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Combined Immersive and Nonimmersive Virtual Reality With Mirror Therapy for Patients With Stroke: Systematic Review and Meta-Analysis of Randomized Controlled Trials

✇Journal of Medical Internet Research
By: Chaoran Gao · Yuan Chen · Yixin Wei · Yitong Qiu · Huiyan Song · Chenfan Gui · Qiang Gao
Background: Stroke frequently leads to various functional impairments. Both virtual reality (VR) and mirror therapy (MT) have shown efficacy in stroke rehabilitation. In recent years, the combination of these two approaches has emerged as a potential treatment for stroke patients. Objective: This systematic review and meta-analysis aim to evaluate the efficacy of combined immersive and non-immersive VR with MT in stroke rehabilitation. Methods: Five electronic databases were systematically searched for relevant articles published up to Jan. 2025. Randomized controlled trials (RCTs) that investigated combination treatment of VR and MT for participants with stroke were included. A grey literature search was also conducted. The risk of bias and the certainty of the evidence were assessed using the Cochrane collaboration’s tool and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guideline, respectively. Results: A total of 475 participants from 14 RCTs were included, of which 7 were eligible for meta-analysis. Meta-analysis revealed significant improvements in upper extremity (UE) motor function and hand dexterity, as evidenced by Fugl-Meyer assessment of upper extremity (FMA-UE) (MD 3.50, 95% CI 1.47 to 5.53; P=0.0007), manual function test (MFT) (MD 2.15, 95% CI 1.22 to 3.09; P6 months or not) revealed significant differences in the FMA-UE outcome. However, the pooled FMA-UE improvement did not consistently exceed the established minimal clinically important difference (MCID; 4.25–7.25), indicating that while statistically significant, the clinical meaningfulness of the observed effect remains uncertain. Narrative evidence also suggested potential benefits for lower extremity function, dynamic balance, and quality of life, though these findings were not meta-analyzed and should be interpreted with caution. Conclusions: Moderate-quality evidence supports VR-MT as a promising nonpharmacological intervention to improve upper extremity function and hand dexterity in stroke rehabilitation. While the intervention demonstrates statistically significant effects, it does not reach the minimum clinically important difference for the FMA-UE outcome. Preliminary descriptive evidence indicates possible advantages for lower extremity function, balance, and quality of life. Clinical Trial: PROSPERO CRD42024572150
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Opinion: STAT+: 5 things to consider to bring ambient digital scribes to clinical research

✇STAT
By: Elise Felicione

About a year ago, I logged into MyChart the day before my annual wellness visit and saw a new consent form. My doctor wanted to record our visit so artificial intelligence could create notes and update my medical record. I agreed. Just hours after my visit ended, a comprehensive, accurate visit summary appeared in MyChart.

As a career clinical trialist, I immediately wondered: What if this tool were used for trial visits? Could it enable a breakthrough in research or patient engagement? How will clinical research adapt to what could be a “new normal” in medical documentation?

Driven by a crisis of clinician burnout, ambient digital scribes (ADS) like the one my doctor used are being rapidly adopted. Studies suggest these digital scribes can reduce documentation burden, improve clinician efficiency, and potentially enhance the patient experience. By digitizing conversations in real time, these scribes open possibilities beyond routine care, particularly in clinical research and trials.

Continue to STAT+ to read the full story…

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Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemia

✇npj Digital Medicine
By: Yue Zhang · Jonathan Martinez · Bahar Tercan · Heikki Kuusanmäki · Frank Emmert-Streib · Jerome G. Chandraseelan · Amer Farea · Olli Yli-Harja · Caroline A. Heckman · David L. Gibbs · Vesteinn Thorsson · Ilya Shmulevich · Mika Kontro · Guangrong Qin · Boris Aguilar

npj Digital Medicine, Published online: 06 October 2025; doi:10.1038/s41746-025-01978-4

Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemia
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Efficient and accurate search in petabase-scale sequence repositories

✇Nature - Issue - nature.com science feeds
By: Mikhail Karasikov · Harun Mustafa · Daniel Danciu · Oleksandr Kulkov · Marc Zimmermann · Christopher Barber · Gunnar Rätsch · André Kahles

Nature, Published online: 08 October 2025; doi:10.1038/s41586-025-09603-w

MetaGraph enables scalable indexing of large sets of DNA, RNA or protein sequences using annotated de Bruijn graphs.
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Stop treating code like an afterthought: record, share and value it

