Pub Date : 2026-01-29DOI: 10.1038/s41746-026-02400-3
Yuqing Lu,Jing Chen,Nini Fan,Wenchao Song,Haiyang Sheng,Yinfeng Yang,Jinghui Wang
Drug-drug interaction (DDI) poses a major challenge in clinical pharmacology, often compromising therapeutic efficacy or causing serious adverse events. Traditional detection methods, heavily dependent on experimental assays and expert knowledge, are constrained by high costs and limited scalability. This work explores emerging machine learning (ML)-based strategies for predicting DDIs by leveraging the rapidly expanding biomedical data landscape. Recent advances in deep learning architectures, graph neural networks and sophisticated feature engineering have markedly improved predictive performance, offering scalable and data-efficient alternatives to conventional approaches. We further highlight real-world clinical applications where ML-based models have enhanced drug safety monitoring and informed therapeutic decision-making. Finally, we discuss critical challenges like model interpretability, generalizability and integration with clinical workflows, and outline future directions toward building robust, explainable and clinically actionable DDI prediction systems. This work provides a comprehensive perspective on how AI-driven methodologies are reshaping pharmacovigilance and precision therapeutics.
{"title":"Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.","authors":"Yuqing Lu,Jing Chen,Nini Fan,Wenchao Song,Haiyang Sheng,Yinfeng Yang,Jinghui Wang","doi":"10.1038/s41746-026-02400-3","DOIUrl":"https://doi.org/10.1038/s41746-026-02400-3","url":null,"abstract":"Drug-drug interaction (DDI) poses a major challenge in clinical pharmacology, often compromising therapeutic efficacy or causing serious adverse events. Traditional detection methods, heavily dependent on experimental assays and expert knowledge, are constrained by high costs and limited scalability. This work explores emerging machine learning (ML)-based strategies for predicting DDIs by leveraging the rapidly expanding biomedical data landscape. Recent advances in deep learning architectures, graph neural networks and sophisticated feature engineering have markedly improved predictive performance, offering scalable and data-efficient alternatives to conventional approaches. We further highlight real-world clinical applications where ML-based models have enhanced drug safety monitoring and informed therapeutic decision-making. Finally, we discuss critical challenges like model interpretability, generalizability and integration with clinical workflows, and outline future directions toward building robust, explainable and clinically actionable DDI prediction systems. This work provides a comprehensive perspective on how AI-driven methodologies are reshaping pharmacovigilance and precision therapeutics.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"281 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072929","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}
To identify novel diagnostic biomarkers for acute pancreatitis (AP) and facilitate the early prediction of severe AP (SAP), this investigation characterized the serum metabolomic profiles of patients across distinct disease phases and integrated metabolomics with artificial intelligence to construct bile acid-based predictive models. The observational protocol was registered with the Chinese Clinical Trial Registry (ChiCTR2000034117) on June 24, 2020. Comparative metabolomic analysis revealed significant alterations in 303 metabolites and 461 lipid species in AP. Subsequent weighted gene coexpression network analysis demonstrated robust correlations between clinical parameters and specific metabolic clusters, particularly bile acids (BAs) and lipid species. Targeted quantification of 63 BAs was subsequently performed within a multicentre validation cohort (n = 948). Machine learning algorithms applied to these data facilitated the derivation of two distinct BA panels. The first panel, comprising nine BAs, demonstrated high diagnostic accuracy for AP, including among individuals with negative conventional enzymatic biomarkers, and effectively discriminated AP from acute cholangitis, as reflected by elevated area under the curve (AUC) values. A second panel, consisting of 13 BAs, reliably identified patients at elevated risk for SAP progression. Collectively, these results validate the translational potential of machine learning-driven metabolic biomarkers for the precision management of acute abdominal conditions, underscore the clinical utility of BAs as promising diagnostic and prognostic biomarkers in acute pancreatitis, and provide a new paradigm for the development of dynamic risk early-warning systems (Clinical Trial Registration Our study is an observational study registered in ChiCTR (ChiCTR2000034117) on 2020/06/24, not a prospective interventional clinical trial, and therefore does not fall under the ICMJE definition of a clinical trial requiring CONSORT compliance).
