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Received — 16 September 2025 ⏭ Favourites

Digital Health Technology Infrastructure Challenges to Support Health Equity in the United States: Scoping Review

✇Journal of Medical Internet Research
By: Shongkour Roy · Stella T Lartey · Polina Durneva · Niharika Jha · Michael Arthur Ofori · Zebunnesa Zeba · Stella Dockery · Nichole Saulsberry Scarboro · Michelle Taylor · Ashish Joshi
16 September 2025 at 05:15
Background: Even though Digital Health Technology (DHT) is widely utilized in the United States (U.S.) at both hospital provider and individual levels, it is beset with several challenges that have contributed to inequities in the health service delivery. Previous studies have shown that health inequities observed may be amplified many by DHT requirements. Objective: The objectives of this scoping review are aimed at synthesizing information on DHT inequities by exploring evidence that describes DHT infrastructure needs focused on promoting health equity in the U.S. and identifying key challenges at both the individual/patient level and at the health service provider's level. Methods: We adapted Arksey and O'Malley's scoping review guidelines in our review. We searched PubMed, Web of Science, CINAHL, and PsycINFO were searched. We also conducted supplementary searches on Google Scholar. The inclusion criteria were peer-reviewed publications that broadly conceptualize or analyze DHT infrastructure from a health equity perspective and the challenges of DHT requirements between 2020 and 2024. Following a full-text screening using eligibility criteria such as studies were included if they examined DHT infrastructure in the U.S. from a health equity perspective, discussed health disparities resulting from DHT interventions, or investigated the variables influencing health inequities connected to DHT. Two researchers evaluated each citation’s individually at the title and abstract levels. Thematic approach and qualitative analysis determined this scoping review’s outcome. Results: Of the 628 research articles from the search, 27 were included in the analysis based on the inclusion criteria. In this review, we discussed factors such as elderly population, education, race, ethnicity, and socioeconomic status leading to health inequities in DHT. Patients and Service providers challenges that exist in health inequities related to DHT. The most common challenges for service providers were infrastructure and technical issues such as inadequate integration with existing workflows, user-unfriendly health information exchange (HIE) interfaces, and lack of skilled staff, while for individuals or patients, this included limited broadband internet access, cultural or linguistic appropriateness, and access to digital tools. Conclusions: The study identified that in the U.S., DHT is an essential part of the delivery of health services, yet it is saddled with key challenges leading to health inequities. Finding pragmatic solutions to these challenges can improve health equity in DHT.

Prompt Engineering in Clinical Practice: Tutorial for Clinicians

✇Journal of Medical Internet Research
By: Jialin Liu · Fang Liu · Changyu Wang · Siru Liu
16 September 2025 at 03:00
Large language models (LLMs), such as OpenAI’s GPT series and Google’s PaLM, are transforming healthcare by improving clinical decision-making, enhancing patient communication, and simplifying administrative tasks. However, their performance relies heavily on prompt design, where small changes in wording or structure can greatly impact output quality. This poses a challenge for clinicians who are not experts in natural language processing (NLP). This tutorial combines prompt engineering techniques tailored for clinical use, covering methods like zero-shot, few-shot, chain-of-thought, and meta-prompting. We examine four critical dimensions (accuracy, bias mitigation, privacy protection, and workflow integration) through clinical case studies grounded in real-world practice. We provide actionable guidance on defining objectives, applying core principles, iteratively refining prompts, and integrating them into interoperable electronic health record (EHR) systems. This framework helps clinicians leverage LLMs to improve decision-making, streamline documentation, and enhance patient communication while maintaining ethical standards and ensuring patient safety.

