Computers in biology and medicine最新文献

{"title":"Analysis of binding kinetics and mass transport in SPR-based biosensor using the Generalized Integral Transform Technique and the Markov Chain Monte Carlo Method","authors":"Carlos Henrique Rodrigues de Moura ,&nbsp;Carlos Adriano Moreira da Silva ,&nbsp;Josiel Lobato Ferreira ,&nbsp;Emanuel Negrão Macêdo ,&nbsp;João Nazareno Nonato Quaresma ,&nbsp;Renato Machado Cotta","doi":"10.1016/j.compbiomed.2025.111129","DOIUrl":"10.1016/j.compbiomed.2025.111129","url":null,"abstract":"<div><div>The present work addresses biomolecular interactions in Surface Plasmon Resonance (SPR)-based biosensors, explicitly focusing on mass transport and binding kinetics. The Generalized Integral Transform Technique (GITT) is employed to solve the nonlinear system of partial differential equations describing mass transport, while the Markov Chain Monte Carlo (MCMC) method is adopted for accurately estimating the kinetic constants of the model. The outcomes were corroborated with simulated measurements and validated against experimental data related to the binding of the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2 bound to the cell receptor angiotensin-converting enzyme 2 (ACE2) in the Biacore system. Our findings demonstrate the efficacy of the GITT in describing the dynamics of average concentrations of the free analyte and of the bound analyte-receptor complex, aligning with results obtained in prior studies. Furthermore, our results demonstrate that the MCMC method is a robust tool for estimating model kinetic constants, with estimates closely approximating the exact values and falling within a 99 % confidence interval. The estimated average concentrations concurred with simulated measurements, even when accounting for Gaussian noise. The experimental validation results strengthen our conclusions, aligning the model parameter estimates with reference values from the literature. Therefore, this study suggests that the adopted mathematical model and numerical methodology hold significant potential for analyzing and comprehending biomolecule binding data, representing a valuable tool for studying complex biomolecular interactions.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 ","pages":"Article 111129"},"PeriodicalIF":6.3,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157573","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":"Immunoinformatics-driven construction of a next-generation epitope-based vaccine from conserved hypothetical proteins of M. tuberculosis for enhanced TB control","authors":"Shaista Arif ,&nbsp;Farheen Aslam","doi":"10.1016/j.compbiomed.2025.111144","DOIUrl":"10.1016/j.compbiomed.2025.111144","url":null,"abstract":"<div><div><em>Mycobacterium tuberculosis</em> (<em>Mtb</em>) has afflicted humanity for centuries. BCG is the only vaccine available for TB, but it shows limited protective efficacy in adults. Therefore, there is an urgent need to develop universal vaccines for controlling TB worldwide. In this study, four conserved mycobacterial hypothetical proteins (HPs) were analyzed for their immunological, structural, and functional properties using various computational tools. The IFN-γ-inducing MHC class I and II binding peptides of the four conserved HP antigens were predicted by the IFNepitope 2.0 server. After detailed in silico validations, the most immunogenic, non-toxic, non-allergenic B-cell, CTL and HTL epitopes with broad population coverage and conservancy were selected for developing a new epitope-based vaccine <strong>(</strong>IDE) construct. Furthermore, the final vaccine construct was verified for its antigenicity, toxicity, allergenicity and solubility properties. Molecular docking and MD simulations analyses showed conformational stability and high binding affinity of the designed vaccine with TLR4, MHC-I, and MHC-II immune receptors. In silico immune simulation revealed the production of high levels of IgG, T-helper, T-cytotoxic cells, IFN-γ and interleukins against the final vaccine construct. Thus, the IDE vaccine could be a potent next-generation epitope-based vaccine candidate to stimulate both humoral and cellular responses against <em>Mtb</em>. However, further animal studies are needed to validate the immunogenicity and biological efficacy of the proposed vaccine construct.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 ","pages":"Article 111144"},"PeriodicalIF":6.