A toxicogenomic chip developed to detect welding-related diseases was tested and validated for field trials. To verify the suitability of the microarray, white blood cells (WBC) or whole blood was purified and characterized from 20 subjects in the control group (average work experience of 7 yr) and 20 welders in the welding-fume exposed group (welders with an average work experience of 23 yr). Two hundred and fifty-three rat genes homologous to human genes were obtained and spotted on the chip slide. Meanwhile, a human cDNA chip spotted with 8600 human genes was also used to detect any increased or decreased levels of gene expression among the welders. After comparing the levels of gene expression between the control and welder groups using the toxicogenomic chips, 103 genes were identified as likely to be specifically changed by welding-fume exposure. Eighteen of the 253 rat genes were specifically changed in the welders, while 103 genes from the human cDNA chip were specifically changed. The genes specifically expressed by the welders were associated with inflammatory responses, toxic chemical metabolism, stress proteins, transcription factors, and signal transduction. In contrast, there was no significant change in the genes related to short-term welding-fume exposure, such as tumor necrosis factor (TNF)-alpha and interleukin. In conclusion, if further validation studies are conducted, the present toxicogenomic gene chips could be used for the effective monitoring of welding-fume-exposure-related diseases among welders.
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http://dx.doi.org/10.1080/15287390701428986 | DOI Listing |
Environ Sci Technol
January 2025
Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah 84112, United States.
Methane (CH) is a greenhouse gas with a global warming potential 81.2 times higher than carbon dioxide (CO). The intentional emission of oxidants into the atmosphere has been proposed as a geoengineering solution to accelerate the oxidation of CH to CO, thereby reducing surface warming.
View Article and Find Full Text PDFChaos
January 2025
Centre for Mathematical Science, Lund University, Märkesbacken 4, 223 62 Lund, Sweden.
We investigate the dynamics of the adaptive Kuramoto model with slow adaptation in the continuum limit, N→∞. This model is distinguished by dense multistability, where multiple states coexist for the same system parameters. The underlying cause of this multistability is that some oscillators can lock at different phases or switch between locking and drifting depending on their initial conditions.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Department of Radiology, University of Chicago, Chicago, IL, USA.
Purpose: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pathology. Determining thyroid nodule contours is necessary for the calculation of TIRADS scores and can also be used in the development of machine learning nodule diagnosis systems. This paper presents the development, validation, and multi-institutional independent testing of a machine learning system for the automatic segmentation of thyroid nodules on ultrasound.
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January 2025
Abteilung für Plastische und Handchirurgie UniversitätsCentrum für Orthopädie, Unfall- & Plastische Chirurgie, Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden, Dresden, Germany.
Background: Kirner deformity is a rare anomaly of the little finger in adolescents, characterized by a deformity of the distal phalanx and a radiologically L-shaped epiphysis, along with palmar and radial angulation of the distal phalanx. Due to the rarity of these pathological findings, there are no systematic literature reviews available. This work serves as an overview of the clinical presentation, frequency and age distributions, as well as possible conservative and surgical treatment options.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Pennsylvania, Philadelphia, PA, USA.
Background: Alzheimer's disease (AD), characterized by significant brain volume reduction, is influenced by genetic predispositions related to brain volumetric phenotypes. While genome-wide association studies (GWASs) have linked brain imaging-derived phenotypes (IDPs) with AD, existing polygenic risk scores (PRSs) based models inadequately capture this relationship. We develop BrainNetScore, a network-based model enhancing AD risk prediction by integrating genetic associations between multiple brain IDPs and AD incidence.
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