Pub Date : 2026-01-16DOI: 10.1038/s41746-026-02341-x
Anca Dobrean,Costina-Ruxandra Poetar,Ionuț-Stelian Florean,Raluca Balan,Gerhard Andersson
Internalizing problems are the most common mental health problems encountered in youths. This study investigated the efficacy of a transdiagnostic Internet-delivered intervention (REBTonAd), delivered over 6 weeks. Our study included Romanian youths (aged 11-17) with a primary diagnosis of an anxiety and/or depressive disorder. Eligible participants (N = 106; Mage = 12.83, SD = 1.63) were randomly assigned to the REBTonAd group (n = 53) or the waitlist condition (WL) (n = 53), with outcomes assessed at baseline, post-test, and 6-month follow-up. At the post-test, the remission rate and clinical reliable change indices were higher in the REBTonAd group. Internalizing problems were reduced more in the REBTonAd group, with a moderate effect size (standardized mean difference = -0.60, 95% CI -0.96 to -0.25). Future research needs to test the effectiveness of this intervention compared to disorder-specific treatments and investigate its cost-effectiveness. This trial was prospectively registered at ClinicalTrials.gov (NCT04179526).
{"title":"Transdiagnostic Internet-delivered intervention for children and adolescents with anxiety and depressive disorders: a randomized controlled trial.","authors":"Anca Dobrean,Costina-Ruxandra Poetar,Ionuț-Stelian Florean,Raluca Balan,Gerhard Andersson","doi":"10.1038/s41746-026-02341-x","DOIUrl":"https://doi.org/10.1038/s41746-026-02341-x","url":null,"abstract":"Internalizing problems are the most common mental health problems encountered in youths. This study investigated the efficacy of a transdiagnostic Internet-delivered intervention (REBTonAd), delivered over 6 weeks. Our study included Romanian youths (aged 11-17) with a primary diagnosis of an anxiety and/or depressive disorder. Eligible participants (N = 106; Mage = 12.83, SD = 1.63) were randomly assigned to the REBTonAd group (n = 53) or the waitlist condition (WL) (n = 53), with outcomes assessed at baseline, post-test, and 6-month follow-up. At the post-test, the remission rate and clinical reliable change indices were higher in the REBTonAd group. Internalizing problems were reduced more in the REBTonAd group, with a moderate effect size (standardized mean difference = -0.60, 95% CI -0.96 to -0.25). Future research needs to test the effectiveness of this intervention compared to disorder-specific treatments and investigate its cost-effectiveness. This trial was prospectively registered at ClinicalTrials.gov (NCT04179526).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"19 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The progression from metabolic dysfunction-associated steatotic liver disease (MASLD) to metabolic dysfunction-associated steatohepatitis (MASH) is a critical link leading to cirrhosis and hepatocellular carcinoma. Yet the responsible cellular programs remain unclear. We integrated public single-cell, spatial, and bulk transcriptomic datasets to map microenvironmental remodeling and regulatory networks during MASLD-MASH progression. Among the seven major liver cell types identified, monocytes/macrophages and hepatic stellate cells (HSCs) were significantly enriched and demonstrated spatial co-localization within the context of MASH. We identified a DTNA+distinct macrophage subpopulation that was specifically enriched in MASH. This subpopulation exhibited characteristics consistent with M2 polarization, hypoxia, and enhanced inflammatory signaling. Pseudotime trajectory analysis revealed that this state represents a differentiation pathway originating from Kupffer cells to the DTNA+ state. RUNX2 emerged as the key transcriptional regulator. Cell communication analysis demonstrated that DTNA+ macrophages potentially interact with activated HSCs via the RUNX2-PLG-PARD3 axis, contributing to the exacerbation of liver fibrosis. Finally, ensemble machine learning models (mean AUC = 0.839), identified DTNA as the optimal predictive biomarker for distinguishing MASLD from MASH. This study highlight DTNA+ macrophages and the RUNX2-PLG-PARD3 axis as candidate mechanisms and targets for non-invasive diagnosis and therapy in MASH.