Do startups still need Silicon Valley? Leaders at SignalFire, Lago, and Revolution debate at TechCrunch Disrupt 2025

✇TechCrunch
By: TechCrunch Events
24 September 2025 at 01:15
Does Silicon Valley still give founders an edge? At TechCrunch Disrupt 2025, Anh-Tho Chuong (Lago), David Hall (Revolution), and Tawni Nazario-Cranz (SignalFire) debate whether location still drives startup success.
Received — 15 September 2025 ⏭ Favourites

Interventions Based on Biofeedback Systems to Improve Workers’ Psychological Well-Being, Mental Health, and Safety: Systematic Literature Review

✇Journal of Medical Internet Research
By: Simão Ferreira · Matilde A Rodrigues · Catarina Mateus · Pedro Pereira Rodrigues · Nuno Barbosa Rocha
13 September 2025 at 04:00
Background: In modern, high-speed work settings, the significance of mental health disorders is increasingly acknowledged as a pressing health issue, with potential adverse consequences for organizations, including reduced productivity and increased absenteeism. Over the past few years, various mental health management solutions, such as biofeedback applications, have surfaced as promising avenues to improve employees’ mental well-being. However, most studies on these interventions have been conducted in controlled laboratory settings. Objective: This review aimed to systematically identify and analyze studies that implemented biofeedback-based interventions in real-world occupational settings, focusing on their effectiveness in improving psychological well-being and mental health. Methods: A systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed and EBSCO databases for studies published between 2012 and 2024. Inclusion criteria were original peer-reviewed studies that focused on employees and used biofeedback interventions to improve mental health or prevent mental illness. Exclusion criteria included nonemployee samples, lack of a description of the intervention, and low methodological quality (assessed using the Physiotherapy Evidence Database [PEDro] checklist). Data were extracted on study characteristics, intervention type, physiological and self-reported outcomes, and follow-up measures. Risk of bias was assessed, and VOSviewer was used to visualize the distribution of research topics. Results: A total of 9 studies met the inclusion criteria. The interventions used a range of delivery methods, including traditional biofeedback, mobile apps, mindfulness techniques, virtual reality, and cerebral blood flow monitoring. Most studies focused on breathing techniques to regulate physiological responses (eg, heart rate variability and respiratory sinus arrhythmia) and showed reductions in stress, anxiety, and depressive symptoms. Mobile and app-directed interventions appeared particularly promising for improving resilience and facilitating recovery after stress. Of the 9 studies, 8 (89%) reported positive outcomes, with 1 (11%) study showing initial increases in stress due to logistical limitations in biofeedback access. Sample sizes were generally small, and long-term follow-up data were limited. Conclusions: Biofeedback interventions in workplace settings show promising short-term results in reducing stress and improving mental health, particularly when incorporating breathing techniques and user-friendly delivery methods such as mobile apps. However, the field remains underexplored in occupational contexts. Future research should address adherence challenges, scalability, cost-effectiveness, and long-term outcomes to support broader implementation of biofeedback as a sustainable workplace mental health strategy.

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progression

✇(Multiomics OR Omics) AND (Pancreatic)
By: Rebecca Melton · Sara Jimenez · Weston Elison · Luca Tucciarone · Abigail Howell · Gaowei Wang · Denise Berti · Elisha Beebe · Michael Miller · Chun Zeng · Carolyn McGrail · Kennedy VanderStel · Katha Korgaonkar · Ruth Elgamal · Hannah Mummey · Joshua Chiou · Emily Griffin · Irina Kusmartseva · Mark Atkinson · Sebastian Preissl · Fabian J Theis · Maike Sander · Kyle J Gaulton
10 September 2025 at 18:00

Sci Adv. 2025 Sep 12;11(37):eady0080. doi: 10.1126/sciadv.ady0080. Epub 2025 Sep 10.

ABSTRACT

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here, we performed single-nucleus multiomics and spatial transcriptomics in up to 32 nondiabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine subtypes. β, Acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in β cells. Last, single-cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting β cell regulation. Overall, these results revealed drivers of T1D in the pancreas, which form the basis for therapeutic targets for disease prevention.

