Computers in biology and medicine最新文献

{"title":"Multiscale modeling of posture-dependent cerebrovascular hemodynamics with autoregulatory coupling","authors":"Hyun Jin Kim ,&nbsp;Chang Min Lee ,&nbsp;Youngjae Choi ,&nbsp;Hyeyeon Chang ,&nbsp;Keun-Hwa Jung","doi":"10.1016/j.compbiomed.2026.111502","DOIUrl":"10.1016/j.compbiomed.2026.111502","url":null,"abstract":"<div><div>Cerebral blood flow is maintained through complex autoregulatory mechanisms that compensate for systemic and postural changes to preserve stable perfusion. We investigate the coupled effects of aortic pressure, body posture, and vessel wall stiffness on cerebrovascular hemodynamics using a multiscale modeling framework. A comprehensive cerebrovascular model was developed that incorporates both arterial and venous networks down to the precapillary and postcapillary levels, coupled with a three-dimensional perfusion domain. The framework integrates passive vessel mechanics and active autoregulatory control to simulate arteriolar dilation and constriction in response to pressure and metabolic demand. Simulations were performed across a wide range of aortic pressures (30–150 mmHg) and body postures (supine, upright, inverted) while varying wall stiffness to assess the impact of compliance. Arterial deformation and total vascular volume were strongly influenced by both systemic and gravitational loading, whereas venous volume remained relatively stable across pressure variations but changed markedly with posture due to hydrostatic effects. Active autoregulation attenuated these changes by dynamically adjusting arteriolar diameters to maintain near-constant cerebral blood flow. Increased vascular compliance amplified posture-induced volume changes and the resulting autoregulatory response, whereas higher stiffness attenuated both. The proposed framework elucidates how vascular wall mechanics and autoregulatory capacity jointly stabilize cerebral perfusion under varying physiological conditions. These findings advance the biomechanical understanding of posture-dependent cerebrovascular regulation and establish a foundation for future investigations linking cerebral hemodynamics to impaired autoregulation and vessel wall remodeling in disease.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111502"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Fully automated quantitative lung ultrasound spectroscopy for the differential diagnosis of lung diseases: The first multicenter in-vivo clinical study” [Comput. Biol. Med. (200), 1 January 2026, 111365]","authors":"Mattia Perpenti ,&nbsp;Federico Mento ,&nbsp;Giovanni Pierro ,&nbsp;Alessandro Perrotta ,&nbsp;Tiziano Perrone ,&nbsp;Andrea Smargiassi ,&nbsp;Riccardo Inchingolo ,&nbsp;Libertario Demi","doi":"10.1016/j.compbiomed.2026.111493","DOIUrl":"10.1016/j.compbiomed.2026.111493","url":null,"abstract":"","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111493"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146008853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstructing in-vitro and in-vivo signals and parameters in networks of elastic vessels using physics-informed neural networks","authors":"J. Orera,&nbsp;J. Mairal,&nbsp;L. Sánchez-Fuster,&nbsp;J. Murillo","doi":"10.1016/j.compbiomed.2026.111472","DOIUrl":"10.1016/j.compbiomed.2026.111472","url":null,"abstract":"<div><div>The reconstruction of waveforms and hidden parameters is crucial for the physical modeling of steady and transient flows in networks of elastic vessels (arteries), where many mechanical properties are not directly measurable. This work investigates the potential of Physics-Informed Neural Networks (PINNs) to address the challenge of reconstructing pressure and flow signals and inferring parameters from experimental data. We incorporate the zero-dimensional (0D) system of coupled differential equations that describe flow in elastic vessels into the neural network, which we call 0D-PINN. We evaluate our methodology with several test cases representing different dynamical systems, including an experimental mock arterial network with 37 silicone vessels replicating the human arterial system, as well as a clinical case based on in-vivo MRI data from a healthy adult’s thoracic aorta. It is shown that coupling 0D models with Physics-Informed Neural Networks (PINNs) enables the recovery of parameters and waveforms from experimental in-vitro or in-vivo data.