical engineering.
View Article and Find Full Text PDFClin Infect Dis
January 2025
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany.
Background: Existing risk evaluation tools underperform in predicting intensive care unit (ICU) admission for patients with the Coronavirus Disease 2019 (COVID-19). This study aimed to develop and evaluate an accurate and calculator-free clinical tool for predicting ICU admission at emergency room (ER) presentation.
Methods: Data from patients with COVID-19 in a nationwide German cohort (March 2020-January 2023) were analyzed.
Medicine (Baltimore)
January 2025
Centro Universitario de Enfermería Cruz Roja, University of Seville, Seville, Spain.
Background: There is an increased prevalence of mental health problems in various population groups as a result of the COVID-19 pandemic and its consequences, especially regarding anxiety, stress, depression, fear, and sleep disturbances, require to be investigated longitudinally.
Objective: This study aimed to determine the impact that the COVID-19 pandemic had on the mental health of Nursing students, as well as to examine other associated factors such as anxiety, fear, sleep disturbances, and coping strategies.
Method: This systematic review and meta-analysis were designed following the PRISMA guidelines and were registered in PROSPERO with code CRD42024541904.
Ann Intensive Care
January 2025
School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 5/F, 3 Sassoon Road, Academic Building, Pokfulam, Hong Kong.
Objective: Evidence of the overall estimated prevalence of post-intensive care cognitive impairment among critically ill survivors discharged from intensive care units at short-term and long-term follow-ups is lacking. This study aimed to estimate the prevalence of the post-intensive care cognitive impairment at time to < 1 month, 1 to 3 month(s), 4 to 6 months, 7-12 months, and > 12 months discharged from intensive care units.
Methods: Electronic databases including PubMed, Cochrane Library, EMBASE, CINAHL Plus, Web of Science, and PsycINFO via ProQuest were searched from inception through July 2024.
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