Computer methods and programs in biomedicine最新文献

{"title":"CellViT++: Energy-efficient and adaptive cell segmentation and classification using foundation models","authors":"Fabian Hörst , Moritz Rempe , Helmut Becker , Lukas Heine , Julius Keyl , Jens Kleesiek","doi":"10.1016/j.cmpb.2025.109206","DOIUrl":"10.1016/j.cmpb.2025.109206","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Deep learning-based cell segmentation and classification methods in digital pathology are critical for diagnostics but are hampered by models that require extensive annotated datasets, are computationally expensive, and lack adaptability to new cell types. This creates a significant bottleneck in research and clinical workflows. This study introduces <span><math><msup><mrow><mtext>CellViT</mtext></mrow><mrow><mo>+</mo><mo>+</mo></mrow></msup></math></span>, a data-efficient and lightweight framework for generalized cell segmentation that allows for rapid adaptation to novel cell taxonomies with minimal data.</div></div><div><h3>Methods:</h3><div><span><math><msup><mrow><mtext>CellViT</mtext></mrow><mrow><mo>+</mo><mo>+</mo></mrow></msup></math></span> leverages a Vision Transformer with a frozen pretrained foundation model for segmentation. It simultaneously extracts deep cell embeddings from the transformer tokens during the forward pass at no extra computational cost. To adapt to new cell types, only a lightweight classifier is trained on these embeddings, bypassing the need to retrain the segmentation model. We also demonstrate an automated workflow to generate training data from registered H&E and immunofluorescence (IF) slides. The framework was validated on seven public datasets.</div></div><div><h3>Results:</h3><div>The framework achieves remarkable zero-shot segmentation results and data efficiency. On the CoNSeP dataset for colon cancer, we achieved superior results with only 10% of the training data. On all other datasets, we outperformed competing methods or at least approached their performance, all in one model. The classifier approach, based on zero-shot segmentation models, drastically reduces computational costs, with training times of minutes versus hours for baseline models, decreasing CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emission by 96.93%. Models trained on automatically generated labels from IF-staining achieved performance comparable to (lymphocytes, <span><math><mrow><mi>Δ</mi><msub><mrow><mtext>F</mtext></mrow><mrow><mn>1</mn></mrow></msub><mo>:</mo><mspace></mspace><mo>−</mo><mn>0</mn><mo>.</mo><mn>042</mn></mrow></math></span>) or even exceeding (plasma cells, <span><math><mrow><mi>Δ</mi><msub><mrow><mtext>F</mtext></mrow><mrow><mn>1</mn></mrow></msub><mo>:</mo><mspace></mspace><mo>+</mo><mn>0</mn><mo>.</mo><mn>108</mn></mrow></math></span>) those trained on expert-annotated datasets.</div></div><div><h3>Conclusions:</h3><div><span><math><msup><mrow><mtext>CellViT</mtext></mrow><mrow><mo>+</mo><mo>+</mo></mrow></msup></math></span> provides a robust and efficient open-source framework that addresses key limitations in computational pathology by decoupling segmentation from classification. Its ability to adapt to new cell types with minimal data and its support for automated dataset generation from IF slides significantly reduce the r","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109206"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024462","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":"In silico modelling of aortic annuloplasty: hemodynamic assessment through in vitro experiments and in vivo MRI","authors":"Marta Zattoni ,&nbsp;Luca Bontempi ,&nbsp;Steffen Ringgaard ,&nbsp;Giulia Luraghi ,&nbsp;Leila Louise Benhassen ,&nbsp;Peter Johansen ,&nbsp;Monika Colombo","doi":"10.1016/j.cmpb.2026.109246","DOIUrl":"10.1016/j.cmpb.2026.109246","url":null,"abstract":"<div><div>Aortic annuloplasty (AA) is an innovative surgical technique for aortic root (AR) enlargement. It is performed by implanting sutures, bands, or rings, either externally or internally the AR, hereby reducing its diameter. This study evaluates the impact of AA approaches on AR hemodynamic by employing a porcine-specific workflow combining <em>in vivo</em> magnetic resonance imaging (MRI), <em>in vitro</em> experiments and <em>in silico</em> fluid-structure interaction (FSI) simulations investigating external single ring AA. CAD models of native and post-annuloplasty ARs were segmented from <em>in