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157576","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":"New algorithm to predict colorectal cancer based on fecal volatile organic compounds profile","authors":"Laura Ripoll ,&nbsp;Hector Gisbert ,&nbsp;Iván Rubio ,&nbsp;David Guill-Berbegal ,&nbsp;Antonio Canals ,&nbsp;Rodrigo Jover ,&nbsp;Lorena Vidal","doi":"10.1016/j.compbiomed.2025.111093","DOIUrl":"10.1016/j.compbiomed.2025.111093","url":null,"abstract":"<div><div>In this study, an algorithm designed for the analysis of fecal samples for colorectal cancer diagnostics, utilizing the data from the advanced technique of thermal-desorption-gas chromatography-mass spectrometry (TD-GC-MS), is constructed. The algorithm performs a comprehensive analysis across the entire spectral range to identify compound patterns for differentiating among three distinct health states: colorectal cancer, colorectal adenomas and controls with normal colonoscopy. The algorithm underwent a rigorous optimization process, resulting in a sensitivity and specificity of 100 %, effectively eliminating both false positives and false negatives. During the validation phase, the algorithm demonstrated remarkable performance, with sensitivity ranging from 74 % to 68 %, specificity ranging from 58 % to 52 %, and accuracy 66 %–62 % (range across twenty randomized train-test splits). Notably, in the context of polyp samples, the algorithm obtained a sensitivity range from 54 % to 50 %, even when trained with data from only healthy individuals (i.e., controls) and cancer patients. Moreover, a detailed table of compounds and their probabilities of occurrence in cancer, adenomas, and healthy samples is provided, offering insight into the interpretability of the algorithm. This qualitative approach signals a significant advancement in diagnostic precision and promises to enhance early detection of colorectal cancer, marking a substantial contribution to the field.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"197 ","pages":"Article 111093"},"PeriodicalIF":6.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154848","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":"Design and prediction of soft-to-hard transitions using bioinspired hierarchical-gradient designs and hybrid stacking machine learning","authors":"Masoud Shirzad ,&nbsp;Juhyun Kang ,&nbsp;Mahdi Bodaghi ,&nbsp;Seung Yun Nam","doi":"10.1016/j.compbiomed.2025.111143","DOIUrl":"10.1016/j.compbiomed.2025.111143","url":null,"abstract":"<div><div>The fabrication of soft-to-hard transition phases poses significant challenges due to the disparity in mechanical properties across the interface. Among all soft-to-hard natural tissues, the tendon-to-bone interface is particularly complex, exhibiting both hierarchical and gradient structural characteristics. This study aims to design, fabricate, and optimize bioinspired structures that replicate tendon-to-bone interfaces by investigating their fundamental relationships with their natural counterparts. To achieve this, various designs featuring simple and hierarchical architectures with negative Poisson's ratio (NPR) were integrated with simple and gradient positive Poisson's ratio (PPR) structures to mimic the physical properties of enthesis. The results demonstrated that the novel hierarchical-gradient designs enhanced Young's modulus and failure force by up to 58.1 %. The finite element method (FEM) was employed to accelerate the prediction of mechanical properties, and a hybrid stacking machine learning (HSML) model trained on FEM results further improved the prediction accuracy, achieving an error of 2 %. The HSML method outperformed traditional approaches like decision trees and convolutional neural networks (CNNs) on small datasets, highlighting its suitability for such applications. Additionally, this study demonstrates that mimicking the energy-absorbing interface between natural soft and hard tissues significantly improves both Young's modulus and failure force in these complex structures.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 ","pages":"Article 111143"},"PeriodicalIF":6.