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http://dx.doi.org/10.2147/OARRR.S122722 | DOI Listing |
Clin Infect Dis
January 2025
Department of Cellular Therapy and Allogeneic Stem Cell Transplantation, Karolinska University Hospital Huddinge, Karolinska Comprehensive Cancer Center, Stockholm, Sweden.
Herpes simplex virus (HSV) infection is one of the most prevalent viral infections worldwide. In general, host immunity is sufficient to clear viral shedding and recurrences, although it is insufficient to prevent subsequent virologic reactivations. In immunocompromised patients, prolonged and difficult-to-treat HSV infections may develop.
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January 2025
Department of Pharmacy, Personalized Drug Therapy Key Laboratory of Sichuan Province, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Objective: To optimize the construction of pharmaceutical services in medical institutions, advance the development of clinical pharmacy as a discipline, enhance the level of clinical pharmacy services, systematically implement and evaluate clinical pharmacy practices, and improve patient therapeutic outcomes, we have developed the Practice Guidelines for the Value Evaluation of Clinical Pharmacy Services (Version 2).
Methods: This guideline was designed following the World Health Organization (WHO) Guideline Development Manual. The Delphi method was employed to identify clinical questions.
Front Public Health
January 2025
Department of Psychiatry, Nihon University School of Medicine, Tokyo, Japan.
Introduction: Preventing depression among nurses is a critical issue from the perspective of occupational welfare, but associations between depressive symptoms in nurses and stress-coping strategies remain unclear.
Methods: In the present study, an epidemiological study was conducted based on a cross-sectional questionnaire survey. Data obtained from 2,534 female nurses working at three general hospitals in Tokyo, Japan, were analyzed.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
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January 2025
Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Introduction: Generating physician letters is a time-consuming task in daily clinical practice.
Methods: This study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner within the field of radiation oncology.
Results: Our findings demonstrate that base LLaMA models, without fine-tuning, are inadequate for effectively generating physician letters.
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