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ExerGeneDB: A physical exercise-regulated differential gene expression database.
J Sport Health Sci
January 2025
Institute of Cardiovascular Sciences, Shanghai Engineering Research Center of Organ Repair, Joint International Research Laboratory of Biomaterials and Biotechnology in Organ Repair (Ministry of Education), School of Life Science, Shanghai University, Shanghai 200444, China; Cardiac Regeneration and Ageing Lab, Institute of Geriatrics (Shanghai University), Affiliated Nantong Hospital of Shanghai University (The Sixth People's Hospital of Nantong), School of Medicine, Shanghai University, Nantong 226011, China. Electronic address:
Background: Exercise induces molecular changes that involve multiple organs and tissues. Moreover, these changes are modulated by various exercise parameters-such as intensity, frequency, mode, and duration-as well as by clinical features like gender, age, and body mass index (BMI), each eliciting distinct biological effects. To assist exercise researchers in understanding these changes from a comprehensive perspective that includes multiple organs, diverse exercise regimens, and a range of clinical features, we developed Exercise Regulated Genes Database (ExerGeneDB), a database of exercise-regulated differential genes.
View Article and Find Full Text PDFHigh nature value farming systems in Europe: A dataset encompassing the environmental impact assessment of farms and extensive ruminant food products.
Data Brief
February 2025
Department of Agricultural Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Latokartanonkaari 5, 00014, Finland.
High Nature Value (HNV) farming systems occur in areas where the major land use is agriculture and are characterized by their significance in promoting biodiversity and ecosystem services due to their extensive land use. Despite their importance for ecological and socio-economic resilience of rural regions, these systems are often overlooked in Life Cycle Assessment (LCA) studies due to challenges in data compilation, especially from small local farms and because of the diversity of production. To address this gap, we established an international collaborative network across Europe, involving professionals directly engaged with farmers, farmer associations, and researchers to collect data on HNV farms employing a developed questionnaire examining inputs and outputs, farm structures, and herd characteristics.
View Article and Find Full Text PDFDevelopment and validation of a search strategy and an automated classifier for retrieving temporomandibular disorders studies.
J Oral Facial Pain Headache
June 2024
Orofacial Pain & TMD Program, Faculty of Odontology, Andres Bello University, 8370133 Santiago, Chile.
The objective was to develop and evaluate a comprehensive search strategy (SS) and automated classifier (AC) for retrieving temporomandibular disorders (TMD) research articles. An initial version of SS and AC was created by compiling terms from various sources, including previous systematic reviews (SRs) and consulting with TMD specialists. Performance was assessed using the relative recall (RR) method against a sample of all the primary studies (PS) included in 100 TMD-related SRs, with RR calculated for both SS and AC based on their ability to capture/classify TMD PSs.
View Article and Find Full Text PDFAssessing Artificial Intelligence in Oral Cancer Diagnosis: A Systematic Review.
J Craniofac Surg
October 2024
Department of Biomedical and Surgical and Biomedical Sciences, Catania University, Catania, Italy.
Background: With the use of machine learning algorithms, artificial intelligence (AI) has become a viable diagnostic and treatment tool for oral cancer. AI can assess a variety of information, including histopathology slides and intraoral pictures.
Aim: The purpose of this systematic review is to evaluate the efficacy and accuracy of AI technology in the detection and diagnosis of oral cancer between 2020 and 2024.
Large Language Models in Worldwide Medical Exams: Platform Development and Comprehensive Analysis.
J Med Internet Res
December 2024
Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Background: Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored.
Objective: This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide.
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