{"title":"Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH.","authors":"Weiheng Wen,Zenghui Liu,Wenliang Tan,Yingzheng Tan,Wei Li,Jian Wan,Hongsai Hu,Zhengwu Jiang,Xing Tang,Jing Yang,Jiao Xiao,Xiongjin Tan,Xun Chen,Peili Wu,Yukun Li","doi":"10.1038/s41746-026-02352-8","DOIUrl":"https://doi.org/10.1038/s41746-026-02352-8","url":null,"abstract":"The progression from metabolic dysfunction-associated steatotic liver disease (MASLD) to metabolic dysfunction-associated steatohepatitis (MASH) is a critical link leading to cirrhosis and hepatocellular carcinoma. Yet the responsible cellular programs remain unclear. We integrated public single-cell, spatial, and bulk transcriptomic datasets to map microenvironmental remodeling and regulatory networks during MASLD-MASH progression. Among the seven major liver cell types identified, monocytes/macrophages and hepatic stellate cells (HSCs) were significantly enriched and demonstrated spatial co-localization within the context of MASH. We identified a DTNA+distinct macrophage subpopulation that was specifically enriched in MASH. This subpopulation exhibited characteristics consistent with M2 polarization, hypoxia, and enhanced inflammatory signaling. Pseudotime trajectory analysis revealed that this state represents a differentiation pathway originating from Kupffer cells to the DTNA+ state. RUNX2 emerged as the key transcriptional regulator. Cell communication analysis demonstrated that DTNA+ macrophages potentially interact with activated HSCs via the RUNX2-PLG-PARD3 axis, contributing to the exacerbation of liver fibrosis. Finally, ensemble machine learning models (mean AUC = 0.839), identified DTNA as the optimal predictive biomarker for distinguishing MASLD from MASH. This study highlight DTNA+ macrophages and the RUNX2-PLG-PARD3 axis as candidate mechanisms and targets for non-invasive diagnosis and therapy in MASH.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"20 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electronic health records (EHRs) capture evolving physiological processes, yet most machine learning models impose static or sequential assumptions that flatten their temporal and relational complexity. We introduce DynaGraph, a dynamic and interpretable graph learning framework that constructs evolving spatio-temporal graphs from multivariate clinical time-series. Unlike previous methods, DynaGraph learns the structure of relationships between different clinical variables over time without predefined graphs, integrates sequential embeddings with contrastive graph augmentation, and incorporates a pseudo-attention mechanism to reveal temporally resolved risk factors. Trained end-to-end with a novel multi-loss objective that combines focal, structural, and contrastive components, DynaGraph addresses two pervasive challenges in real-world clinical modelling: class imbalance and temporal instability. We evaluated DynaGraph on four large-scale EHR datasets totalling 40,856 patients: MIMIC-III (17,279 ICU admissions), eICU (1433 cardiac ICU patients), HiRID-ICU (33,000 patients), and EHRSHOT (2378 primary care patients). DynaGraph consistently outperforms 14 state-of-the-art baselines, achieving 6-8% relative improvements in area under the precision-recall curve (AUPRC) and significant gains in sensitivity (12-22% over leading methods). Beyond predictive performance, DynaGraph offers time-specific interpretability aligned with clinical reasoning, providing gradient-based feature importance scores at 3-hour intervals that identify which physiological relationships drive predictions. This framework explicitly models temporal attribution of risk factors across patient trajectories in a millisecond inference time.
