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Stem cell-based models of the human brain benefit from biospecimens that can be used for a broad range of future research. But current regulations do not address the desire of research participants to remain engaged beyond initial biospecimen donation. We present practicable strategies for engaging participants while preserving scientific potential.
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http://dx.doi.org/10.1016/j.stemcr.2025.102546 | DOI Listing |
Open Med (Wars)
August 2025
Research Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Roma, 00128, Italy.
Introduction: Advance care planning is a critical process that brings patients, their families, and healthcare providers together to set goals and outline preferences for future medical treatments, especially when chronic or terminal illnesses are involved. Recently, artificial intelligence has begun playing a key role in shared decision making, offering personalized recommendations based on detailed data analysis to help refine treatment decisions.
Objective: This review explores Artificial Intelligence's role in shared decision making, noting its potential to enhance treatment precision, reduce the workload for healthcare providers, and empower patients to engage more actively in their cares.
Ren Fail
December 2025
Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Large language models (LLMs) represent a transformative advance in artificial intelligence, with growing potential to impact chronic kidney disease (CKD) management. CKD is a complex, highly prevalent condition requiring multifaceted care and substantial patient engagement. Recent developments in LLMs-including conversational AI, multimodal integration, and autonomous agents-offer novel opportunities to enhance patient education, streamline clinical documentation, and support decision-making across nephrology practice.
View Article and Find Full Text PDFJ Clin Epidemiol
September 2025
Australian Living Evidence Collaboration, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
Objectives: Guideline developers have long recognised the importance of maintaining up-to-date guidelines to support evidence-based practice and policy, contributing to narrowing the gap between research generation and its application. This commentary reflects on key insights from the Journal of Clinical Epidemiology's Methods for Living Guidelines series and issues an open call for contributions aimed at advancing the development, implementation and evaluation of living guideline methods.
Methods: This commentary synthesises methodological innovations and practice experiences shared in the Methods for Living Guidelines series, highlighting emerging practices and lessons learned.
J Public Health (Oxf)
September 2025
Department of Preventive Medicine and Public Health, Faculty of Medicine, University of Granada, Avda. Dr. Jesús Canden Fábregas 11, 18016 Granada, Spain.
Background: Randomized clinical trials (RCTs) based on Mediterranean Diet (MedDiet) have reported that higher adherence is associated with better health outcomes. Our aim was to describe the perspectives and experiences of older adults in a MedDiet RCT for cardiovascular disease prevention.
Methods: Three focus groups on 25 participants from a MedDiet RCT, aged from 63 to 76 years old, were conducted after a conference on patient and public involvement in research at the University of Granada (Spain).
Neural Regen Res
September 2025
Shandong Co-Innovation Center of Classic TCM formula, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China.
Although many previous studies have highlighted the advances in prediction models, instruments for pathological and histological diagnosis and treatment, as well as individualized treatment modalities in mental disorders, these previous syntheses usually study the research outcomes separately and ignore the holistic integration of research regarding artificial intelligence technological approaches, data sets used and applications in mental health research. We used the BioBERT pretrained language model to systematically extract relevant information and develop an extensive knowledge graph that includes 3158 entities connected with 3248 different relationships. Our knowledge graph delineates essential artificial intelligence technological frameworks and explicitly maps out the relationships linking artificial intelligence methods, mental disorders, and diverse data modalities.
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