{"title":"Non-invasive liquid biopsy based on metabolomic profiling improves diagnosis and early warning of severe acute pancreatitis.","authors":"Dawei Deng,Qihang Yuan,Chen Pan,Junhong Chen,Jianjun Liu,Song Wei,Yi Liu,Yutong Zhu,Tianfu Wei,Jianliang Cao,Zeming Wu,Yuepeng Hu,Dong Shang,Peiyuan Yin","doi":"10.1038/s41746-025-02294-7","DOIUrl":"https://doi.org/10.1038/s41746-025-02294-7","url":null,"abstract":"To identify novel diagnostic biomarkers for acute pancreatitis (AP) and facilitate the early prediction of severe AP (SAP), this investigation characterized the serum metabolomic profiles of patients across distinct disease phases and integrated metabolomics with artificial intelligence to construct bile acid-based predictive models. The observational protocol was registered with the Chinese Clinical Trial Registry (ChiCTR2000034117) on June 24, 2020. Comparative metabolomic analysis revealed significant alterations in 303 metabolites and 461 lipid species in AP. Subsequent weighted gene coexpression network analysis demonstrated robust correlations between clinical parameters and specific metabolic clusters, particularly bile acids (BAs) and lipid species. Targeted quantification of 63 BAs was subsequently performed within a multicentre validation cohort (n = 948). Machine learning algorithms applied to these data facilitated the derivation of two distinct BA panels. The first panel, comprising nine BAs, demonstrated high diagnostic accuracy for AP, including among individuals with negative conventional enzymatic biomarkers, and effectively discriminated AP from acute cholangitis, as reflected by elevated area under the curve (AUC) values. A second panel, consisting of 13 BAs, reliably identified patients at elevated risk for SAP progression. Collectively, these results validate the translational potential of machine learning-driven metabolic biomarkers for the precision management of acute abdominal conditions, underscore the clinical utility of BAs as promising diagnostic and prognostic biomarkers in acute pancreatitis, and provide a new paradigm for the development of dynamic risk early-warning systems (Clinical Trial Registration Our study is an observational study registered in ChiCTR (ChiCTR2000034117) on 2020/06/24, not a prospective interventional clinical trial, and therefore does not fall under the ICMJE definition of a clinical trial requiring CONSORT compliance).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"43 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073002","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 : 2026-01-28DOI: 10.1038/s41746-026-02383-1
Júlia Tannús, Caroline Valentini, Eduardo Naves
Stroke is a leading cause of long-term disability, often affecting upper-limb motor function and requiring continuous assessment. The Fugl-Meyer Assessment (FMA), though a clinical gold standard, is time-consuming and demands specialized personnel. This study presents an AI-driven, low-cost rehabilitation exergame that simultaneously provides therapy and automatically estimates upper-limb motor performance during gameplay using only a standard camera. Sixteen kinematic and spatiotemporal features were extracted from 2D hand and arm trajectories of twelve post-stroke individuals (24 limbs, 14 affected) using the MediaPipe framework. Features such as hand angle, range of motion, movement area, traveled distance, and shoulder–elbow coordination showed strong correlations with FMA scores and stratified participants by motor severity. A lightweight linear regression model achieved high predictive performance (Spearman ρ = 0.92, R² = 0.89, RMSE = 4.42) and classified severity levels with 86–93% accuracy. This interpretable approach outperformed complex machine learning models, highlighting the clinical relevance of transparent metrics embedded in gameplay. The proposed framework is sensor-free, scalable, and reproducible, offering immediate feedback while reducing clinical workload and enabling accessible digital biomarkers for telerehabilitation and remote monitoring after stroke.
