Hasil untuk "Medicine (General)"

J. Devlin, Y. Skrobik, C. Gélinas et al.

Objective: To update and expand the 2013 Clinical Practice Guidelines for the Management of Pain, Agitation, and Delirium in Adult Patients in the ICU. Design: Thirty-two international experts, four methodologists, and four critical illness survivors met virtually at least monthly. All section groups gathered face-to-face at annual Society of Critical Care Medicine congresses; virtual connections included those unable to attend. A formal conflict of interest policy was developed a priori and enforced throughout the process. Teleconferences and electronic discussions among subgroups and whole panel were part of the guidelines’ development. A general content review was completed face-to-face by all panel members in January 2017. Methods: Content experts, methodologists, and ICU survivors were represented in each of the five sections of the guidelines: Pain, Agitation/sedation, Delirium, Immobility (mobilization/rehabilitation), and Sleep (disruption). Each section created Population, Intervention, Comparison, and Outcome, and nonactionable, descriptive questions based on perceived clinical relevance. The guideline group then voted their ranking, and patients prioritized their importance. For each Population, Intervention, Comparison, and Outcome question, sections searched the best available evidence, determined its quality, and formulated recommendations as “strong,” “conditional,” or “good” practice statements based on Grading of Recommendations Assessment, Development and Evaluation principles. In addition, evidence gaps and clinical caveats were explicitly identified. Results: The Pain, Agitation/Sedation, Delirium, Immobility (mobilization/rehabilitation), and Sleep (disruption) panel issued 37 recommendations (three strong and 34 conditional), two good practice statements, and 32 ungraded, nonactionable statements. Three questions from the patient-centered prioritized question list remained without recommendation. Conclusions: We found substantial agreement among a large, interdisciplinary cohort of international experts regarding evidence supporting recommendations, and the remaining literature gaps in the assessment, prevention, and treatment of Pain, Agitation/sedation, Delirium, Immobility (mobilization/rehabilitation), and Sleep (disruption) in critically ill adults. Highlighting this evidence and the research needs will improve Pain, Agitation/sedation, Delirium, Immobility (mobilization/rehabilitation), and Sleep (disruption) management and provide the foundation for improved outcomes and science in this vulnerable population.

D. Chung

Articles selected and commented on by: Professor Doo Ryeon Chung, MD, PhD, Chief, Division of Infectious Diseases, Professor of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea, Director of Center for Infection Prevention and Control, Samsung Medical Center Secretary General, ANSORP, APFID (Asia Pacific Foundation for Infectious Diseases) Email: iddrchung@gmail.com

N. Glaser, M. Fritsch, L. Priyambada et al.

Department of Pediatrics, Section of Endocrinology, University of California, Davis School of Medicine, Sacramento, California, USA Department of Pediatric and Adolescent Medicine, Division of General Pediatrics, Medical University of Graz, Austria Medical University of Graz, Graz, Austria Division of Pediatric Endocrinology, Rainbow Children's Hospital, Hyderabad, India Department of Pediatrics, School of Medicine, University of Colorado, Aurora, Colorado, USA Department of Women's and Children's Health, G. Salesi Hospital, Ancona, Italy Department of Pediatrics, Division of Endocrinology and Metabolism, University of the Philippines, College of Medicine, Manila, Philippines Division of Endocrinology, Boston Children's Hospital, Boston, Massachusetts, USA Institute of Maternal and Child Research, School of Medicine, University of Chile, Santiago, Chile

I. Vroman, L. Tighzert

Biodegradable materials are used in packaging, agriculture, medicine and other areas. In recent years there has been an increase in interest in biodegradable polymers. Two classes of biodegradable polymers can be distinguished: synthetic or natural polymers. There are polymers produced from feedstocks derived either from petroleum resources (non renewable resources) or from biological resources (renewable resources). In general natural polymers offer fewer advantages than synthetic polymers. The following review presents an overview of the different biodegradable polymers that are currently being used and their properties, as well as new developments in their synthesis and applications.