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157571","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":"Improving knee joint angle prediction through Dynamic Contextual Focus and Gated Linear Units","authors":"Lyes Saad Saoud ,&nbsp;Humaid Ibrahim ,&nbsp;Ahmad Aljarah ,&nbsp;Irfan Hussain","doi":"10.1016/j.compbiomed.2025.111119","DOIUrl":"10.1016/j.compbiomed.2025.111119","url":null,"abstract":"<div><div>Real-time, accurate knee joint angle prediction is crucial in biomechanics and rehabilitation, where precision supports improved patient outcomes and more responsive exoskeleton control. This paper introduces FocalGatedNet, a novel deep learning framework combining Dynamic Contextual Focus (DCF) Attention and Gated Linear Units (GLUs) to enhance feature dependency capture, making it highly effective for multi-step gait trajectory prediction. Unlike conventional approaches that rely solely on recurrent or convolutional architectures, FocalGatedNet leverages attention-based mechanisms tailored for time-series forecasting, ensuring superior temporal dependency modeling. Our extensive evaluation of FocalGatedNet on a comprehensive, multimodal gait dataset compares it against top-performing models across multiple prediction intervals (20 ms, 60 ms, 80 ms, and 100 ms). Results show that FocalGatedNet delivers substantial gains in predictive accuracy, with marked improvements in Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Notably, FocalGatedNet consistently outperforms transformer-based models, demonstrating enhanced robustness across varying movement conditions. For instance, at the 80 ms prediction window, FocalGatedNet achieves reductions in MAE by up to 24%, RMSE by up to 14%, and MAPE by up to 36% over the Transformer model, highlighting its effectiveness in capturing complex knee joint movement patterns. Additionally, we conduct an ablation study to validate the role of GLU and DCF Attention in performance gains, confirming that feature gating significantly enhances model efficiency. Experimental evaluations also assess the impact of sensor noise on prediction accuracy, ensuring real-world applicability. Also, FocalGatedNet works with less time consumption than many other deep learning models. Its efficient inference speed, coupled with high accuracy, makes it a viable solution for deployment in real-time gait analysis and exoskeleton-assisted rehabilitation. Thus, FocalGatedNet is quite helpful and relatively reliable for real-time biomechanical applications. The model implementation is accessible in the GitHub repository: <span><span>https://github.com/LyesSaadSaoud/FocalGatedNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"197 ","pages":"Article 111119"},"PeriodicalIF":6.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154847","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":"GRAPHITE: Graph-based interpretable tissue examination for enhanced explainability in breast cancer histopathology","authors":"Raktim Kumar Mondol ,&nbsp;Ewan K.A. Millar ,&nbsp;Peter H. Graham ,&nbsp;Lois Browne ,&nbsp;Arcot Sowmya ,&nbsp;Erik Meijering","doi":"10.1016/j.compbiomed.2025.111106","DOIUrl":"10.1016/j.compbiomed.2025.111106","url":null,"abstract":"<div><div>Explainable AI (XAI) in medical histopathology is essential for enhancing the interpretability and clinical trustworthiness of deep learning models in cancer diagnosis. However, the black-box nature of these models often limits their clinical adoption. We introduce GRAPHITE (Graph-based Interpretable Tissue Examination), a post-hoc explainable framework designed for breast cancer tissue microarray (TMA) analysis. GRAPHITE employs a multiscale approach, extracting patches at various magnification levels, constructing an hierarchical graph, and utilising graph attention networks (GAT) with scalewise attention (SAN) to capture scale-dependent features. We trained the model on 140 tumour TMA cores and four benign whole slide images from which 140 benign samples were created, and tested it on 53 pathologist-annotated TMA samples. GRAPHITE outperformed traditional XAI methods, achieving a mean average precision (mAP) of 0.56, an area under the receiver operating characteristic curve (AUROC) of 0.94, and a threshold robustness (ThR) of 0.70, indicating that the model maintains high performance across a wide range of thresholds. In clinical utility, GRAPHITE achieved the highest area under the decision curve (AUDC) of 4.17e+5, indicating reliable decision support across thresholds. These results highlight GRAPHITE’s potential as a clinically valuable tool in computational pathology, providing interpretable visualisations that align with the pathologists’ diagnostic reasoning and support precision medicine.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"197 ","pages":"Article 111106"},"PeriodicalIF":6.