vivo</em> porcine MRI data and served as the basis for fabricating 3D-printed resin phantoms and implementing computational digital twins. The former were tested on a pulsatile flow-loop, whereas the latter were integrated in FSI simulations, with time-dependent boundary conditions based on the resultant experimental pressure waveforms. Additionally, a proof-of-concept validation of the <em>in silico</em> model against <em>in vivo</em> data is proposed. Computational results of the two cases were compared in terms of fluid velocity, vorticity, helicity, and wall shear stresses, providing a step towards understanding the complex interactions between the AR and blood flow dynamics. Results suggested that the presence of the ring increased the systolic jet flow and post-valve velocities (three-fold increase), reduced the backward, vortical flow during diastole (∼ 9% decrease), and induced modifications in bulk flow and wall shear stresses distribution. Furthermore, the development of an animal-specific digital twin of a post-AA AR represents a significant advancement in the field, providing a valuable tool for future research and for clinical applications to aid AA decision-making process.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109246"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024463","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":"A novel multi-field interaction cutting model for ultrasonically activated surgical devices","authors":"Shilun Du,&nbsp;Yingda Hu,&nbsp;Fan Wei,&nbsp;Yong Lei","doi":"10.1016/j.cmpb.2026.109241","DOIUrl":"10.1016/j.cmpb.2026.109241","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>Ultrasonically activated surgical devices (UASDs) are widely used in surgery due to their cutting, hemostatic, and thermal control capabilities. Modeling the UASD cutting process enhances understanding of these surgical procedures, aiding in surgery planning and design optimization. However, existing models lack consideration for the high-frequency cutting interactions, limiting their predictive accuracy. This study aims to develop a UASD-tissue interaction cutting model that considers high-frequency interactions and enhances prediction accuracy for multi-physical fields during cutting.</div></div><div><h3>Methods:</h3><div>This paper models the multi-field interaction process during the soft tissue cutting in UASD. First, a novel multi-field interaction cutting model is proposed, designed to predict cutting force, deformation, temperature, and tissue damage. Second, a LuGre-based interactive force module considering cellular rupture lubrication effects is developed for characterizing high-frequency UASD-tissue interactions. Third, a localized contact algorithm utilizing position-based dynamics and an adaptive time solver are proposed to achieve stable contact and solve the multi-time scale mechanism equations. Numerical experiments and physical experiments on phantoms and porcine livers are conducted.</div></div><div><h3>Results:</h3><div>The simulated force, temperature, damage, and deformation are consistent with the physical experimental results. The model captures the negative correlation between cutting speed and lubrication with temperature and friction, and shows increased vibration amplitude can lead to higher friction and heat generation, while maintaining stability across different cutting scenarios.</div></div><div><h3>Conclusions:</h3><div>The proposed model can robustly and accurately predict the multi-physical interactions during cutting, providing insights into the UASDs cutting process, thereby facilitating surgical planning and instrument design.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109241"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975306","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":"Multi-scale heart simulation augments the explainability of artificial intelligence-enabled electrocardiogram through provision of an electrocardiogram database labelled with cellular pathologies","authors":"Jun-ichi Okada ,&nbsp;Katsuhito Fujiu ,&nbsp;Eriko Hasumi ,&nbsp;Ying Chen ,&nbsp;Takumi Washio ,&nbsp;Toshiaki Hisada ,&nbsp;Seiryo Sugiura","doi":"10.1016/j.cmpb.2026.109247","DOIUrl":"10.1016/j.cmpb.2026.109247","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Although artificial-intelligence-enhanced electrocardiograms (AI-ECGs) offer prediction and diagnosis capabilities superior to those of humans, they exhibit poor explainability and interpretability because of their complex-neural-network-derived black-box characteristics. To augment the explainability of artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis, we proposed a method combining AI-ECG and synthetic ECG database created by a multi-scale heart simulator.