✇Nature - Issue - nature.com science feeds
By: Roberto Di Cosmo · Sabrina Granger · Konrad Hinsen · Nicolas Jullien · Daniel Le Berre · Violaine Louvet · Camille Maumet · Clémentine Maurice · Raphaël Monat · Nicolas P. Rougier

Nature, Published online: 07 October 2025; doi:10.1038/d41586-025-03196-0

Scientists, research institutions, funders, libraries and publishers must all improve software practices.
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HALO: hierarchical causal modeling for single cell multi-omics data

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Haiyi Mao · Minxue Jia · Marissa Di · Eleanor Valenzi · Xiaoyu Tracy Cai · Robert Lafyatis · Kun Zhang · Panayiotis V Benos

Nat Commun. 2025 Oct 7;16(1):8892. doi: 10.1038/s41467-025-63921-1.

ABSTRACT

Though open chromatin may promote active transcription, gene expression responses may not be directly coordinated with changes in chromatin accessibility. Most existing methods for single-cell multi-omics data focus only on learning stationary, shared information among these modalities, overlooking modality-specific information delineating cellular states and dynamics resulting from causal relations among modalities. To address this, the epigenome-transcriptome relationship can be characterized in relation to time as coupled (changing dependently) or decoupled (changing independently). We propose the framework HALO, adopting a causal approach to model these temporal causal relations on two levels. On the representation level, HALO factorizes these two modalities into both coupled and decoupled latent representations, revealing their dynamic interplay. On the individual gene level, HALO matches gene-peak pairs and characterizes their changes over time. HALO discovers analogous biological functions between modalities, distinguishes epigenetic factors for lineage specification, and identifies temporal cis-regulation interactions relevant to cellular differentiation and human diseases.

PMID:41057364 | PMC:PMC12504611 | DOI:10.1038/s41467-025-63921-1

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Pathobiology and Genetics

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Christina A Eichstaedt · Andrea Olschewski · Lars Harbaum · Sebastian Cordes · Peter Dorfmüller · Vasile Foris · Wolfgang M Kuebler · Grazyna Kwapiszewska · Soni Pullamsetti · Natascha Sommer · Christoph Tabeling · Norbert Weissmann · Anja Zoll · Katrin Hinderhofer · Ralph T Schermuly

Pneumologie. 2025 Oct;79(10):701-711. doi: 10.1055/a-2625-4648. Epub 2025 Oct 6.

ABSTRACT

Genetics and pathobiology were addressed at the 7th World Symposium on Pulmonary Hypertension in Task Forces 2 and 3. The Genetics Task Force also focused on precision medicine approaches, and the Pathobiology working group concentrated heavily on new omics technologies. Therefore, the following not only summarises the current state of knowledge on genetics, genetic testing methods, and molecular pathophysiological changes, but also places it in context and critically discusses it. In addition, the importance of national and international biobanks and cohorts, as well as the active involvement of patients and families, is emphasized.

PMID:41052524 | DOI:10.1055/a-2625-4648

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The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment

✇Journal of Medical Internet Research
By: Ivana Nanevski · Sebastian Jäger · Matthias Schulte-Althoff · Eva-Maria Behnke · Daniel Fürstenau · Felix Biessmann
Background: With 28%-35% of individuals aged 65 years and older experiencing incidents of falling, falls are the second leading cause of unintentional injury–related deaths globally. Limited availability of clinical staff often impedes the timely detection and prevention of potential falls. Advances in artificial intelligence (AI) could complement existing fall risk assessment and help better allocate nursing care resources. Yet, many studies are based on small datasets from a single institution, which can restrict the generalizability of the model, and do not investigate important aspects in AI model development, such as fairness across demographic groups. Objective: This study aimed to provide a comprehensive empirical evaluation of the potential of AI in nursing care, focusing on the case of fall risk prediction. To account for demographic and contextual differences in fall incidences, we analyze data from a university and a geriatric hospital in Germany. To the best of our knowledge, these are the largest fall risk prediction datasets to date with heterogeneous data distributions. We focus on 3 key objectives. First, does AI help in improving fall risk prediction? Second, how can AI models be trained safely across different hospitals? Finally, are these models fair? Methods: This study used 2 datasets for fall risk prediction: one from a university hospital with 931,726 participants, 10,442 of whom experienced falls, and another from a geriatric hospital with 12,773 participants, 1728 of whom have fallen. State-of-the-art AI models were trained with 3 approaches, including 2 decentralized learning paradigms. First, separate models were trained on data from each hospital; second, models were retrained on the respective other dataset; and federated learning (FL) was applied to both datasets. The performance of these models was compared with the rule-based systems as implemented in clinical practice for fall risk prediction. Additional analyses were conducted to test for model fairness. Results: Our findings demonstrate that AI models consistently outperform rule-based systems across all experimental setups, with the area under the receiver operating characteristic curve of 0.735 (90% CI 0.727-0.744) for the geriatric hospital, and 0.926 (90% CI 0.924-0.928) for the university hospital. FL did not improve the fall risk prediction in this setting. Our fairness analysis ruled out disparities in model performance between different sex groups, but we found fairness infringements across age groups. Conclusions: This study demonstrates that AI models consistently outperform traditional rule-based systems across heterogeneous datasets in predicting fall risk. However, it also reveals the challenges related to demographic shifts and label distribution imbalances, which limited the FL models’ ability to generalize. While the fairness analysis indicated fair results across sex subgroups, age-related disparities emerged. Addressing data imbalances and ensuring broader representation across demographic groups will be crucial for developing more fair and generalizable models.
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Quality of Cancer-Related Information on New Media (2014-2023): Systematic Review and Meta-Analysis