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154833","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":"Leaflet thrombosis automatic identification in transcatheter aortic valves using 4DCT","authors":"Laura Busto ,&nbsp;César Veiga ,&nbsp;Carlos Martínez ,&nbsp;Olivia Zulaica ,&nbsp;José A. González-Nóvoa ,&nbsp;Silvia Campanioni ,&nbsp;José Luis Alba ,&nbsp;Pablo Juan-Salvadores ,&nbsp;Manuel Barreiro-Pérez ,&nbsp;Víctor Jiménez ,&nbsp;José A. Baz ,&nbsp;Andrés Íñiguez","doi":"10.1016/j.compbiomed.2025.111130","DOIUrl":"10.1016/j.compbiomed.2025.111130","url":null,"abstract":"<div><div>Leaflet thrombosis (LT) is a significant complication of transcatheter aortic valve implantation (TAVI) that impacts patient outcomes and transcatheter heart valves (THVs) long-term durability. Subclinical LT (SLT), manifested as hypo-attenuated leaflet thickening (HALT) and potential reduced leaflet motion (RELM), is challenging to diagnose due to its reliance on manual evaluation and observer variability. Although computed tomography (CT) is the preferred imaging modality for LT detection, manual assessment remains labor-intensive and prone to inconsistencies. Additionally, as TAVI procedures are increasingly performed in younger patients, concerns about THVs long-term durability —and particularly regarding LT— are growing. This study aims to develop automated segmentation models using the nnU-Net architecture to detect and characterize thrombi in 4DCT scans of TAVI patients. The methodology includes three main steps: manual annotation of the dataset, thrombus segmentation using eight distinct nnU-Net models, and evaluation based on segmentation metrics and clinical thrombus information. Several models achieved precision values exceeding 0.8 for LT patients, demonstrating the potential of automated segmentation to enhance LT detection. Furthermore, the observed variations in thrombus volume across the cardiac cycle highlight the importance of selecting the optimal phase for LT assessment, suggesting that dynamic evaluation could improve diagnostic accuracy. This work lays the groundwork for early LT detection and the development of predictive biomarkers, offering automated LT detection and characterization that reduces manual effort and observer variability. The proposed dynamic 4DCT analyses could improve LT diagnostics and inform personalized anticoagulation strategies, potentially leading to better long-term outcomes.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"197 ","pages":"Article 111130"},"PeriodicalIF":6.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154846","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":"CACTUS: Multiview classifier for Punctate White Matter Lesions detection & segmentation in cranial ultrasound volumes","authors":"Flora Estermann ,&nbsp;Valerie Kaftandjian ,&nbsp;Philippe Guy ,&nbsp;Philippe Quetin ,&nbsp;Philippe Delachartre","doi":"10.1016/j.compbiomed.2025.111085","DOIUrl":"10.1016/j.compbiomed.2025.111085","url":null,"abstract":"<div><div>Punctate white matter lesions (PWML) are the most common white matter injuries found in preterm neonates, with several studies indicating a connection between these lesions and negative long-term outcomes. Automated detection of PWML through ultrasound (US) imaging could assist doctors in diagnosis more effectively and at a lower cost than MRI. However, this task is highly challenging because of the lesions’ small size and low contrast, and the number of lesions can vary significantly between subjects. In this work, we propose a two-phase approach: (1) Segmentation using a vision transformer to increase the detection rate of lesions. (2) Multi-view classification leveraging cross-attention to reduce false positives and enhance precision. We also investigate multiple postprocessing approaches to ensure prediction quality and compare our results with what is known in MRI. Our method demonstrates improved performance in PWML detection on US images, achieving recall and precision rates of 0.84 and 0.70, respectively, representing an increase of 2% and 10% over the best published US models. Moreover, by reducing the task to a slightly simpler problem (detection of MRI-visible PWML), the model achieves 0.82 recall and 0.89 precision, which is equivalent to the latest method in MRI.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"197 ","pages":"Article 111085"},"PeriodicalIF":6.