{"title":"DynaGraph: interpretable dynamic graph learning for temporal electronic health records.","authors":"Munib Mesinovic,Soheila Molaei,Peter Watkinson,Tingting Zhu","doi":"10.1038/s41746-025-02328-0","DOIUrl":"https://doi.org/10.1038/s41746-025-02328-0","url":null,"abstract":"Electronic health records (EHRs) capture evolving physiological processes, yet most machine learning models impose static or sequential assumptions that flatten their temporal and relational complexity. We introduce DynaGraph, a dynamic and interpretable graph learning framework that constructs evolving spatio-temporal graphs from multivariate clinical time-series. Unlike previous methods, DynaGraph learns the structure of relationships between different clinical variables over time without predefined graphs, integrates sequential embeddings with contrastive graph augmentation, and incorporates a pseudo-attention mechanism to reveal temporally resolved risk factors. Trained end-to-end with a novel multi-loss objective that combines focal, structural, and contrastive components, DynaGraph addresses two pervasive challenges in real-world clinical modelling: class imbalance and temporal instability. We evaluated DynaGraph on four large-scale EHR datasets totalling 40,856 patients: MIMIC-III (17,279 ICU admissions), eICU (1433 cardiac ICU patients), HiRID-ICU (33,000 patients), and EHRSHOT (2378 primary care patients). DynaGraph consistently outperforms 14 state-of-the-art baselines, achieving 6-8% relative improvements in area under the precision-recall curve (AUPRC) and significant gains in sensitivity (12-22% over leading methods). Beyond predictive performance, DynaGraph offers time-specific interpretability aligned with clinical reasoning, providing gradient-based feature importance scores at 3-hour intervals that identify which physiological relationships drive predictions. This framework explicitly models temporal attribution of risk factors across patient trajectories in a millisecond inference time.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"30 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1038/s41746-025-02335-1
Shriienidhie Ganesh,Romit Bhattacharya,Aarushi Bhatnagar,Rishi Madnani,Christine Russo,Sara Haidermota,Bhaavana Oruganty,Harshil Bhavsar,Priyansh Shah,Sarah Pitafi,Nishant Uppal,Namrata Sengupta,Kenneth Rice,Matthew P Conomos,Ravi Dave,Abha Khandelwal,Aniruddh P Patel,Kaavya Paruchuri,Yamini Levitsky,Sanchita Singal Parulkar,Rohan Khera,Martha Gulati,Amit V Khera,Whitney E Hornsby,Latha Palaniappan,Pradeep Natarajan
South Asians experience disproportionately elevated cardiometabolic disease risk yet remain underrepresented in genomic research. The OurHealth Study builds a digital biobank of US South Asian adults, integrating remote surveys, mailed biospecimens for sequencing, and electronic health record sharing to identify genetic and non-genetic drivers of cardiometabolic disease. By pairing remote participation with culturally tailored outreach, OurHealth enhances accessibility, supports granular phenotyping, and addresses logistical barriers to genomic research inclusion.
{"title":"The OurHealth Study: A digital genomic cohort for cardiometabolic risk mechanisms in US South Asians.","authors":"Shriienidhie Ganesh,Romit Bhattacharya,Aarushi Bhatnagar,Rishi Madnani,Christine Russo,Sara Haidermota,Bhaavana Oruganty,Harshil Bhavsar,Priyansh Shah,Sarah Pitafi,Nishant Uppal,Namrata Sengupta,Kenneth Rice,Matthew P Conomos,Ravi Dave,Abha Khandelwal,Aniruddh P Patel,Kaavya Paruchuri,Yamini Levitsky,Sanchita Singal Parulkar,Rohan Khera,Martha Gulati,Amit V Khera,Whitney E Hornsby,Latha Palaniappan,Pradeep Natarajan","doi":"10.1038/s41746-025-02335-1","DOIUrl":"https://doi.org/10.1038/s41746-025-02335-1","url":null,"abstract":"South Asians experience disproportionately elevated cardiometabolic disease risk yet remain underrepresented in genomic research. The OurHealth Study builds a digital biobank of US South Asian adults, integrating remote surveys, mailed biospecimens for sequencing, and electronic health record sharing to identify genetic and non-genetic drivers of cardiometabolic disease. By pairing remote participation with culturally tailored outreach, OurHealth enhances accessibility, supports granular phenotyping, and addresses logistical barriers to genomic research inclusion.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"56 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Precise localization of perfusion deficits in diffusion-weighted MRI (DWI) is critical for acute ischemic stroke management. However, existing deep learning methods typically produce discrete binary masks, failing to capture the continuous nature of ischemic injury and discarding valuable intra-lesion information. We propose StrokeFlow, a novel framework that represents the ischemic region as a continuous field. Our coordinate-based network is trained to output a smooth ischemic density field, representing voxel-level infarction probability. Furthermore, we introduce a vector flow head, explicitly supervised to learn a vector field that aligns with the negative gradient of the Apparent Diffusion Coefficient (ADC) map, thereby modeling the directionality of the perfusion deficit. Evaluated on the public ISLES 2022 dataset, StrokeFlow demonstrated superior lesion boundary accuracy, significantly outperforming strong baselines in the 95% Hausdorff Distance metric. The model also showed enhanced sensitivity in detecting small and multifocal lesions. By shifting the paradigm from discrete segmentation to continuous, functionally-aware fields, StrokeFlow offers a more biologically plausible and interpretable tool for a nuanced clinical assessment of ischemic stroke.