</div></div><div><h3>Methods</h3><div>Using the “UT-Heart” multi-scale heart simulator running on the supercomputer Fugaku, we simulated 30,720 12-lead ECG recordings. This dataset comprises an exhaustive combination of 12 cellular and subcellular pathologies reportedly associated with heart failure and was analysed using a previously developed AI-ECG system that accurately classifies ECGs into New York Heart Association (NYHA) functional classes. By analysing the relationship between HF severity and labelled pathology in each simulated ECG recording, we elucidated the origin of abnormalities detected using AI-ECG.</div></div><div><h3>Results</h3><div>AI-ECG classified 30,618 ECGs (excluding 102 arrhythmia cases) into 2234 control and 28,384 HF cases. A separate three-group classification identified 2234 control, 18,444 NYHA I/II, and 9940 NYHA III/IV cases. In the two-group classification, significant differences (<em>p</em> &lt; 0.01) were observed in sodium (Na) and Na–calcium exchanger currents and the transmural distribution of distinct cell types. Although the three-group classification revealed a severity-dependent progression of the Na current abnormality, the cell distribution in NYHA III/IV was closer to that of normal cases than to that of NYHA I/II. These findings did not explain the changes in the ECG waveform that the AI-ECG identified as notable features of heart failure in the heatmap analysis.</div></div><div><h3>Conclusions</h3><div>The ECG dataset generated using the multi-scale heart simulator can enhance the explainability of AI-ECGs by elucidating the mechanisms underlying HF-severity-specific changes in ECGs of heart failure.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109247"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975339","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":"HeartUnloadNet: A cycle-consistent graph network with reduced supervision for predicting unloaded cardiac geometry from diastolic states","authors":"Siyu Mu,&nbsp;Wei Xuan Chan,&nbsp;Choon Hwai Yap","doi":"10.1016/j.cmpb.2026.109243","DOIUrl":"10.1016/j.cmpb.2026.109243","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>The unloaded cardiac geometry, representing the zero-stress and zero-strain reference state of the heart, is fundamental for personalized biomechanical modeling of cardiac function. However, this state cannot be directly observed in vivo, as clinical imaging only captures pressure-loaded geometries such as those at end-diastole. Traditional inverse finite element solvers are commonly used to reconstruct the unloaded geometry, but they require iterative optimization, are computationally expensive, and may suffer from convergence issues. The objective of this study was to develop an efficient and accurate deep learning framework to predict the unloaded left ventricular geometry directly from clinical end-diastolic states.</div></div><div><h3>Methods:</h3><div>We propose HeartUnloadNet, a graph attention-based neural network that incorporates both mesh topology and physiological parameters, including pressure, myocardial stiffness, and fiber orientation. The framework employs a cycle-consistent bidirectional training strategy, allowing reduced supervision by enforcing that the predicted unloaded state can reconstruct the original end-diastolic geometry. The model was trained and validated on 10,350 finite element simulations generated across diverse anatomical shapes and physiological conditions. Performance was evaluated using geometric metrics such as Dice similarity coefficient, Hausdorff distance, mean distance, and standard deviation of nodal errors.</div></div><div><h3>Results:</h3><div>HeartUnloadNet achieved sub-millimeter accuracy, with a Dice similarity coefficient of 0.986 <span><math><mo>±</mo></math></span> 0.023 and a Hausdorff distance of 0.083 <span><math><mo>±</mo></math></span> 0.028 cm. Compared to conventional inverse finite element solvers, the framework was over 100,000 times faster, with an average inference time of 0.02 seconds per case. Ablation studies demonstrated that cycle consistency enabled the model to maintain high accuracy even when only 3% of the training data were labeled. The method consistently outperformed baseline architectures across all evaluation metrics.