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154834","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":"Geometric deep learning adapted to prediction of liver resection zone","authors":"Joy Rakshit ,&nbsp;Robert Kreher ,&nbsp;Tobias Huber ,&nbsp;Hauke Lang ,&nbsp;Florentine Huettl ,&nbsp;Sylvia Saalfeld","doi":"10.1016/j.compbiomed.2025.111103","DOIUrl":"10.1016/j.compbiomed.2025.111103","url":null,"abstract":"<div><div>Due to patient-specific anatomical variations in the presence of liver cancer, resection planning can be complex requiring thorough preoperative planning. In addition to the calculation of the Future Liver Remnant, the assessment of any vascular and biliary structures that may be at risk is essential to minimize postoperative morbidity and mortality. Despite the progress of modern technologies, this resection planning is still mostly performed mentally, but can be supported by volumetric calculations or planning on a three-dimensional (3D) model.</div><div>The aim of this work is to investigate the effectiveness of geometric deep learning (DL) in predicting liver resection zones. We adopted a geometric DL framework, specifically RandLA-Net, a lightweight and efficient neural network designed for semantic segmentation of large-scale 3D point clouds, to support surgical planning for liver tumor resections using 3D geometric data, presented in either mesh or point cloud format. RandLA-Net can process up to one million points in a single pass and operates up to 200 times faster than comparable frameworks, making it particularly well suited for high-resolution anatomical data in clinical settings.</div><div>We conducted our experiment in two stages. In the first stage, the pilot study, we evaluated two geometric deep learning models in combination with four different loss functions: Cross-Entropy (CE), Dice coefficient (DICE), Intersection over Union (IoU), and a hybrid loss (a combination of CE and IoU) to efficiently predict the resection volume. Among all the configurations tested, RandLA-Net combined with hybrid loss achieved the best performance. In the second stage, the extended study, we increased the dataset size and repeated the experiment using the best-performing configuration identified in the pilot study, with minor modifications. The extended study demonstrated improved performance, with a mean IoU of 0.76, F1-score of 0.84, precision of 0.86, and recall of 0.82.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"197 ","pages":"Article 111103"},"PeriodicalIF":6.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154845","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":"Comparative assessment of classical and fractional Casson models for hemodynamic flow in inclined vessels","authors":"Wan Faezah Wan Azmi,&nbsp;Ahmad Qushairi Mohamad,&nbsp;Lim Yeou Jiann,&nbsp;Sharidan Shafie","doi":"10.1016/j.compbiomed.2025.111101","DOIUrl":"10.1016/j.compbiomed.2025.111101","url":null,"abstract":"<div><div>Mathematical modelling in hemodynamic applications is essential for rapidly developing hypotheses and predicting experimental results within vascular systems. For such models to be reliable, they must closely replicate real-world physiological conditions. This study aims to analytically compare classical and fractional Casson fluid models for blood flow in inclined cylinders, incorporating slip velocity effects, magnetohydrodynamics (MHD), and porous media. Recent studies suggest that fractional fluid models offer advantages by capturing memory effects and non-local behaviour in blood flow. <em>The Caputo-Fabrizio fractional derivative is employed to resolve singularities inherent in classical approaches, facilitating improved modelling of viscoelastic blood behaviour under pulsatile conditions.</em> Analytical solutions for both models are attained using Laplace and finite Hankel transforms. Graphical results illustrate velocity and temperature profiles, highlighting key parameters such as magnetic influence, Casson fluid properties, Darcy's law, fractional derivatives, slip velocity, Grashof number, and inclination angle. Findings show that increased slip velocity augments fluid flow near the cylinder wall, with greater blood flow observed when the artery is oriented vertically upward. Results reveal that the fractional model can mitigate unphysical velocity spikes (common in classical models). The analytical results provide a benchmark for validating numerical models and demonstrate the fractional model's ability to address mathematical limitations of classical approaches. Although the study is theoretical, it provides a foundation for future mapping of physiological parameters and experimental validation.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"197 ","pages":"Article 111101"},"PeriodicalIF":6.3,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118161","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}