{"title":"AI-driven low-cost rehabilitation exergame as a lightweight framework for stroke assessment","authors":"Júlia Tannús, Caroline Valentini, Eduardo Naves","doi":"10.1038/s41746-026-02383-1","DOIUrl":"https://doi.org/10.1038/s41746-026-02383-1","url":null,"abstract":"Stroke is a leading cause of long-term disability, often affecting upper-limb motor function and requiring continuous assessment. The Fugl-Meyer Assessment (FMA), though a clinical gold standard, is time-consuming and demands specialized personnel. This study presents an AI-driven, low-cost rehabilitation exergame that simultaneously provides therapy and automatically estimates upper-limb motor performance during gameplay using only a standard camera. Sixteen kinematic and spatiotemporal features were extracted from 2D hand and arm trajectories of twelve post-stroke individuals (24 limbs, 14 affected) using the MediaPipe framework. Features such as hand angle, range of motion, movement area, traveled distance, and shoulder–elbow coordination showed strong correlations with FMA scores and stratified participants by motor severity. A lightweight linear regression model achieved high predictive performance (Spearman ρ = 0.92, R² = 0.89, RMSE = 4.42) and classified severity levels with 86–93% accuracy. This interpretable approach outperformed complex machine learning models, highlighting the clinical relevance of transparent metrics embedded in gameplay. The proposed framework is sensor-free, scalable, and reproducible, offering immediate feedback while reducing clinical workload and enabling accessible digital biomarkers for telerehabilitation and remote monitoring after stroke.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"52 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057194","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}
Human–AI collaboration (H + AI) using large language models (LLMs) offers a promising approach to enhance clinical reasoning, documentation, and interpretation tasks. Following PRISMA 2020 (PROSPERO registration: CRD420251068272), we systematically compared H + AI with human-only (H) workflows, searching four databases through June 28, 2025. Ten peer-reviewed studies met eligibility criteria, with three preprints informing sensitivity analyses only. Diagnostic/interpretation accuracy (k = 2) showed a positive trend for H + AI (Risk Ratio [RR] 1.59), but was statistically imprecise and non-significant (95% CI 0.08 to 32.74), with 95% prediction intervals (PI) crossing the null. Composite diagnostic/management scores (k = 2) showed a statistically significant improvement (Mean Difference [MD] +4.88 percentage points, 95% CI + 0.65 to +9.12), yet the PI (–31.65 to 41.42) indicates high real-world uncertainty. Time efficiency (k = 3) showed no overall difference (MD + 0.4 min, 95%CI −4.18 to +4.97; I² = 70.1%). While documentation quality improved, but factual error rates remained high (~26–36%), undermining quality gains. In three-arm settings, H + AI did not universally outperform AI-only. Evidence remains preliminary yet highly uncertain and context-dependent. We recommend preregistered, pragmatic, multicenter trials embedded in real workflows, with harmonized core outcomes that prioritize safety/error metrics and interfaces that surface uncertainty and support verification.
{"title":"Human–large language model collaboration in clinical medicine: a systematic review and meta-analysis","authors":"Guoyong Wang, Kaijun Zhang, Jiyue Jiang, Chaonan Wang, Hui Bi, Haojun Liang, Zuoliang Qi, Ying Huang, Yu Li, Xiaonan Yang","doi":"10.1038/s41746-026-02382-2","DOIUrl":"https://doi.org/10.1038/s41746-026-02382-2","url":null,"abstract":"Human–AI collaboration (H + AI) using large language models (LLMs) offers a promising approach to enhance clinical reasoning, documentation, and interpretation tasks. Following PRISMA 2020 (PROSPERO registration: CRD420251068272), we systematically compared H + AI with human-only (H) workflows, searching four databases through June 28, 2025. Ten