PMID:40929272 | PMC:PMC12422192 | DOI:10.1126/sciadv.ady0080

Organoids in Genetic Disorders: from Disease Modeling to Translational Applications

✇Omics In Lung
By: Yuanhang Zhu · Nanshan Lin · Juan Li · Haoqian Zhang · Ping Zhang · Xin Cheng · Qian Yang · Ling Liu
10 September 2025 at 18:00

Stem Cell Rev Rep. 2025 Sep 11. doi: 10.1007/s12015-025-10973-x. Online ahead of print.

ABSTRACT

The emergence of organoid models has significantly bridged the gap between traditional cell cultures/animal models and authentic human disease states, particularly for genetic disorders, where their inherent genetic fidelity enables more biologically relevant research directions and enhances translational validity. This review systematically analyzes established organoid models of genetic diseases across organs (e.g., brain, eye, kidney, lung, and heart), highlighting their pivotal roles in identifying novel pathogenic genes, elucidating disease mechanisms, and advancing therapeutic strategies such as drug screening platforms, gene-editing therapies, and organ transplantation strategies. Furthermore, we critically address current limitations-including challenges in recapitulating complex pathologies and scaling production-while underscoring their potential for personalized medicine through multi-omics integration and bioengineering innovations. Although the scope of "genetic diseases" is broad, this synthesis focuses on disorders with well-defined inheritance patterns, such as monogenic disorders, copy number variations (CNVs), and aneuploidies. Despite covering only a subset of these conditions, this review aims to provide researchers with a comprehensive overview of the field, emphasizing how organoid-based approaches could accelerate both mechanistic discoveries and clinical translation in genetic disease research.

PMID:40931310 | DOI:10.1007/s12015-025-10973-x

Received — 9 September 2025 ⏭ Favourites

Liquid biopsy- A pivotal test to help navigate clinical decisions at a precision center in India!

✇MRD
By: Sewanti Limaye · Aditya Shreenivas · Darshana Patil · Janani Sambath · Irene A George · Anjali Parab · Aakriti Datta · Punita Jain · Shambhavi Singh · Chetan Madre · Pritam Kataria · Darshit Shah · Niyati Shah · Ruturaj Deshpande · Rajan Datar · Prashant Kumar
8 September 2025 at 18:00

J Liq Biopsy. 2025 Aug 9;9:100323. doi: 10.1016/j.jlb.2025.100323. eCollection 2025 Sep.

ABSTRACT

Liquid biopsy, specifically circulating tumor DNA (ctDNA) analysis, has emerged as a transformative tool in precision oncology, providing real-time, minimally invasive characterizations of the tumor and tumor dynamics. While tissue biopsy is a critical tool for baseline diagnosis of malignancy, it is often limited by sampling constraints and an inability to capture tumor heterogeneity. In this study, we explored the clinical utility of serial ctDNA testing in guiding therapeutic decisions across a cohort of 30 patients with diverse solid tumors. Our real-world analysis demonstrates that ctDNA profiling meaningfully influenced treatment escalation, de-escalation, disease monitoring, and early relapse prediction. Cases where ctDNA positivity indicated minimal residual disease prompted timely escalation of therapy, while ctDNA clearance allowed safe treatment de-intensification, minimizing toxicity without compromising outcomes. Longitudinal ctDNA monitoring provided a dynamic, non-invasive method for assessing treatment response and detecting recurrence months before radiological progression. Our study highlights the potential of integrating liquid biopsy into routine clinical practice to enable dynamic treatment monitoring, early detection of therapeutic resistance, and more informed, personalized decision-making across various cancer types.

PMID:40919127 | PMC:PMC12409318 | DOI:10.1016/j.jlb.2025.100323

Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review

✇Journal of Medical Internet Research
By: David Guo · Kim-Kwang Raymond Choo
9 September 2025 at 05:30
Background: Adverse drug reactions (ADRs) pose significant challenges in healthcare, where early prevention is vital for effective treatment and patient safety. Objective: Traditional supervised learning methods are limited in addressing healthcare data, which is often unstructured, heavily regulated, and involves restricted access to sensitive personal information. Methods: The integration of Federated Learning (FL) and Large Language Model (LLM) offers a promising solution to these challenges since FL supports the distributed training on edge device with limited resources and the capability of LLM to deal with unstructured healthcare data. Additionally, client models trained on the edge device can be merged into a global model on the server, preserving data privacy. Results: Natural Language Processing (NLP) technologies underpinning LLM provide a full set of tools that can readily be used to process unstructured ADR as input, enabling LLM to predict ADR outcome effectively. The ADR output space can be discrete labels, unstructured texts, or both. Conclusions: This review presents a scoping review following the PRISMA protocol on the applications of Federated Large Language Model (FedLLM) in ADR prediction, aiming to explore future research venue on ADR applications

