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Spreading depolarization triggers pro- and anti-inflammatory signalling: a potential link to headache.
Brain
January 2025
Institute of Neurological Sciences and Psychiatry, Hacettepe University, 06100, Ankara, Turkey.
Cortical spreading depolarization (CSD), the neurophysiological event believed to underlie aura, may trigger migraine headaches through inflammatory signaling that originates in neurons and spreads to the meninges via astrocytes. Increasing evidence from studies on rodents and migraine patients supports this hypothesis. The transition from pro-inflammatory to anti-inflammatory mechanisms is crucial for resolving inflammation.
View Article and Find Full Text PDFNanocapsuled Neutrophil Extracellular Trap Scavenger Combating Chronic Infectious Bone Destruction Diseases.
Adv Sci (Weinh)
January 2025
State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Department of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China.
Chronic infectious bone destruction diseases, such as periodontitis, pose a significant global health challenge. Repairing the bone loss caused by these chronic infections remains challenging. In addition to pathogen removal, regulating host immunity is imperative.
View Article and Find Full Text PDFExploring medical error taxonomies and human factors in simulation-based healthcare education.
PLoS One
January 2025
Faculty of Medicine, Department of Simulation Medicine, Masaryk University, Brno, Czech Republic.
This study aims to provide an updated overview of medical error taxonomies by building on a robust review conducted in 2011. It seeks to identify the key characteristics of the most suitable taxonomy for use in high-fidelity simulation-based postgraduate courses in Critical Care. While many taxonomies are available, none seem to be explicitly designed for the unique context of healthcare simulation-based education, in which errors are regarded as essential learning opportunities.
View Article and Find Full Text PDFSemi-supervised learning from small annotated data and large unlabeled data for fine-grained Participants, Intervention, Comparison, and Outcomes entity recognition.
J Am Med Inform Assoc
January 2025
Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States.
Objective: Extracting PICO elements-Participants, Intervention, Comparison, and Outcomes-from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of PICO entities. This study aims to develop a named entity recognition (NER) model to extract PICO entities with fine granularities.
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