{"title":"Modeling Ischemic Stroke Pathological Dynamics via Continuous Fields and Vector Flow","authors":"Liuxi Chu, Ying Wang, Zhijin Li, Xiaotong Liu, Shui Tian, Hongqiang Xie, Yalin Zhang","doi":"10.1038/s41746-025-02222-9","DOIUrl":"https://doi.org/10.1038/s41746-025-02222-9","url":null,"abstract":"Precise localization of perfusion deficits in diffusion-weighted MRI (DWI) is critical for acute ischemic stroke management. However, existing deep learning methods typically produce discrete binary masks, failing to capture the continuous nature of ischemic injury and discarding valuable intra-lesion information. We propose StrokeFlow, a novel framework that represents the ischemic region as a continuous field. Our coordinate-based network is trained to output a smooth ischemic density field, representing voxel-level infarction probability. Furthermore, we introduce a vector flow head, explicitly supervised to learn a vector field that aligns with the negative gradient of the Apparent Diffusion Coefficient (ADC) map, thereby modeling the directionality of the perfusion deficit. Evaluated on the public ISLES 2022 dataset, StrokeFlow demonstrated superior lesion boundary accuracy, significantly outperforming strong baselines in the 95% Hausdorff Distance metric. The model also showed enhanced sensitivity in detecting small and multifocal lesions. By shifting the paradigm from discrete segmentation to continuous, functionally-aware fields, StrokeFlow offers a more biologically plausible and interpretable tool for a nuanced clinical assessment of ischemic stroke.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"26 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-15DOI: 10.1038/s41746-026-02346-6
Sijia Zhao, Sofia Toniolo, Qian-Yuan Tang, Anna Scholcz, Akke Ganse-Dumrath, Claudia Gendarini, M. John Broulidakis, Sian Thompson, Sanjay G. Manohar, Masud Husain
The global rise in dementia necessitates scalable cognitive assessments that can evolve to serve both clinical and research applications. We present the Oxford Cognitive Testing Portal (OCTAL), a remote, browser-based platform providing performance metrics for memory, attention, visuospatial and executive function domains. Four validation studies (N = 1664) confirmed cross-cultural applicability, lifespan sensitivity and clinical utility. Task performance was equivalent in English- and Chinese-speaking younger adults and mapped domain-specific ageing trajectories in mid- to late-adulthood. In a memory-clinic cohort (N = 194), 5-minute OCTAL screen distinguished patients with Alzheimer’s disease dementia from subjective cognitive decline (AUC = 0.92), matching a standard paper-based test, while a 20-minute subset surpassed this (AUC = 0.97; p = 0.04). Test-retest reliability was very good (ICC ≥ 0.79; N = 118). OCTAL enables remote assessment for large-scale research and screening, with an open, modular architecture that makes it a uniquely sustainable and evolvable tool for the research community.