The WHO global landscape of cancer clinical trials

✇Nature Medicine
By: Raffaella Casolino · Lambed Tatah · Sarah Charnaud · Marilina Santero · Andrè Ilbawi · Anna Laura Ross
9 September 2025 at 08:00

Nature Medicine, Published online: 09 September 2025; doi:10.1038/s41591-025-03926-x

This Review of the WHO’s International Clinical Trials Registry Platform presents a snapshot of the global cancer trial landscape and provides critical empirical evidence to inform policy, practice and investment.

Building the world’s first truly global medical foundation model

✇Nature Medicine
By: Yih Chung Tham · Jocelyn Hui Lin Goh · Paul Nderitu · Yukun Zhou · An Ran Ran · Sahana Srinivasan · Gabriel Dawei Yang · Gatera Fiston Kitema · Polly Rawlinson · Hongyang Jiang · Ke Zou · Carol Y. Cheung · Pearse A. Keane · Hatem M. Abdelakher · Eberechukwu Achigbu · Referano Agustiawan · Islam Sh Ahmed · Shehab Ahmad Al-Abed · Minhaj Nur Alam · Sura Yousef Alaroud · Abdulrahman F. Albloushi · Tsedeke Asaminew Alemu · Nchifor Alice · Faisal Alkathery · Otim Ambrose · Kwesi N. Amissah-Arthur · Damara Andalia · Fares Antaki · Louis Arnould · Simon Arunga · Olufemi Emmanuel Babalola · Vilma Jurate Balciuniene · Covadonga Bascaran · Ciara Bergin · Carolina Bernal-Morales · Ansgar Beuse · Mukharram M. Bikbov · Guzel M. Bikbova · Iryna Blagun · Damir Bosnar · Rupert Bourne · Tasanee Braithwaite · Iva Bulat · Philip Burgess · Matthew J. Burton · Avshalom Caspi · Ling-Ping Cen · Manju Chandran · Robert T. Chang · Kao-Jung Chang · Varun Chaudhary · David Ziyou Chen · Haoyu Chen · Xi Chen · Xinjian Chen · Xuejing Chen · Youxin Chen · Qingyu Chen · Ching-Yu Cheng · Kai Xiong C
8 September 2025 at 08:00

Nature Medicine, Published online: 08 September 2025; doi:10.1038/s41591-025-03859-5

Building the world’s first truly global medical foundation model

Opinion: Bringing AI to medicine requires philosophers, cognitive scientists, and ethicists

✇STAT
By: Charlotte Blease
9 September 2025 at 16:30

“My watch saved my life.”

Liam — not his real name — is a 75-year-old retired teacher in Boston. Two years ago, his son-in-law gave him an Apple Watch. Soon after, it began flagging something strange: possible atrial fibrillation.

Read the rest…

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Antibody–bottlebrush prodrug conjugates for targeted cancer therapy

✇Nature Biotechnology - Issue - nature.com science feeds
By: Bin Liu · Hung V.-T. Nguyen · Yivan Jiang · Aiden X. Wang · Valerie Lensch · Zehao Sun · Zane H. Boyer · Philip A. Raftopoulos · Yutong Dai · Piper L. MacNicol · Yuyan Wang · Nidhi Jyotsana · Wencong Wang · Sachin Bhagchandani · Sanjana Hemdev · Peyton Shieh · Samantha L. Kristufek · Magalie Boucher · Michael Downes · Ronald M. Evans · David W. C. MacMillan · Jeremiah A. Johnson
9 September 2025 at 08:00