peer-reviewed studies met eligibility criteria, with three preprints informing sensitivity analyses only. Diagnostic/interpretation accuracy (k = 2) showed a positive trend for H + AI (Risk Ratio [RR] 1.59), but was statistically imprecise and non-significant (95% CI 0.08 to 32.74), with 95% prediction intervals (PI) crossing the null. Composite diagnostic/management scores (k = 2) showed a statistically significant improvement (Mean Difference [MD] +4.88 percentage points, 95% CI + 0.65 to +9.12), yet the PI (–31.65 to 41.42) indicates high real-world uncertainty. Time efficiency (k = 3) showed no overall difference (MD + 0.4 min, 95%CI −4.18 to +4.97; I² = 70.1%). While documentation quality improved, but factual error rates remained high (~26–36%), undermining quality gains. In three-arm settings, H + AI did not universally outperform AI-only. Evidence remains preliminary yet highly uncertain and context-dependent. We recommend preregistered, pragmatic, multicenter trials embedded in real workflows, with harmonized core outcomes that prioritize safety/error metrics and interfaces that surface uncertainty and support verification.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"183 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057190","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 delineation of hepatocellular carcinoma (HCC) in abdominal CT is pivotal for early diagnosis and surgical planning, yet remains challenged by morphological heterogeneity, low contrast in small lesions, and scanner variability. To address these limitations, we propose Prompt-Mamba-AF, a framework tailored for robust HCC segmentation. Our method uniquely integrates anatomy-aware prompts to guide feature extraction within liver regions and leverages Mamba-based state-space modeling to capture long-range volumetric dependencies with linear complexity. Furthermore, we introduce structure-aware filtering to enforce topological consistency along lesion boundaries. Extensive validation on the LiTS, 3DIRCADb, and CHAOS benchmarks demonstrates that Prompt-Mamba-AF outperforms current state-of-the-art CNN and Transformer architectures. The model achieves leading Dice similarity and boundary accuracy while maintaining a compact parameter footprint (27.6M). Results indicate significant improvements in small nodule sensitivity and generalization across diverse imaging domains, positioning Prompt-Mamba-AF as an efficient solution for multi-center clinical workflows.
{"title":"Prompt-mamba filtering networks for accurate hepatocellular carcinoma lesion segmentation in abdominal CT.","authors":"Long Xia,Hai-Yang Chen,Ya-Wen Cao,Chen-Quan Gan,Jun-Zhang Zhao,Wei-Hua Zheng,Haiwen Jia,Shuai Jiang,Xuwang Li,Hua Li,Yi-Nuo Tu,Jun-Jing Zhang","doi":"10.1038/s41746-026-02371-5","DOIUrl":"https://doi.org/10.1038/s41746-026-02371-5","url":null,"abstract":"Precise delineation of hepatocellular carcinoma (HCC) in abdominal CT is pivotal for early diagnosis and surgical planning, yet remains challenged by morphological heterogeneity, low contrast in small lesions, and scanner variability. To address these limitations, we propose Prompt-Mamba-AF, a framework tailored for robust HCC segmentation. Our method uniquely integrates anatomy-aware prompts to guide feature extraction within liver regions and leverages Mamba-based state-space modeling to capture long-range volumetric dependencies with linear complexity. Furthermore, we introduce structure-aware filtering to enforce topological consistency along lesion boundaries. Extensive validation on the LiTS, 3DIRCADb, and CHAOS benchmarks demonstrates that Prompt-Mamba-AF outperforms current state-of-the-art CNN and Transformer architectures. The model achieves leading Dice similarity and boundary accuracy while maintaining a compact parameter footprint (27.6M). Results indicate significant improvements in small nodule sensitivity and generalization across diverse imaging domains, positioning Prompt-Mamba-AF as an efficient solution for multi-center clinical workflows.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"117 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056415","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 : 2026-01-27DOI: 10.1038/s41746-026-02356-4