{"title":"Remote digital cognitive assessment for aging and dementia using the Oxford Cognitive Testing Portal OCTAL","authors":"Sijia Zhao, Sofia Toniolo, Qian-Yuan Tang, Anna Scholcz, Akke Ganse-Dumrath, Claudia Gendarini, M. John Broulidakis, Sian Thompson, Sanjay G. Manohar, Masud Husain","doi":"10.1038/s41746-026-02346-6","DOIUrl":"https://doi.org/10.1038/s41746-026-02346-6","url":null,"abstract":"The global rise in dementia necessitates scalable cognitive assessments that can evolve to serve both clinical and research applications. We present the Oxford Cognitive Testing Portal (OCTAL), a remote, browser-based platform providing performance metrics for memory, attention, visuospatial and executive function domains. Four validation studies (N = 1664) confirmed cross-cultural applicability, lifespan sensitivity and clinical utility. Task performance was equivalent in English- and Chinese-speaking younger adults and mapped domain-specific ageing trajectories in mid- to late-adulthood. In a memory-clinic cohort (N = 194), 5-minute OCTAL screen distinguished patients with Alzheimer’s disease dementia from subjective cognitive decline (AUC = 0.92), matching a standard paper-based test, while a 20-minute subset surpassed this (AUC = 0.97; p = 0.04). Test-retest reliability was very good (ICC ≥ 0.79; N = 118). OCTAL enables remote assessment for large-scale research and screening, with an open, modular architecture that makes it a uniquely sustainable and evolvable tool for the research community.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"4 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-15DOI: 10.1038/s41746-025-02142-8
Richard A. Bryant, Anne M. de Graaff, Rand Habashneh, Sarah Fanatseh, Dharani Keyan, Aemal Akhtar, Adnan Abualhaija, Muhannad Faroun, Ibrahim Said Aqel, Latefa Dardas, Hadeel Afar, Chiara Servili, Dusan Hadzi-Pavlovic, Mark van Ommeren, Kenneth Carswell
This randomised controlled trial compared a 10-session chatbot intervention with 5 weekly brief support calls (STARS) to enhanced usual care (EUC) in distressed young adults in Jordan (N = 344). Primary outcome was change in anxiety and depression severity assessed at baseline by the Hopkins Symptom Checklist (HSCL), 1-week posttreatment, and 3 months after treatment (primary outcome timepoint), as well as secondary outcome measures of psychological distress, personally identified problems, functional impairment, wellbeing and perceived agency. At the 3-month assessment, relative to EUC participants enrolled in STARS reported greater reductions of anxiety (effect size, 0.70) and depression (size, 0.61), as well as greater reductions in psychological distress, personally identified problems, functional impairment and greater improvement in wellbeing and sense of agency. Similar levels of efficacy were retained even for those with more severe symptom levels. This guided chatbot offers a scalable psychological intervention that can be implemented to increase access to evidence-based mental health care. Trial Registration: The trial was prospectively registered on ISRCTN on 02/11/2022 (https://doi.org/10.1186/ISRCTN19217696).
{"title":"A guided chatbot-based psychological intervention for psychologically distressed older adolescents and young adults: a randomised clinical trial in Jordan","authors":"Richard A. Bryant, Anne M. de Graaff, Rand Habashneh, Sarah Fanatseh, Dharani Keyan, Aemal Akhtar, Adnan Abualhaija, Muhannad Faroun, Ibrahim Said Aqel, Latefa Dardas, Hadeel Afar, Chiara Servili, Dusan Hadzi-Pavlovic, Mark van Ommeren, Kenneth Carswell","doi":"10.1038/s41746-025-02142-8","DOIUrl":"https://doi.org/10.1038/s41746-025-02142-8","url":null,"abstract":"This randomised controlled trial compared a 10-session chatbot intervention with 5 weekly brief support calls (STARS) to enhanced usual care (EUC) in distressed young adults in Jordan (N = 344). Primary outcome was change in anxiety and depression severity assessed at baseline by the Hopkins Symptom Checklist (HSCL), 1-week posttreatment, and 3 months after treatment (primary outcome timepoint), as well as secondary outcome measures of psychological distress, personally identified problems, functional impairment, wellbeing and perceived agency. At the 3-month assessment, relative to EUC participants enrolled in STARS reported greater reductions of anxiety (effect size, 0.70) and depression (size, 0.61), as well as greater reductions in psychological distress, personally identified problems, functional impairment and greater improvement in wellbeing and sense of agency. Similar levels of efficacy were retained even for those with more severe symptom levels. This guided chatbot offers a scalable psychological intervention that can be implemented to increase access to evidence-based mental health care. Trial Registration: The trial was prospectively registered on ISRCTN on 02/11/2022 (https://doi.org/10.1186/ISRCTN19217696).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"48 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-15DOI: 10.1038/s41746-025-02325-3