Nature Biotechnology, Published online: 09 September 2025; doi:10.1038/s41587-025-02772-z

Antibody–bottlebrush conjugates expand the options for drug cargos compared to antibody–drug conjugates.
Received — 8 September 2025 ⏭ Favourites

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

✇Omics In Lung
By: Zijian Zhang · Xiang Wang · Chenyang Li · Robert Taylor Ripley · Jia Wu · Jianjun Zhang · Christopher I Amos · Chao Cheng
5 September 2025 at 18:00

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zijian Zhang · Xiang Wang · Chenyang Li · Robert Taylor Ripley · Jia Wu · Jianjun Zhang · Christopher I Amos · Chao Cheng
5 September 2025 at 18:00

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

Extracting Clinical Guideline Information Using Two Large Language Models: Evaluation Study

✇Journal of Medical Internet Research
By: Hsing-Yu Hsu · Lu-Wen Chen · Wan-Tseng Hsu · Yow-Wen Hsieh · Shih-Sheng Chang
6 September 2025 at 04:15
Background: The effective implementation of personalized pharmacogenomics (PGx) requires the integration of released clinical guidelines into decision support systems (CDSS) to facilitate clinical applications. Large language models (LLMs) can be valuable tools for automating information extraction and updates. Objective: To assess the effectiveness of repeated cross-comparisons and an agreement-threshold strategy in two advanced LLMs as supportive tools for updating information. Methods: The study evaluated the performance of two LLMs, GPT-4o and Gemini-1.5-Pro, in extracting PGx clinical guidelines and comparing their outputs with expert-annotated evaluations. The two LLMs classified 385 PGx clinical guidelines, with each recommendation tested 20 times per model. Accuracy was assessed by comparing the results with manually labeled data. Two prospectively defined strategies were employed to identify inconsistent predictions. The first involved repeated cross-comparison, flagging discrepancies between the most frequent classifications from each model. The second employed a consistency threshold strategy, which designated predictions appearing in less than 60% of the 40 combined outputs as unstable. Cases flagged by either strategy were subjected to manual review. This study also estimated the overall cost of model usage and was conducted between October 1 and November 30, 2024. Results: GPT-4o and Gemini-1.5-Pro yielded reproducibility rates of 97.8% (7,534/7,700) and 98.9% (7,612/7,700), respectively, based on the most frequent classification for each query. Compared with expert labels, GPT-4o achieved 93.5% accuracy (Cohen’s Kappa=0.90; P<.001 and gemini-1.5-pro accuracy kappa="0.89;" p both models demonstrated high overall performance with comparable weighted average f1 scores gemini: the generated consistent predictions for of guideline items reducing need manual review by among these agreed-upon cases only one diverged from expert labels. applying a predefined agreement-threshold strategy further reduced number priority to although error rate slightly increased inconsistencies identified through methods prompted prioritization minimize errors enhance clinical applicability. total combined cost using llms was conclusions: findings suggest that two can effectively streamline pgx integration into cdss while maintaining minimal cost. selective remains necessary this approach offers practical scalable solution classification in workflows.>
Received — 5 September 2025 ⏭ Favourites

Scalable generation and functional classification of genetic variants in inborn errors of immunity to accelerate clinical diagnosis and treatment

✇Cell
By: Zachary H. Walsh · Chris J. Frangieh · Neeharika Kothapalli · Jay Levy · Clarissa K. Heck · Johannes C. Melms · Ron S. Gejman · Parin Shah · Jared M. Pollard · Akul Naik · Sarah L. Grauman · Lei Haley Huang · Ashley Lee · Dusan Bogunovic · Joshua D. Milner · Benjamin Izar
20 June 2025 at 08:00
In lieu of traditional genetic variant testing approaches, an approach using scalable variant classification in primary human T cells with a clinically relevant readout can inform rapid diagnosis and treatment of inborn errors of immunity.
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