</div></div><div><h3>Conclusions:</h3><div>HeartUnloadNet provides a scalable and accurate alternative to traditional inverse finite element approaches for estimating the unloaded cardiac geometry. By combining mesh-aware learning with physiological conditioning and reduced supervision, the framework achieves real-time performance while maintaining biomechanical fidelity. This work establishes a foundation for future integration of learning-based surrogates into clinical workflows, supporting patient-specific cardiac modeling and real-time functional assessment.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109243"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145965515","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":"Beyond a single mode: GAN ensembles for diverse medical data generation","authors":"Lorenzo Tronchin ,&nbsp;Tommy Löfstedt ,&nbsp;Paolo Soda ,&nbsp;Valerio Guarrasi","doi":"10.1016/j.cmpb.2026.109234","DOIUrl":"10.1016/j.cmpb.2026.109234","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>The advancement of generative AI in medical imaging faces the trilemma of simultaneously achieving high fidelity and diversity in synthetic data generation. Although Generative Adversarial Networks (GANs) have demonstrated significant potential, they are often hindered by limitations such as mode collapse and poor coverage of real data distributions. This study investigates the use of GAN ensembles as a solution to these challenges, with the goal of enhancing the quality and utility of synthetic medical images.</div></div><div><h3>Methods:</h3><div>We formulate a multi-objective optimisation problem to select an optimal ensemble of GANs that balances fidelity and diversity. The ensemble comprises models that contribute uniquely to the synthetic data space, ensuring minimal redundancy. A comprehensive evaluation was conducted using three distinct medical imaging datasets. We tested 22 GAN architectures, incorporating various loss functions and regularisation techniques. By sampling models at different training epochs, we crafted 110 unique configurations for ensemble selection.</div></div><div><h3>Results:</h3><div>The selected GAN ensembles demonstrated improved performance in generating synthetic medical images that closely resemble real data distributions. These ensembles preserved image fidelity while increasing diversity. In some settings, downstream models trained on synthetic data achieved slightly higher accuracy than those trained on real data alone. This effect arises because the synthetic images act as a targeted data augmentation mechanism that enhances class balance and diversity rather than replacing real data.</div></div><div><h3>Conclusions:</h3><div>GAN ensembles offer a robust solution to the fidelity–diversity–efficiency trade-off in medical image synthesis. By integrating multiple complementary models, the proposed approach improves the representativeness and utility of synthetic medical data, potentially advancing a wide range of clinical and research applications in diagnostic AI.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109234"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924499","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":"Towards open-set myoelectric gesture recognition via dual-perspective inconsistency learning","authors":"Chen Liu ,&nbsp;Can Han ,&nbsp;Chengfeng Zhou ,&nbsp;Yaqi Wang ,&nbsp;Crystal Cai ,&nbsp;Dahong Qian","doi":"10.1016/j.cmpb.2026.109258","DOIUrl":"10.1016/j.cmpb.2026.109258","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Gesture recognition based on surface electromyography (sEMG) has achieved significant progress in human-machine interaction (HMI), especially in prosthetic control and movement rehabilitation. However, accurately recognizing predefined gestures within a closed set is still inadequate in practice; a robust open-set system needs to effectively reject unknown gestures while correctly classifying known ones, which is rarely explored in the field of myoelectric gesture recognition.