Magdalena Fuchs,Zachary M Boyd,Alice Schwarze,Danielle Cosme,Ovidia Stanoi,Yoona Kang,Tobias Kowatsch,Florian von Wangenheim,Dani S Bassett,Kevin N Ochsner,David M Lydon-Staley,Emily B Falk,Peter J Mucha,Mia Jovanova
Digital interventions can change behaviors like alcohol use, but effectiveness varies widely across individuals. Accurately identifying non-responders-i.e., those least (vs. most) likely to change their behavior-before intervention delivery is difficult. Individual intervention effectiveness predictions from prior studies perform only slightly above chance (e.g., AUC ≈0.60; balanced accuracy ≈0.60). We present a novel approach integrating multimodal data across theory-driven domains-including psychological assessments, social network data, and neural responses to alcohol cues-to make ex-ante predictions about the effectiveness of smartphone-delivered alcohol interventions targeting psychological distancing in young adults (Study 1: N = 67; Study 2: N = 114). Demonstrating the feasibility of this approach, random forest models predicted individual differences in intervention effectiveness (Study 1: balanced accuracy = 0.71, 95% CI: 0.69-0.73, p = .020; AUC = 0.87, 95% CI: 0.85-0.88, p = .020) and replicated in a an external test sample (Study 2, balanced accuracy = 0.68; AUC = 0.68, 95% CI: 0.54-0.82), meeting clinical-utility thresholds from prior digital health studies (balanced accuracy = 0.67; correctly classifying (non)responders 67% of the time). Interventions were most effective for participants who perceived their peers as moderate but frequent drinkers. Peer drinking perceptions may serve as a low-burden indicator to support early identification of non-responders in preventive alcohol interventions among young adults. Future work can apply and extend the multimodal approach developed here for adaptive tailoring of digital behavior change interventions in real-world settings.
{"title":"Predicting individual differences in digital alcohol intervention effectiveness through multimodal data.","authors":"Magdalena Fuchs,Zachary M Boyd,Alice Schwarze,Danielle Cosme,Ovidia Stanoi,Yoona Kang,Tobias Kowatsch,Florian von Wangenheim,Dani S Bassett,Kevin N Ochsner,David M Lydon-Staley,Emily B Falk,Peter J Mucha,Mia Jovanova","doi":"10.1038/s41746-026-02356-4","DOIUrl":"https://doi.org/10.1038/s41746-026-02356-4","url":null,"abstract":"Digital interventions can change behaviors like alcohol use, but effectiveness varies widely across individuals. Accurately identifying non-responders-i.e., those least (vs. most) likely to change their behavior-before intervention delivery is difficult. Individual intervention effectiveness predictions from prior studies perform only slightly above chance (e.g., AUC ≈0.60; balanced accuracy ≈0.60). We present a novel approach integrating multimodal data across theory-driven domains-including psychological assessments, social network data, and neural responses to alcohol cues-to make ex-ante predictions about the effectiveness of smartphone-delivered alcohol interventions targeting psychological distancing in young adults (Study 1: N = 67; Study 2: N = 114). Demonstrating the feasibility of this approach, random forest models predicted individual differences in intervention effectiveness (Study 1: balanced accuracy = 0.71, 95% CI: 0.69-0.73, p = .020; AUC = 0.87, 95% CI: 0.85-0.88, p = .020) and replicated in a an external test sample (Study 2, balanced accuracy = 0.68; AUC = 0.68, 95% CI: 0.54-0.82), meeting clinical-utility thresholds from prior digital health studies (balanced accuracy = 0.67; correctly classifying (non)responders 67% of the time). Interventions were most effective for participants who perceived their peers as moderate but frequent drinkers. Peer drinking perceptions may serve as a low-burden indicator to support early identification of non-responders in preventive alcohol interventions among young adults. Future work can apply and extend the multimodal approach developed here for adaptive tailoring of digital behavior change interventions in real-world settings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"7 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056416","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 : 2026-01-27DOI: 10.1038/s41746-026-02396-w
