Hasil untuk "Medicine"

M. Hamburg, F. Collins

Chien-Chi Lin, K. Anseth

Polyethylene glycol (PEG) hydrogels are widely used in a variety of biomedical applications, including matrices for controlled release of biomolecules and scaffolds for regenerative medicine. The design, fabrication, and characterization of PEG hydrogels rely on the understanding of fundamental gelation kinetics as well as the purpose of the application. This review article will focus on different polymerization mechanisms of PEG-based hydrogels and the importance of these biocompatible hydrogels in regenerative medicine applications. Furthermore, the design criteria that are important in maintaining the availability and stability of the biomolecules as well as the mechanisms for loading of biomolecules within PEG hydrogels will also be discussed. Finally, we overview and provide a perspective on some of the emerging novel design and applications of PEG hydrogel systems, including the spatiotemporal-controlled delivery of biomolecules, hybrid hydrogels, and PEG hydrogels designed for controlled stem cell differentiation.

M. Sporn, A. Roberts, D. Goodman

A. Karnad, A. Verghese

W. Rosenberg, A. Donald

Mark C. Jones

E. Gilbert, S. Lowenstein, J. Koziol-Mclain et al.

H. D. Vet, C. Terwee, L. Mokkink et al.

D. Lupton

S. Parikh, A. Goldstein, M. Koenig et al.

T. Kaptchuk, F. Miller

Rui Yang, Huitao Li, Weihao Xuan et al.

Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed the landscape of medicine and holds promise for improving health care quality and expanding access to medical information globally. However, existing LLMs are primarily trained on high-resource languages, limiting their applicability in global medical scenarios. To address this gap, we constructed GlobMed, a large multilingual medical dataset, containing over 500,000 entries spanning 12 languages, including four low-resource languages. Building on this, we established GlobMed-Bench, which systematically assesses 56 state-of-the-art proprietary and open-weight LLMs across multiple multilingual medical tasks, revealing significant performance disparities across languages, particularly for low-resource languages. Additionally, we introduced GlobMed-LLMs, a suite of multilingual medical LLMs trained on GlobMed, with parameters ranging from 1.7B to 8B. GlobMed-LLMs achieved an average performance improvement of over 40% relative to baseline models, with a more than threefold increase in performance on low-resource languages. Together, these resources provide an important foundation for advancing the equitable development and application of LLMs globally, enabling broader language communities to benefit from technological advances.

Pamela M. Chiroque-Solano, M Lee Van Horn, Thomas Jaki

Precision medicine seeks to match patients with treatments that produce the greatest benefit. The Predicted Individual Treatment Effect (PITE)-the difference between predicted outcomes under treatment and control-quantifies this benefit but is difficult to estimate due to unobserved counterfactuals, high dimensionality, and complex interactions. We compared 30+ modeling strategies, including penalized and projection-based methods, flexible learners, and tree-ensembles, using a structured simulation framework varying sample size, dimensionality, multicollinearity, and interaction complexity. Performance was measured using root mean squared error (RMSE) for prediction accuracy and directional accuracy (DIR) for correctly classifying benefit versus harm. Internal validation produced optimistic estimates, whereas external validation with distributional shifts and higher-order interactions more clearly revealed model weaknesses. Penalized and projection-based approaches-ridge, lasso, elastic net, partial least squares (PLS), and principal components regression (PCR)-consistently achieved strong RMSE and DIR performance. Flexible learners excelled only under strong signals and sufficient sample sizes. Results highlight robust linear/projection defaults and the necessity of rigorous external validation.