✇Journal of Medical Internet Research
By: Xue-Jing Liu · Danny Valdez · Maria A Parker · Andi Mai · Eric R Walsh-Buhi
Background: New media have become vital sources of cancer-related health information. However, concerns about the quality of that information persist. Objective: This study aims to identify characteristics of studies considering cancer-related information on new media (including social media and artificial intelligence chatbots); analyze patterns in information quality across different platforms, cancer types, and evaluation tools; and synthesize the quality levels of the information. Methods: We systematically searched PubMed, Web of Science, Scopus, and Medline databases for peer-reviewed studies published in English between 2014 and 2023. The validity of the included studies was assessed based on risk of bias, reporting quality, and ethical approval, using the Joanna Briggs Institute Critical Appraisal and the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklists. Features of platforms, cancer types, evaluation tools, and trends were summarized. Ordinal logistic regression was used to estimate the associations between the conclusion of quality assessments and study features. A random-effects meta-analysis of proportions was conducted to synthesize the overall levels of information quality and corresponding 95% CIs for each assessment indicator. Results: A total of 75 studies were included, encompassing 297,519 posts related to 17 cancer types across 15 media platforms. Studies focusing on video-based media (odds ratio [OR] 0.02, 95% CI 0.01-0.12), rare cancers (OR 0.32, 95% CI 0.16-0.65), and combined cancer types (OR 0.04, 95% CI 0.01-0.14) were statistically less likely to yield higher quality conclusions compared to those on text-based media and common cancers. The pooled estimates reported moderate overall quality (DISCERN 43.58, 95% CI 37.80-49.35; Global Quality Score 49.91, 95% CI 43.31-56.50), moderate technical quality (Journal of American Medical Association Benchmark Criteria 46.13, 95% CI 38.87-53.39; Health on the Net Foundation Code of Conduct 49.68, 95% CI 19.68-79.68), moderate-high understandability (Patient Education Material Assessment Tool for Understandability 66.92, 95% CI 59.86-73.99), moderate-low actionability (Patient Education Materials Assessment Tool for Actionability 37.24, 95% CI 18.08-58.68; usefulness 48.86, 95% CI 26.24-71.48), and moderate-low completeness (34.22, 95% CI 27.96-40.48). Furthermore, 27.15% (95% CI 21.36-33.35) of posts contained misinformation, 21.15% (95% CI 8.96-36.50) contained harmful information, and 12.46% (95% CI 7.52-17.39) contained commercial bias. Publication bias was detected only in misinformation studies (Egger test: bias –5.67, 95% CI –9.63 to –1.71; P=.006), with high heterogeneity across most outcomes (I²>75%). Conclusions: Meta-analysis results revealed that the overall quality of cancer-related information on social media and artificial intelligence chatbots was moderate, with relatively higher scores for understandability but lower scores for actionability and completeness. A notable proportion of content contained misleading, harmful, or commercially biased information, posing potential risks to users. To support informed decision-making in cancer care, it is essential to improve the quality of information delivered through these media platforms. Trial Registration: PROSPERO CRD420251058032; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251058032
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Evaluating Large Language Models and Retrieval-Augmented Generation Enhancement for Delivering Guideline-Adherent Nutrition Information for Cardiovascular Disease Prevention: Cross-Sectional Study

