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Evolution of artificial intelligence in healthcare: a 30-year bibliometric study.
Front Med (Lausanne)
January 2025
Department of Cardiology, Heart Center, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Introduction: In recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, has significantly supported clinical work. Concurrently, the integration of artificial intelligence with the medical field has garnered increasing attention from medical experts. This study undertakes a dynamic and longitudinal bibliometric analysis of AI publications within the healthcare sector over the past three decades to investigate the current status and trends of the fusion between medicine and artificial intelligence.
View Article and Find Full Text PDFGlobal insights into the genome dynamics of associated with antimicrobial resistance, virulence, and genomic adaptations among clonal lineages.
Front Cell Infect Microbiol
January 2025
Department of Bacteriology, Pasteur Institute of Iran, Tehran, Iran.
Background: is a significant cause of healthcare-associated infections, with rising antimicrobial resistance complicating treatment. This study offers a genomic analysis of , focusing on sequence types (STs), global distribution, antibiotic resistance genes, and virulence factors in its chromosomal and plasmid DNA.
Methods: A total of 19,711 genomes were retrieved from GenBank.
Major change in swine influenza virus diversity in France owing to emergence and widespread dissemination of a newly introduced H1N2 1C genotype in 2020.
Virus Evol
December 2024
ANSES, Ploufragan-Plouzané-Niort Laboratory, Swine Virology Immunology Unit, National Reference Laboratory for Swine Influenza, BP53, Ploufragan 22440, France.
Swine influenza A viruses (swIAVs) are a major cause of respiratory disease in pigs worldwide, presenting significant economic and health risks. These viruses can reassort, creating new strains with varying pathogenicity and cross-species transmissibility. This study aimed to monitor the genetic and antigenic evolution of swIAV in France from 2019 to 2022.
View Article and Find Full Text PDFEffects of thermal aging on the performance of ordinary and novel superhydrophobic and oleophobic ultra-fine dry powder extinguishing agent.
Sci Rep
January 2025
School of Safety Science and Engineering (School of Emergency Management), Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China.
Powder-based fire extinguishing agents have become a kind of promising substitutes for halon extinguishing agents in civil aircrafts. However, their storage lifespan, significantly influenced by the thermal aging, emerges as a crucial yet overlooked aspect for aviation use. This study investigates the effects of thermal aging cycles on various parameters of ordinary dry powder extinguishing agent (ODPEA) and novel superhydrophobic and oleophobic ultra-fine dry powder extinguishing agent (SHOU DPEA), including surface microscopic morphology, D90 (the diameter at which 90% of the cumulative volume of particles are equal to or smaller than this value), chemical structure, hydrophobic and oleophobic angles, flowability, extinguishing time and effectiveness.
View Article and Find Full Text PDFExploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
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