Caroline Poisson, Adeline Duflot-Boukobza, Delphine Mathivon, Mohamed Khettab, Marie Ferrua, Aude Fourcade, Naïma Lezghed, Frédéric Dhermain, François Lemare, Vanessa Puglisi, May Abbas, Mario Di Palma, Florian Scotté, Etienne Minvielle, Olivier Mir, David Guyon, Sarah N. Dumont
Oral anticancer agents (OAAs) are commonly prescribed for patients with primary brain tumors, but adherence can be challenging due to cognitive impairment and discontinuous treatment schedules. This subgroup analysis of the randomized phase 3 CAPRI trial evaluated the impact of a nurse navigator-led intervention combined with a digital platform (web portal and mobile app) versus standard care in patients with primary brain tumors treated with OAAs. The primary endpoint was Relative Dose Intensity (RDI), with secondary endpoints including adherence, toxicity, healthcare utilization, and patient-reported experience. Fifty-one patients were included between October 2016 and May 2019, 63% of whom had glioblastoma. Twenty-six patients received the intervention. RDI was significantly higher in the intervention group compared to the control group (105% ± 12 vs. 97.6% ± 13, p = 0.04). The intervention also resulted in fewer emergency room visits, reduced hospitalizations, greater use of supportive care services, and improved patient-reported experience (all p < 0.05). Remote monitoring allowed early corticosteroid adjustments in cases suggestive of intracranial hypertension, helping to prevent hospitalizations. No significant differences were observed in treatment-related toxicity. These findings suggest that a nurse navigator-led digital intervention can improve care continuity and outcomes in this population and merit further investigation.
{"title":"Impact of nurse navigation and mobile app on brain tumor patients receiving oral anticancer therapy","authors":"Caroline Poisson, Adeline Duflot-Boukobza, Delphine Mathivon, Mohamed Khettab, Marie Ferrua, Aude Fourcade, Naïma Lezghed, Frédéric Dhermain, François Lemare, Vanessa Puglisi, May Abbas, Mario Di Palma, Florian Scotté, Etienne Minvielle, Olivier Mir, David Guyon, Sarah N. Dumont","doi":"10.1038/s41746-025-02325-3","DOIUrl":"https://doi.org/10.1038/s41746-025-02325-3","url":null,"abstract":"Oral anticancer agents (OAAs) are commonly prescribed for patients with primary brain tumors, but adherence can be challenging due to cognitive impairment and discontinuous treatment schedules. This subgroup analysis of the randomized phase 3 CAPRI trial evaluated the impact of a nurse navigator-led intervention combined with a digital platform (web portal and mobile app) versus standard care in patients with primary brain tumors treated with OAAs. The primary endpoint was Relative Dose Intensity (RDI), with secondary endpoints including adherence, toxicity, healthcare utilization, and patient-reported experience. Fifty-one patients were included between October 2016 and May 2019, 63% of whom had glioblastoma. Twenty-six patients received the intervention. RDI was significantly higher in the intervention group compared to the control group (105% ± 12 vs. 97.6% ± 13, p = 0.04). The intervention also resulted in fewer emergency room visits, reduced hospitalizations, greater use of supportive care services, and improved patient-reported experience (all p < 0.05). Remote monitoring allowed early corticosteroid adjustments in cases suggestive of intracranial hypertension, helping to prevent hospitalizations. No significant differences were observed in treatment-related toxicity. These findings suggest that a nurse navigator-led digital intervention can improve care continuity and outcomes in this population and merit further investigation.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"92 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pain is a complex multidimensional experience that integrates sensory and emotional components, presenting significant challenges for accurate assessment in clinical practice. Traditional methods of pain evaluation rely on subjective self-reporting and each individual’s ability to communicate their pain experience. In light of the effect of pain on the Autonomic Nervous System, researchers are interested in developing objective assessment techniques using physiological signals. This paper outlines the latest advances in pain biomarkers and machine learning methods for assessing pain using physiological signals, highlighting the growing interest and unmet demand in this area. A comprehensive literature review was conducted, covering studies between 2014 and 2024. The discussion is organised into two areas: first, an analysis of the variations in signal feature behaviour across different pain types, and second, a review of the current state-of-the-art models for pain assessment developed using classical machine learning and deep learning techniques.