Florence D Mowlem, Jeremiah J Trudeau, Jill V Platko, Jos Bloemers, Emuella Flood, Jessica L Abel, Kelly Dumais, Paul O'Donohoe, Sabrina Grant, Ryan Naville-Cook, Onyekachukwu Illoh, Shannon D Keith, Jing Ju, Megan Fitter, Dorothee Oberdhan, Randall Winnette, Manuela Bossi, Naomi Suminski, Sonya Eremenco, Lindsay Hughes, Amy Fasiczka, Luisana Rojas, Michelle Campbell, Scottie Kern
{"title":"Publisher Correction: Best practice recommendations and considerations for designing and electronically implementing event-driven diaries in clinical trials.","authors":"Florence D Mowlem, Jeremiah J Trudeau, Jill V Platko, Jos Bloemers, Emuella Flood, Jessica L Abel, Kelly Dumais, Paul O'Donohoe, Sabrina Grant, Ryan Naville-Cook, Onyekachukwu Illoh, Shannon D Keith, Jing Ju, Megan Fitter, Dorothee Oberdhan, Randall Winnette, Manuela Bossi, Naomi Suminski, Sonya Eremenco, Lindsay Hughes, Amy Fasiczka, Luisana Rojas, Michelle Campbell, Scottie Kern","doi":"10.1038/s41746-026-02396-w","DOIUrl":"10.1038/s41746-026-02396-w","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"9 1","pages":"77"},"PeriodicalIF":15.1,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12847838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146065398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1038/s41746-025-02291-w
Yan Zhang,Lining Zhang,Zehong Zhang,Yuxi Lin,Zexu Jiang,Fulong Yu
Coronavirus disease 2019 (COVID-19) and other respiratory viral infections, such as influenza and respiratory syncytial virus (RSV), elicit both common and virus-specific host responses. Here, we present an integrative analysis leveraging the COVID-19 Host Genetics Initiative (HGI) GWAS data (freeze 7) and publicly available multi-omics datasets (including influenza/RSV human challenge transcriptomes and plasma proteomics) to construct an explainable AI model for comparing host infection mechanisms between COVID-19 and other viral illnesses. We identified shared antiviral pathways (type I interferon (IFN) signaling) active in host responses to all three viruses, as well as virus-specific mechanisms: for instance, SARS-CoV-2 infection induced uniquely strong coagulation and renin-angiotensin system dysregulation, along with sustained AP-1/MAPK activation, whereas influenza provoked more robust T-cell activation, and RSV triggered an excessive neutrophil-driven inflammatory response. Genetic risk pathway fingerprints from GWAS highlight that COVID-19 severity is associated with variants in IFN and inflammatory pathways, while host genetic effects in influenza point to distinct receptor usage (sialic acid biosynthesis) with minimal overlap. Mendelian randomization (MR) pinpointed key causal proteins for COVID-19 severity, including ABO (blood group glycosyltransferase) and inflammatory mediators, suggesting that host glycomic and immune factors modulate disease outcomes. Our explainable machine learning model integrated these multi-omic features to accurately distinguish COVID-19 from other viral infections, with SHAP interpretation confirming the predominance of the above mechanisms in model predictions. In summary, this cross-omics study provides a comprehensive comparative map of host responses in COVID-19 versus influenza and RSV, yielding biologically interpretable insights into both common antiviral defenses and unique pathogenic pathways. These findings inform the development of targeted therapies (IL-6 or MAPK inhibitors for COVID-19) and broad-spectrum antivirals (enhancing IFN responses) to mitigate severe respiratory viral diseases.