✇Journal of Medical Internet Research
By: Vijaya Parameswaran · Jenna Bernard · Alec Bernard · Neil Deo · Sean Tsung · Kalle Lyytinen · Christopher Sharp · Fatima Rodriguez · David J Maron · Rajesh Dash
Background: Cardiovascular disease (CVD) remains the leading cause of death worldwide, yet many web-based sources on cardiovascular (CV) health are inaccessible. Large language models (LLMs) are increasingly used for health-related inquiries and offer an opportunity to produce accessible and scalable CV health information. However, because these models are trained on heterogeneous data, including unverified user-generated content, the quality and reliability of food and nutrition information on CVD prevention remain uncertain. Recent studies have examined LLM use in various health care applications, but their effectiveness for providing nutrition information remains understudied. Although retrieval-augmented generation (RAG) frameworks have been shown to enhance LLM consistency and accuracy, their use in delivering nutrition information for CVD prevention requires further evaluation. Objective: To evaluate the effectiveness of off-the-shelf and RAG-enhanced LLMs in delivering guideline-adherent nutrition information for CVD prevention, we assessed 3 off-the-shelf models (ChatGPT-4o, Perplexity, and Llama 3-70B) and a Llama 3-70B+RAG model. Methods: We curated 30 nutrition questions that comprehensively addressed CVD prevention. These were approved by a registered dietitian providing preventive cardiology services at an academic medical center and were posed 3 times to each model. We developed a 15,074-word knowledge bank incorporating the American Heart Association’s 2021 dietary guidelines and related website content to enhance Meta’s Llama 3-70B model using RAG. The model received this and a few-shot prompt as context, included citations in a Context Source section, and used vector similarity to align responses with guideline content, with the temperature parameter set to 0.5 to enhance consistency. Model responses were evaluated by 3 expert reviewers against benchmark CV guidelines for appropriateness, reliability, readability, harm, and guideline adherence. Mean scores were compared using ANOVA, with statistical significance set at P<.05. interrater agreement was measured using the cohen coefficient and readability estimated flesch-kincaid score. results: llama model scored higher than perplexity gpt-4o models on reliability appropriateness guideline adherence showed no harm.>70%; P<.001 indicated high reviewer agreement. conclusions: the llama model outperformed off-the-shelf models across all measures with no evidence of harm although responses were less readable due to technical language. scored lower on and produced some harmful responses. these findings highlight limitations demonstrate that rag system integration can enhance llm performance in delivering evidence-based dietary information.>
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The Role of Data in Public Health and Health Innovation: Perspectives on Social Determinants of Health, Community-Based Data Approaches, and AI

✇Journal of Medical Internet Research
By: Kaakpema Yelpaala · Michael Christopher Gibbons · Ines Maria Vigil · Jennifer Leaño · Terika McCall · Ijeoma Opara · Anne Zink · Marcella Nunez-Smith · Bhramar Mukherjee · Megan Ranney
Public health is undergoing profound transformation driven by data from the global health sector and related fields. To address systemic health disparities, scholars and practitioners are increasingly applying a data equity lens, an approach that has become even more urgent as the United States faces the erosion of public health data infrastructure. This paper summarizes insights from an April 2024 convening by the Yale School of Public Health—The Role of Data in Public Health Equity and Innovation—with intersectoral stakeholders from academia, government (local, state, and federal), healthcare, and private industry. The convening included keynote presentations and roundtables regarding the depiction of social determinants of health (SDOH) in data; effects of artificial intelligence (AI) on health data equity; and community-based models for data, providing a framework for cross-cutting discussions. Through a narrative synthesis, themes were identified and synthesized from systematically gathered information from presentations and roundtables. This process led to a set of actionable, cross-cutting recommendations to guide inclusive and impactful data practices for policymakers, public health professionals, and health innovators across diverse contexts: (1) Enable big data and interoperability connecting SDOH and health outcomes; (2) Include diverse, non-technical voices in AI and health discussions; (3) Fund research on data equity and AI in health sciences; (4) Modernize Health Insurance Portability and Accountability Act (HIPAA) with new guidelines for AI and big data; and (5) Research and conceptual frameworks are needed to elucidate interconnections between data equity and health equity.
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Generative artificial intelligence in medicine

✇Nature Medicine
By: Zhen Ling Teo · Arun James Thirunavukarasu · Kabilan Elangovan · Haoran Cheng · Prasanth Moova · Brian Soetikno · Christopher Nielsen · Andreas Pollreisz · Darren Shu Jeng Ting · Robert J. T. Morris · Nigam H. Shah · Curtis P. Langlotz · Daniel Shu Wei Ting