</div></div><div><h3>Methods:</h3><div>To handle this challenge, we first report a significant distinction in prediction inconsistency discovered for unknown classes, which arises from perspective differences and can substantially enhance open-set recognition performance. Based on this insight, we propose a novel dual-perspective inconsistency learning approach, PredIN, to explicitly magnify the prediction inconsistency by enhancing the inconsistency of class feature distribution within different perspectives. Specifically, PredIN maximizes the class feature distribution inconsistency among the dual perspectives to enhance their differences. Meanwhile, it optimizes inter-class separability within an individual perspective to maintain individual performance.</div></div><div><h3>Results:</h3><div>We evaluate our method on four public benchmark sEMG datasets. Comprehensive experiments demonstrate that the PredIN outperforms state-of-the-art methods by a clear margin.</div></div><div><h3>Conclusion:</h3><div>Our proposed method simultaneously achieves accurate closed-set classification for predefined gestures and effective rejection for unknown gestures, exhibiting its efficacy and superiority in open-set gesture recognition based on sEMG.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109258"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024466","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":"Prey capture enhanced Harris hawks optimizer for wrapper-based feature selection in high-dimensional medical data","authors":"Mohammed Batis ,&nbsp;Yi Chen ,&nbsp;Lei Liu ,&nbsp;Ali Asghar Heidari ,&nbsp;Huiling Chen","doi":"10.1016/j.cmpb.2026.109237","DOIUrl":"10.1016/j.cmpb.2026.109237","url":null,"abstract":"<div><h3>Background and Objective</h3><div>While the Harris Hawks Optimizer (HHO) is widely utilized for wrapper-based Feature Selection (FS) due to its efficiency and ease of implementation, existing HHO-based FS approaches encounter challenges when handling high-dimensional datasets, such as falling into local optima and high computational costs. In the HHO algorithm, the Harris hawks engage in surprise attacks on the identified prey according to the prey's escape energy. However, there may be scenarios where the prey could escape due to the algorithm's limitations. To enhance the algorithm's prey-capture ability, this article introduces an enhanced HHO algorithm termed Prey Capture Harris Hawks Optimizer (PCHHO).</div></div><div><h3>Methods</h3><div>The prey capture strategy incorporates crossover and mutation operators to enhance the algorithm's exploratory-exploitative capabilities. The performance of PCHHO is evaluated on the CEC2017 benchmark suite, where it is compared to HHO, with three enhanced HHO algorithms, nine classical metaheuristic algorithms, and nine improved metaheuristic algorithms. The experimental comparison results are synthesized using the Wilcoxon signed-rank and Friedman tests. Ultimately, a binary form of PCHHO (bPCHHO) is designed for wrapper-based FS and compared with six excellent binary metaheuristics using 15 high-dimensional medical datasets.</div></div><div><h3>Results</h3><div>The results demonstrate the excellent performance of the proposed algorithm on the CEC2017 benchmark suite compared to other algorithms, as well as the effectiveness of bPCHHO in evolving a subset of features with 77% reduction in classification error, 8% reduction in computational time, and 73% fewer features selected compared to bHHO.</div></div><div><h3>Conclusions</h3><div>The proposed PCHHO and its binary variant bPCHHO exhibit superior performance in both benchmark optimization and wrapper-based FS for high-dimensional medical data, highlighting their potential for practical applications.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109237"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975338","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":"Decoding metabolic reprogramming heterogeneity across bladder cancer stages using single-cell and spatial multi-omics approaches","authors":"Jiang Zhao ,&nbsp;Jingwei Zhao ,&nbsp;Wei Huang ,&nbsp;Weijie Lin ,&nbsp;Kuangzheng Jie ,&nbsp;Zihang Wu ,&nbsp;Benyi Li ,&nbsp;Lixin Fan ,&nbsp;Xiangwei Wang","doi":"10.1016/j.cmpb.2026.109261","DOIUrl":"10.1016/j.cmpb.2026.109261","url":null,"abstract":"<div><h3>Background</h3><div>Reprogramming of metabolic pathways represents a central indicator in cancer pathogenesis, but the metabolic heterogeneity of bladder cancer (BLCA) at different stages is not well understood. This study aims to analyze metabolic reprogramming in BLCA across stages and its impact on patient survival.