J. Gerring

M. Levy, M. Fink, J. Marshall et al.

R. Reiter

K. Kroenke, R. Spitzer, Janet B W Williams

J. Bamford, P. Sandercock, C. Warlow et al.

S. Inouye, S. Bogardus, P. Charpentier et al.

V. Jevtovic-Todorovic, R. Hartman, Y. Izumi et al.

F. Pacini, M. Schlumberger, H. Dralle et al.

K. Ericsson, A. Lehmann

M. Levy, M. Fink, J. Marshall et al.

G. Hanks, N. Cherny, N. Christakis et al.

E. Lillie, B. Patay, J. Diamant et al.

Samia S. Alkhalil

IntroductionThe rising antibiotic resistance of Streptococcus pyogenes necessitates alternative anti-virulence strategies. Bacteriophage-encoded hyaluronate lyase (HylP2), a key virulence factor that promotes bacterial dissemination by degrading host extracellular matrix components, represents an attractive therapeutic target.MethodsIn this study, an integrated in silico approach was employed to identify potential HylP2 inhibitors from a library of 118 bioactive natural compounds. Following protocol validation through redocking of ascorbic acid (RMSD = 1.897 Å), virtual screening, ADMET prediction, molecular dynamics (MD) simulations, and per-residue energy decomposition analyses were performed.ResultsViolacein (−7.7 kcal/mol), sulfangolid C (−7.427 kcal/mol), chlorotonil A (−7.4 kcal/mol), xiamycin (−7.3 kcal/mol), and kulkenon (−7.1 kcal/ mol) were identified as the most potent binders. ADMET analysis confirmed that these leads possess favorable pharmacokinetic properties and compliance with Lipinski’s Rule of Five. Subsequent 100-ns molecular dynamics (MD) simulations and per-residue energy decomposition revealed that violacein, xiamycin, and kulkenon formed stable, compact complexes by “trapping” catalytic residues Arg279 and Tyr264.ConclusionThese findings suggest that these natural product scaffolds are promising anti-virulence leads that may limit S. pyogenes tissue invasion while minimizing selective pressure for resistance development.

ZiXuan Zhang, Bowen Hao, Yingjie Li et al.

Traditional Chinese Medicine (TCM) formulas play a significant role in treating epidemics and complex diseases. Existing models for TCM utilize traditional algorithms or deep learning techniques to analyze formula relationships, yet lack comprehensive results, such as complete formula compositions and detailed explanations. Although recent efforts have used TCM instruction datasets to fine-tune Large Language Models (LLMs) for explainable formula generation, existing datasets lack sufficient details, such as the roles of the formula's sovereign, minister, assistant, courier; efficacy; contraindications; tongue and pulse diagnosis-limiting the depth of model outputs. To address these challenges, we propose ZhiFangDanTai, a framework combining Graph-based Retrieval-Augmented Generation (GraphRAG) with LLM fine-tuning. ZhiFangDanTai uses GraphRAG to retrieve and synthesize structured TCM knowledge into concise summaries, while also constructing an enhanced instruction dataset to improve LLMs' ability to integrate retrieved information. Furthermore, we provide novel theoretical proofs demonstrating that integrating GraphRAG with fine-tuning techniques can reduce generalization error and hallucination rates in the TCM formula task. Experimental results on both collected and clinical datasets demonstrate that ZhiFangDanTai achieves significant improvements over state-of-the-art models. Our model is open-sourced at https://huggingface.co/tczzx6/ZhiFangDanTai1.0.

Rui Yang, Matthew Yu Heng Wong, Huitao Li et al.

The rapid growth of medical knowledge and increasing complexity of clinical practice pose challenges. In this context, large language models (LLMs) have demonstrated value; however, inherent limitations remain. Retrieval-augmented generation (RAG) technologies show potential to enhance their clinical applicability. This study reviewed RAG applications in medicine. We found that research primarily relied on publicly available data, with limited application in private data. For retrieval, approaches commonly relied on English-centric embedding models, while LLMs were mostly generic, with limited use of medical-specific LLMs. For evaluation, automated metrics evaluated generation quality and task performance, whereas human evaluation focused on accuracy, completeness, relevance, and fluency, with insufficient attention to bias and safety. RAG applications were concentrated on question answering, report generation, text summarization, and information extraction. Overall, medical RAG remains at an early stage, requiring advances in clinical validation, cross-linguistic adaptation, and support for low-resource settings to enable trustworthy and responsible global use.

Tianai Huang, Lu Lu, Jiayuan Chen et al.

Large language models (LLMs) excel in various NLP tasks and modern medicine, but their evaluation in traditional Chinese medicine (TCM) is underexplored. To address this, we introduce TCM3CEval, a benchmark assessing LLMs in TCM across three dimensions: core knowledge mastery, classical text understanding, and clinical decision-making. We evaluate diverse models, including international (e.g., GPT-4o), Chinese (e.g., InternLM), and medical-specific (e.g., PLUSE). Results show a performance hierarchy: all models have limitations in specialized subdomains like Meridian & Acupoint theory and Various TCM Schools, revealing gaps between current capabilities and clinical needs. Models with Chinese linguistic and cultural priors perform better in classical text interpretation and clinical reasoning. TCM-3CEval sets a standard for AI evaluation in TCM, offering insights for optimizing LLMs in culturally grounded medical domains. The benchmark is available on Medbench's TCM track, aiming to assess LLMs' TCM capabilities in basic knowledge, classic texts, and clinical decision-making through multidimensional questions and real cases.