Brooklyn L. Laubinger, Kelsey M. Harvey, William Isaac Jumper

Gastrointestinal nematodes (GINs) remain a significant challenge to productivity and sustainability in beef cattle systems in the United States, contributing to subclinical reductions in growth, reproductive performance, and overall herd health across production stages. Control programs have historically relied on routine anthelmintic use; however, increasing reports of anthelmintic resistance highlight the need for alternative management strategies. This narrative review synthesizes peer-reviewed literature identified through targeted searches of major scientific databases spanning approximately seven decades, with articles selected for relevance to GIN epidemiology, diagnostics, and control in beef cattle. Particular emphasis is placed on life stage-specific susceptibility, host immune development, and the role of diagnostic tools in guiding evidence-based interventions. The review further examines non-anthelmintic strategies such as grazing management, nutritional supplementation, selective breeding, and integrated parasite management practices adapted from small ruminant systems. Across studies, young and immunologically developing cattle experience the greatest productivity losses, while mature animals contribute disproportionately to pasture contamination, reinforcing the importance of targeted control measures. Overall, the literature supports a transition toward integrated, diagnostics-driven parasite control programs that sustain productivity and animal well-being while preserving long-term anthelmintic efficacy.

F. Malentacchi, I. Mancini, Ivan Brandslund et al.

J. Neal, M. Barrington, R. Brull et al.

Yuan Tian, Min Zhou, Yitong Chen et al.

Patient face images provide a convenient mean for evaluating eye diseases, while also raising privacy concerns. Here, we introduce ROFI, a deep learning-based privacy protection framework for ophthalmology. Using weakly supervised learning and neural identity translation, ROFI anonymizes facial features while retaining disease features (over 98\% accuracy, $κ> 0.90$). It achieves 100\% diagnostic sensitivity and high agreement ($κ> 0.90$) across eleven eye diseases in three cohorts, anonymizing over 95\% of images. ROFI works with AI systems, maintaining original diagnoses ($κ> 0.80$), and supports secure image reversal (over 98\% similarity), enabling audits and long-term care. These results show ROFI's effectiveness of protecting patient privacy in the digital medicine era.

Zewen Sun, Ruoxiang Huang, Jiahe Feng et al.

Enhancing interrogation capabilities in Traditional Chinese Medicine (TCM) diagnosis through multi-turn dialogues and knowledge graphs presents a significant challenge for modern AI systems. Current large language models (LLMs), despite their advancements, exhibit notable limitations in medical applications, particularly in conducting effective multi-turn dialogues and proactive questioning. These shortcomings hinder their practical application and effectiveness in simulating real-world diagnostic scenarios. To address these limitations, we propose DoPI, a novel LLM system specifically designed for the TCM domain. The DoPI system introduces a collaborative architecture comprising a guidance model and an expert model. The guidance model conducts multi-turn dialogues with patients and dynamically generates questions based on a knowledge graph to efficiently extract critical symptom information. Simultaneously, the expert model leverages deep TCM expertise to provide final diagnoses and treatment plans. Furthermore, this study constructs a multi-turn doctor-patient dialogue dataset to simulate realistic consultation scenarios and proposes a novel evaluation methodology that does not rely on manually collected real-world consultation data. Experimental results show that the DoPI system achieves an accuracy rate of 84.68 percent in interrogation outcomes, significantly enhancing the model's communication ability during diagnosis while maintaining professional expertise.

Gustavo Correia, Victor Alves, Paulo Novais

Artificial Intelligence (AI) is revolutionizing emergency medicine by enhancing diagnostic processes and improving patient outcomes. This article provides a review of the current applications of AI in emergency imaging studies, focusing on the last five years of advancements. AI technologies, particularly machine learning and deep learning, are pivotal in interpreting complex imaging data, offering rapid, accurate diagnoses and potentially surpassing traditional diagnostic methods. Studies highlighted within the article demonstrate AI's capabilities in accurately detecting conditions such as fractures, pneumothorax, and pulmonary diseases from various imaging modalities including X-rays, CT scans, and MRIs. Furthermore, AI's ability to predict clinical outcomes like mechanical ventilation needs illustrates its potential in crisis resource optimization. Despite these advancements, the integration of AI into clinical practice presents challenges such as data privacy, algorithmic bias, and the need for extensive validation across diverse settings. This review underscores the transformative potential of AI in emergency settings, advocating for a future where AI and clinical expertise synergize to elevate patient care standards.