</div></div><div><h3>Methods</h3><div>Single-cell sequencing data were used to examine metabolic heterogeneity of epithelial cells and cell subpopulation differentiation in BLCA at various clinical stages. Spatial transcriptome data were analyzed for copy number variability and riboflavin metabolism in BLCA epithelial cells. Bulk RNA sequencing data from BLCA patients were used for riboflavin pathway expression analysis and prognostic biomarker identification. The effects of three biomarkers (ENPP1, ACP1, and RFK) on BLCA risk were validated using RT-qPCR, Mendelian randomization and co-localization analysis.</div></div><div><h3>Results</h3><div>Epithelial cells exhibited significant metabolic heterogeneity during bladder cancer progression. Compared to normal control stage, riboflavin metabolic activity progressively increased with disease stage, as validated by spatial transcriptomics and bulk RNA-seq. High expression of ENPP1, ACP1, and RFK (riboflavin pathway) strongly correlated with poor overall survival. RT-qPCR confirmed their high expression in tumours, increasing with stage. Mendelian randomisation/co-localisation indicated these genes localise to bladder epithelium, and their genetic variation associates negatively with BLCA risk.</div></div><div><h3>Conclusion</h3><div>Increased riboflavin metabolism is likely to be an important marker of malignant progression in BLCA. The ENPP1, ACP1 and RFK genes in this pathway may serve as valuable prognostic biomarkers for BLCA, with potential implications for early diagnosis, monitoring disease progression, and guiding personalized treatment strategies.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109261"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074614","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":"Effect of Doppler ultrasound and high-frame-rate ultrasound particle image velocimetry derived inlet boundary conditions on wall shear stress parameters in the stented superficial femoral artery","authors":"Lisa Rutten ,&nbsp;Lennart van de Velde ,&nbsp;Lente Pol ,&nbsp;Kartik Jain ,&nbsp;Michel M.P.J. Reijnen ,&nbsp;Michel Versluis","doi":"10.1016/j.cmpb.2026.109259","DOIUrl":"10.1016/j.cmpb.2026.109259","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Hemodynamic predictions by computational fluid dynamics (CFD) strongly depend on inlet boundary conditions (IBC). One-dimensional Doppler ultrasound (DUS) is typically used for estimating flow IBCs, despite its sensitivity to the operator, ultrasound hardware and assumptions in flow rate computation. An alternative is two-dimensional high-frame-rate ultrasound particle image velocimetry (echoPIV). This study investigated the differences between DUS and echoPIV-derived IBCs and their effect on wall shear stress parameters in the stented superficial femoral artery.</div></div><div><h3>Methods</h3><div>CFD simulations using DUS and echoPIV-derived IBCs were performed for three patients with a superficial femoral artery stenosis that were treated with a stent. Spatiotemporal velocity profiles were compared at 0 – 50 mm from the inlet. Differences were quantified with the root-mean-square error (RMSE). Regions of low time-averaged wall shear stress (TAWSS) and high oscillatory shear index (OSI) using a literature-based threshold of 0.4 Pa and 0.2, respectively, and an IBC-specific threshold (lower third and upper third, respectively) were determined. Co-localization was quantified using the Jaccard similarity index.</div></div><div><h3>Results</h3><div>The DUS and echoPIV-derived IBCs differed in flow rate and velocity profile, with the largest difference found at peak systole (RMSE: &gt; 50 cm/s). Using the literature-based threshold, similarity in low TAWSS was high for two patients (0.85 – 0.88) and low for one (0.57). Agreement in high OSI was low in two patients (0.45 – 0.48) and high in one patient (0.83). The IBC-specific threshold increased the agreement for both low TAWSS and high OSI (≥0.75).</div></div><div><h3>Conclusions</h3><div>Differences in DUS and echoPIV-derived IBCs affected the TAWSS and OSI magnitudes. Regions of low TAWSS and high OSI corresponded well using an IBC-specific threshold. The literature-based threshold resulted in lower similarity values and different interpretations of restenosis risk that may cause differences in follow-up intensity or medical management.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109259"},"PeriodicalIF":4.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024464","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}