{"title":"Pain assessment using physiological responses/markers in different types of pain: a scoping review","authors":"Camila Camacho-Navas, Ling Li, Kavita Poply, Vivek Mehta, Panicos Kyriacou","doi":"10.1038/s41746-025-02241-6","DOIUrl":"https://doi.org/10.1038/s41746-025-02241-6","url":null,"abstract":"Pain is a complex multidimensional experience that integrates sensory and emotional components, presenting significant challenges for accurate assessment in clinical practice. Traditional methods of pain evaluation rely on subjective self-reporting and each individual’s ability to communicate their pain experience. In light of the effect of pain on the Autonomic Nervous System, researchers are interested in developing objective assessment techniques using physiological signals. This paper outlines the latest advances in pain biomarkers and machine learning methods for assessing pain using physiological signals, highlighting the growing interest and unmet demand in this area. A comprehensive literature review was conducted, covering studies between 2014 and 2024. The discussion is organised into two areas: first, an analysis of the variations in signal feature behaviour across different pain types, and second, a review of the current state-of-the-art models for pain assessment developed using classical machine learning and deep learning techniques.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"4 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-15DOI: 10.1038/s41746-025-02061-8
Jiake Wu, Zongyu Wen, Hainan Zhou, Na Sun, Yuanyuan Zhang
Precise delineation and parametric modeling of curvilinear vascular architectures in volumetric medical imaging are pivotal for advancing clinical diagnostics and therapeutic planning. Prevailing methodologies predominantly adopt discrete voxel-wise representations, such as binary masks, which are prone to topological disruptions and artifact-induced fragmentation arising from inherent per-voxel classification biases. To address these challenges, we present FlowAxis, a pioneering continuous parameterization paradigm leveraging Adaptive Vessel Axes (AVA), wherein adaptive keypoints function as interconnected vertices to encapsulate intrinsic spatial topologies. FlowAxis distinguishes itself through superior topological coherence guaranteed by displacement convexity of the energy functional. Comprehensive empirical validations across four benchmark datasets for three-dimensional vascular segmentation substantiate FlowAxis’s performance, achieving significant improvements in both topological accuracy (clDice) and geometric fidelity (Hausdorff distance). Furthermore, qualitative assessments via curved planar reformations highlight its transformative potential in clinical workflows, while theoretical guarantees ensure reliability in safety-critical medical applications. Our work bridges the gap between mathematical rigor and practical medical imaging, providing the first complete theoretical framework for continuous vessel representation with provable optimality and convergence guarantees.
{"title":"Geometric-topological deep transfer learning for precise vessel segmentation in 3D medical volumes","authors":"Jiake Wu, Zongyu Wen, Hainan Zhou, Na Sun, Yuanyuan Zhang","doi":"10.1038/s41746-025-02061-8","DOIUrl":"https://doi.org/10.1038/s41746-025-02061-8","url":null,"abstract":"Precise delineation and parametric modeling of curvilinear vascular architectures in volumetric medical imaging are pivotal for advancing clinical diagnostics and therapeutic planning. Prevailing methodologies predominantly adopt discrete voxel-wise representations, such as binary masks, which are prone to topological disruptions and artifact-induced fragmentation arising from inherent per-voxel classification biases. To address these challenges, we present FlowAxis, a pioneering continuous parameterization paradigm leveraging Adaptive Vessel Axes (AVA), wherein adaptive keypoints function as interconnected vertices to encapsulate intrinsic spatial topologies. FlowAxis distinguishes itself through superior topological coherence guaranteed by displacement convexity of the energy functional. Comprehensive empirical validations across four benchmark datasets for three-dimensional vascular segmentation substantiate FlowAxis’s performance, achieving significant improvements in both topological accuracy (clDice) and geometric fidelity (Hausdorff distance). Furthermore, qualitative assessments via curved planar reformations highlight its transformative potential in clinical workflows, while theoretical guarantees ensure reliability in safety-critical medical applications. Our work bridges the gap between mathematical rigor and practical medical imaging, providing the first complete theoretical framework for continuous vessel representation with provable optimality and convergence guarantees.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"33 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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