{"title":"Explainable AI multiomics analysis reveals shared and divergent host responses in COVID-19 and influenza.","authors":"Yan Zhang,Lining Zhang,Zehong Zhang,Yuxi Lin,Zexu Jiang,Fulong Yu","doi":"10.1038/s41746-025-02291-w","DOIUrl":"https://doi.org/10.1038/s41746-025-02291-w","url":null,"abstract":"Coronavirus disease 2019 (COVID-19) and other respiratory viral infections, such as influenza and respiratory syncytial virus (RSV), elicit both common and virus-specific host responses. Here, we present an integrative analysis leveraging the COVID-19 Host Genetics Initiative (HGI) GWAS data (freeze 7) and publicly available multi-omics datasets (including influenza/RSV human challenge transcriptomes and plasma proteomics) to construct an explainable AI model for comparing host infection mechanisms between COVID-19 and other viral illnesses. We identified shared antiviral pathways (type I interferon (IFN) signaling) active in host responses to all three viruses, as well as virus-specific mechanisms: for instance, SARS-CoV-2 infection induced uniquely strong coagulation and renin-angiotensin system dysregulation, along with sustained AP-1/MAPK activation, whereas influenza provoked more robust T-cell activation, and RSV triggered an excessive neutrophil-driven inflammatory response. Genetic risk pathway fingerprints from GWAS highlight that COVID-19 severity is associated with variants in IFN and inflammatory pathways, while host genetic effects in influenza point to distinct receptor usage (sialic acid biosynthesis) with minimal overlap. Mendelian randomization (MR) pinpointed key causal proteins for COVID-19 severity, including ABO (blood group glycosyltransferase) and inflammatory mediators, suggesting that host glycomic and immune factors modulate disease outcomes. Our explainable machine learning model integrated these multi-omic features to accurately distinguish COVID-19 from other viral infections, with SHAP interpretation confirming the predominance of the above mechanisms in model predictions. In summary, this cross-omics study provides a comprehensive comparative map of host responses in COVID-19 versus influenza and RSV, yielding biologically interpretable insights into both common antiviral defenses and unique pathogenic pathways. These findings inform the development of targeted therapies (IL-6 or MAPK inhibitors for COVID-19) and broad-spectrum antivirals (enhancing IFN responses) to mitigate severe respiratory viral diseases.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"77 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056414","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 : 2026-01-27DOI: 10.1038/s41746-026-02373-3
Jewel N White,Lakota Watson,Yang Wang,Giancarlo Colloca,Jonathan Michael Heagerty,Sida Li,Barbara Brawn,Amitabh Varshney,Roni Shafir,Carmen-Édith Belleï-Rodriguez,Luana Colloca
Digital environments are increasingly used to study social and pain-related behaviors. Empathy and contextual factors influence observationally induced placebo analgesia. We tested whether state empathy (i.e., immediate affective and cognitive responses to another's experience) differs when observing a demonstrator in immersive VR versus 2D video, and whether this modulation affects placebo hypoalgesia. Forty-seven participants observed a human or avatar demonstrator receiving painful stimulation with or without placebo, then experienced the same stimulations. Observation induced significant placebo hypoalgesia for pain intensity and unpleasantness. Human demonstrators evoked greater cognitive empathy, while placebo treatments reduced empathy across contexts. Analgesic effects were stronger in 2D after observing humans, but in VR, avatars induced greater placebo effects. Placebo responsiveness was related to trait empathy in the VR-Human condition; however, state empathy did not mediate the effect. Our findings highlight that demonstrator characteristics and immersion critically shape the social transfer of placebo effects.
{"title":"Context-dependent placebo hypoalgesia through observational learning: the role of empathy in immersive and non-immersive environments.","authors":"Jewel N White,Lakota Watson,Yang Wang,Giancarlo Colloca,Jonathan Michael Heagerty,Sida Li,Barbara Brawn,Amitabh Varshney,Roni Shafir,Carmen-Édith Belleï-Rodriguez,Luana Colloca","doi":"10.1038/s41746-026-02373-3","DOIUrl":"https://doi.org/10.1038/s41746-026-02373-3","url":null,"abstract":"Digital environments are increasingly used to study social and pain-related behaviors. Empathy and contextual factors influence observationally induced placebo analgesia. We tested whether state empathy (i.e., immediate affective and cognitive responses to another's experience) differs when observing a demonstrator in immersive VR versus 2D video, and whether this modulation affects placebo hypoalgesia. Forty-seven participants observed a human or avatar demonstrator receiving painful stimulation with or without placebo, then experienced the same stimulations. Observation induced significant placebo hypoalgesia for pain intensity and unpleasantness. Human demonstrators evoked greater cognitive empathy, while placebo treatments reduced empathy across contexts. Analgesic effects were stronger in 2D after observing humans, but in VR, avatars induced greater placebo effects. Placebo responsiveness was related to trait empathy in the VR-Human condition; however, state empathy did not mediate the effect. Our findings highlight that demonstrator characteristics and immersion critically shape the social transfer of placebo effects.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"40 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056772","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：