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111472"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Between minds and machines: A neurocognitive comparison of human and chatbot interaction in language learning","authors":"Gamze Turun Ozel ,&nbsp;Semin Kazazoglu ,&nbsp;Burcak Yavuz ,&nbsp;Emir Rusen","doi":"10.1016/j.compbiomed.2026.111499","DOIUrl":"10.1016/j.compbiomed.2026.111499","url":null,"abstract":"<div><div>This study explores neurocognitive differences between human–human interaction (HHI) and human–chatbot interaction (HCI) during English-speaking tasks using EEG analysis. Results showed that HHI elicited significantly greater neural activation, particularly in the left frontal and temporal regions (F3, F7, T3), which are associated with language processing and social cognition. The F3 site exhibited the strongest difference (HHI: 27.09 vs. HCI: 15.5, p &lt; .001, d = −4.02). EEG band analysis revealed higher delta activity during HCI, indicating lower cortical arousal and attentional engagement, while HHI showed greater alpha and beta power (alpha: 6.5 % vs. 2.1 %; beta: 12.1 % vs. 2.4 %), reflecting enhanced cognitive processing and emotional salience. These patterns extended to central, temporal, and parieto-occipital regions, with consistently stronger beta activity in HHI. Findings suggest that natural human interaction elicits deeper and more distributed neural engagement than chatbot communication, offering key insights into the cognitive and emotional dimensions of technology-mediated language use in educational and social contexts.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111499"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146017502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Helical static-mixer insert for pediatric and neonatal gas blending: RANS-CFD comparison of commercial and in-house monolithic designs","authors":"Shirley Ferraz Crispilho ,&nbsp;Paulo Cesar Duarte Junior ,&nbsp;Martin Poulsen Kessler ,&nbsp;Rudolf Huebner ,&nbsp;Altibano Ortenzi","doi":"10.1016/j.compbiomed.2026.111475","DOIUrl":"10.1016/j.compbiomed.2026.111475","url":null,"abstract":"<div><div>Accurate blending of oxygen and air in pediatric and neonatal respiratory support depends on compact connectors that promote efficient mixing without generating excessive pressure drop or dead volume. In current clinical practice, commercially available T-shaped connectors are often used as passive mixers, but their internal geometry was not originally optimized for this purpose. In this work, the original commercial connector (Geometry A), an in-house modified multi-part connector incorporating a static insert (Geometry B), and a new monolithic helical static-mixer insert (Geometry C) were evaluated under identical flow conditions. Three-dimensional Reynolds-averaged Navier–Stokes computational fluid dynamics simulations were performed to represent oxygen–nitrogen mixing in high-flow nasal cannula circuits, considering realistic flow rates and boundary conditions. For each geometry, mixture quality at the outlet was assessed from the spatial distribution of species mass fraction, hydraulic performance was quantified by the device pressure drop, and residence-time behavior for the helical insert was obtained from a transient scalar-pulse simulation. Geometry B improved outlet homogeneity relative to the commercial connector but required several assembled parts, which complicates handling and sterilization. Geometry C, designed as a monolithic helical static mixer, produced more uniform gas mixing than both previous configurations while maintaining pressure drops within ranges compatible with pediatric and neonatal use. These results indicate that the proposed helical insert has the potential to replace the current multi-part in-house adaptation and to offer a more effective alternative to standard commercial connectors when implemented as a monolithic medical-grade component.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111475"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146008969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal diagnosis of Parkinson’s disease with an internet-based collaborative agent architecture of medical language models","authors":"Eugenio Peixoto Junior ,&nbsp;Felipe Cordeiro de Sousa ,&nbsp;Junxin Chen ,&nbsp;David Camacho ,&nbsp;Stephen Rathinaraj Benjamin ,&nbsp;Victor Hugo C. de Albuquerque","doi":"10.1016/j.compbiomed.2026.111468","DOIUrl":"10.1016/j.compbiomed.2026.111468","url":null,"abstract":"<div><div>Parkinson’s disease (PD) remains one of the most prevalent neurodegenerative disorders, where delays in diagnosis compromise therapeutic outcomes and increase healthcare costs. Conventional unimodal approaches, based on voice, sensors, or imaging, face critical limitations, including small datasets, lack of reproducibility, and high infrastructure demands. To address these challenges, the proposed multimodal agent-based architecture integrates medical language models, audio signals, and neuroimaging, and is supported by data–machine learning pipelines and an edge–cloud infrastructure. The system leverages ensemble learning, large and vision language models, and Retrieval-Augmented Generation (RAG) to enhance clinical decision support. The transparency of the model was supported by explainability techniques (SHapley Additive exPlanations, permutation importance, partial dependence, and individual conditional expectation), which highlighted the main audio and sensor variables responsible for the predictions. Experimental evaluation confirmed the effectiveness of multimodal fusion. When integrated, the architecture achieved robust performance, with an accuracy of 0.86, an F1-score above 0.88, ROC-AUC greater than 0.93, and both sensitivity and specificity above 0.89. Calibration and hypothesis tests were validated by a low Brier score of 0.205 and an Expected Calibration Error of 0.151, while Decision Curve Analysis confirmed clinical relevance by minimizing false negatives, critical for early screening, and reducing redundant interventions. Multimodal fusion produced accurate, well-calibrated, and interpretable risk estimates for PD screening; larger prospective studies and cost-effectiveness analyses are needed to consolidate clinical applicability.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111468"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph neural network-guided identification and biological evaluation of potential AKT1 inhibitors for triple-negative breast cancer","authors":"Ravishankar Jaiswal ,&nbsp;Girdhar Bhati ,&nbsp;Santosh Shukla ,&nbsp;Shakil Ahmed ,&nbsp;Mohammad Imran Siddiqi","doi":"10.1016/j.compbiomed.2026.111481","DOIUrl":"10.1016/j.compbiomed.2026.111481","url":null,"abstract":"<div><div>Triple-negative breast cancer (TNBC) presents a significant therapeutic challenge due to its aggressive behavior and lack of targeted therapies. The PI3K/AKT/mTOR signaling pathway, particularly AKT1, is frequently dysregulated in TNBC, driving disease progression. Despite extensive research, many clinically evaluated AKT1 inhibitors have encountered challenges related to both efficacy and tolerability, highlighting the need for novel therapeutics. Here, we employed graph neural networks (GNNs) for molecular graph-based prediction of potential AKT1 inhibitors. Six GNN architectures, including attention-based (AttentiveFP, GATv2Conv, TransformerConv) and non-attention-based (GCNConv, GINConv, GraphSAGE) models were trained and benchmarked against traditional machine learning (ML) methods using random and scaffold-based data splits. To enhance predictive relevance and model generalizability, we integrated phenotypic screening data from breast cancer (BC) cell lines alongside AKT1 bioassay data to capture broader pathway effects. Screening the Maybridge chemical library, we identified 9 novel scaffold compounds through consensus hit selection, molecular docking, and novelty filtration. Enzymatic validation confirmed 4 early-stage AKT1 inhibitors with low-micromolar potency (IC₅₀ down to 2.5 μM). Explainable AI analyses using Integrated Gradients and Captum saliency maps highlighted key structural features driving AKT1 inhibition, providing interpretable structure-activity relationship (SAR) insights. Scaffold diversity analysis further confirmed that the validated hits occupy chemical space distinct from known AKT1 inhibitors. Overall, this study presents an interpretable AI-driven discovery framework that identifies novel AKT1 inhibitor scaffolds and provides a validated starting point for hit-to-lead optimization in TNBC drug discovery.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111481"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Brain dysfunction assessment in Alzheimer's disease: A phase-space projection and interactive signal decomposition framework” [Comput. Biol. Med. (2026) 111440 201]","authors":"Wanus Srimaharaj","doi":"10.1016/j.compbiomed.2026.111483","DOIUrl":"10.1016/j.compbiomed.2026.111483","url":null,"abstract":"","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111483"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling for dynamical aspects of smoking abuse with e-cigarette users: Threshold dynamics and optimal control strategies","authors":"Abdullah ,&nbsp;Ghaus ur Rahman ,&nbsp;Faira Kanwal Janjua ,&nbsp;Ravi P. Agarwal ,&nbsp;J.F. Gómez-Aguilar","doi":"10.1016/j.compbiomed.2026.111457","DOIUrl":"10.1016/j.compbiomed.2026.111457","url":null,"abstract":"<div><div>Smoking is one of the leading causes of mortality and morbidity around the globe, and e-cigarette use introduces new challenges to public health concerns. Understanding the connection between traditional smoking, e-cigarette adoption, and cessation dynamics is critical for developing effective tobacco-related harm reduction initiatives. In this paper, we present a compartmental mathematical model that captures the transitions between nonsmokers, smokers, e-cigarette users, and quitters while explicitly integrating relapse and