Nature Medicine, Published online: 06 October 2025; doi:10.1038/s41591-025-03983-2

This Review summarizes recent technical advancements in generative AI, outlines how new models might improve healthcare and discusses validation approaches—using lessons from recent successes and failures in the field.
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Single-cell and multi-omics analysis identifies TRIM9 as a key ubiquitination regulator in pancreatic cancer

✇(Multiomics OR Omics) AND (Pancreatic)
By: Liang Chen · Xiaomei Ying · Chenfeng Ma · Qikai Tang · Shuai Chen

Front Immunol. 2025 Sep 19;16:1631708. doi: 10.3389/fimmu.2025.1631708. eCollection 2025.

ABSTRACT

This study investigates the role of ubiquitination-related genes in pancreatic cancer (PC) using single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and multi-omics approaches. scRNA-seq data (GSE155698) from PC samples identified 12 cell types, with endothelial cells exhibiting high ubiquitination scores (High_ubiquitin-Endo) and enriched interactions with fibroblasts/macrophages via WNT, NOTCH, and integrin pathways. Spatial transcriptomics (GSE235315) validated cell-type localization. Mendelian randomization (SMR) analysis prioritized TRIM9 as a PC-protective gene, downregulated in tumors and correlated with better survival. WGCNA revealed TRIM9-co-expressed modules linked to prognosis. A machine learning-based prognostic model (CoxBoost+RSF) integrating seven genes (TSPAN6, TSC1, RNF167, PBXIP1, LRRC49, KATNAL2, IGF2BP2) stratified patients into high/low-risk groups with distinct survival, mutation burdens, and immune infiltration. TRIM9 overexpression suppressed PC cell proliferation/migration in vitro, while knockdown enhanced malignancy. Mechanistically, TRIM9 promoted K11-linked ubiquitination and proteasomal degradation of HNRNPU, dependent on its RING domain. In vivo, TRIM9 overexpression reduced tumor growth, rescued by HNRNPU co-expression. Integrated analyses highlight TRIM9 as a tumor suppressor and prognostic biomarker, mediated via ubiquitination-dependent regulation of HNRNPU stability. This work provides insights into ubiquitination-driven PC pathogenesis and therapeutic targeting.

PMID:41050689 | PMC:PMC12491318 | DOI:10.3389/fimmu.2025.1631708

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A computational medicine framework integrating multi-omics, systems biology, and artificial neural networks for Alzheimer's disease therapeutic discovery

✇(Multiomics OR Omics) AND (Pancreatic)
By: Yisheng Yang · Yizhu Diao · Lulu Jiang · Fanlu Li · Liye Chen · Ming Ni · Zheng Wang · Hai Fang

Acta Pharm Sin B. 2025 Sep;15(9):4411-4426. doi: 10.1016/j.apsb.2025.07.018. Epub 2025 Jul 16.

ABSTRACT

The translation of genetic findings from genome-wide association studies into actionable therapeutics persists as a critical challenge in Alzheimer's disease (AD) research. Here, we present PI4AD, a computational medicine framework that integrates multi-omics data, systems biology, and artificial neural networks for therapeutic discovery. This framework leverages multi-omic and network evidence to deliver three core functionalities: clinical target prioritisation; self-organising prioritisation map construction, distinguishing AD-specific targets from those linked to neuropsychiatric disorders; and pathway crosstalk-informed therapeutic discovery. PI4AD successfully recovers clinically validated targets like APP and ESR1, confirming its prioritisation efficacy. Its artificial neural network component identifies disease-specific molecular signatures, while pathway crosstalk analysis reveals critical nodal genes (e.g., HRAS and MAPK1), drug repurposing candidates, and clinically relevant network modules. By validating targets, elucidating disease-specific therapeutic potentials, and exploring crosstalk mechanisms, PI4AD bridges genetic insights with pathway-level biology, establishing a systems genetics foundation for rational therapeutic development. Importantly, its emphasis on Ras-centred pathways-implicated in synaptic dysfunction and neuroinflammation-provides a strategy to disrupt AD progression, complementing conventional amyloid/tau-focused paradigms, with the future potential to redefine treatment strategies in conjunction with mRNA therapeutics and thereby advance translational medicine in neurodegeneration.

PMID:41049755 | PMC:PMC12491700 | DOI:10.1016/j.apsb.2025.07.018

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