cessation behaviors. In the paradigm, e-cigarettes serve as a harm-reduction transition for current smokers, from which users can either quit entirely or return to smoking. The model is analyzed using threshold dynamics to determine the conditions under which smoking behavior persists or declines in a population, and stability analysis is employed to characterize equilibrium states and identify crucial parameters that influence long-term outcomes. Building on this paradigm, we formulate an optimal-control problem by linking control functions to intervention-sensitive processes such as the transition from smoking to e-cigarette usage, the cessation rate among e-cigarette users, and the relapse rate from vaping to smoking. Following harm-reduction principles, the framework prioritizes reducing explosive smoking while discouraging consistent vaping, provided that doing so does not increase smoking prevalence. This paradigm enables researchers to investigate how policy-relevant levers may alter the trajectory of tobacco use over time; however, the report does not compare specific treatments. The model can be extended with numerical simulations that compare the efficacy of interventions like awareness campaigns, taxation, and cessation programs. Such simulations might also include cost-effectiveness studies to determine how limited public health resources could be best allocated across various initiatives. The proposed paradigm provides a theoretical foundation for embedding public health interventions into the dynamics of smoking and e-cigarette use, allowing policymakers and academics to explore how different strategies affect long-term prevalence and reduction outcomes. The graphs illustrate the difference between uncontrolled baseline dynamics and the implementation of an effective management method, demonstrating how interventions can accelerate smoking prevalence reductions. The findings provide a theoretical basis for evaluating how interventions can influence the trajectories of tobacco/vaping use, without claiming to have identified a single “most effective” policy. Future developments could include comparative simulations and cost-effectiveness assessments to help inform targeted decisions related to public health.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111457"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the renal therapeutic potential of Nypa fruticans leaves: An integrated experimental and in silico approach","authors":"Farhana Islam ,&nbsp;Mostafa Kamal ,&nbsp;Shoeb Ahmad ,&nbsp;Masum Shahriar ,&nbsp;Fariya Islam Rodru ,&nbsp;Md. Nazmul Hasan ,&nbsp;Md. Nazmul Hasan Zilani ,&nbsp;Md. Ataur Rahman ,&nbsp;Shahad Saif Khandker ,&nbsp;Saquiba Yesmine","doi":"10.1016/j.compbiomed.2026.111482","DOIUrl":"10.1016/j.compbiomed.2026.111482","url":null,"abstract":"<div><div>Chronic kidney disease (CKD) is a progressive, irreversible disorder associated with renal dysfunction, inflammation, and oxidative stress. Given the limitations of current therapies, this study assessed the renal curative effects of <em>Nypa fruticans</em> ethyl acetate leaf extract (EaNFL) in a gentamicin-induced nephrotoxicity rat model. GC‒MS and HPLC analyses identified 23 bioactive compounds in EaNFL, including rosmarinic acid, quercetin, and (−)-epicatechin, which were selected based on ADMET profiling, Lipinski's rule, and DFT analysis. These compounds were further investigated through computational studies against two renal targets: the AT1 receptor (PDB ID: 4YAY) and SGLT2 (PDB ID: 7VSI). Treatment with EaNFL, particularly at 400 mg/kg body weight and in combination therapy, significantly improved renal function and normalized biochemical and hematological parameters, likely due to its potent antioxidant and anti-inflammatory properties. Histopathological data supported these findings, showing reduced tubular necrosis, glomerular damage, and inflammation, especially in the high-dose groups. DFT analysis revealed that rosmarinic acid had the highest HOMO–LUMO energy gap (ΔE = 0.1314 eV), suggesting high chemical reactivity and potential biological compatibility. Molecular docking identified quercetin, rosmarinic acid, and (−)-epicatechin as the top binders, with rosmarinic acid showing the strongest affinity and forming a stable complex, as confirmed by 100 ns MDS. Taken together, the <em>in vivo</em> and <em>in silico</em> results indicate that EaNFL offers renoprotective benefits by targeting the RAAS and glucose transport pathways while also mitigating oxidative stress and inflammation. These findings demonstrate its therapeutic potential and warrant further investigation into its bioactive constituents and potential clinical use in renal treatment.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111482"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}