Computers in biology and medicine最新文献

{"title":"In silico screening of a designed focused chemical space identifies novel alkyl hydrazides as potent HDAC11 inhibitors","authors":"Fady Baselious ,&nbsp;Sebastian Hilscher ,&nbsp;Lukas Handke ,&nbsp;Cyril Barinka ,&nbsp;Mike Schutkowski ,&nbsp;Wolfgang Sippl","doi":"10.1016/j.compbiomed.2025.110695","DOIUrl":"10.1016/j.compbiomed.2025.110695","url":null,"abstract":"<div><div>The therapeutic potential of HDAC inhibitors containing a hydroxamic acid moiety as a zinc-binding group (ZBG) is limited in clinical use due to their potential mutagenicity. In addition, hydroxamic acids often exhibit off-target effects that can lead to undesirable toxicity. Therefore, the development of HDAC inhibitors with alternative ZBGs has proven to be a promising approach to overcome these drawbacks. HDAC inhibitors carrying alkyl hydrazide as ZBG have recently been published as selective inhibitors for different HDAC subtypes. In the present study, a ligand-based virtual screening workflow, employing a classification categorical model, was developed and applied for a designed targeted chemical space. The two most promising hits from the virtual screening were synthesized and evaluated by in vitro enzyme inhibition assays. Both hits showed strong inhibition of HDAC11 with IC₅₀ values in the nanomolar range. In addition, the compounds showed good selectivity towards HDAC11 at a concentration of 1 μM, only HDAC8 was also significantly inhibited among all tested subtypes. Finally, the binding mode of the selected candidates was investigated by docking against different HDACs, followed by molecular dynamics simulations and metadynamics studies to provide insights for further chemical optimization.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110695"},"PeriodicalIF":7.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557277","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":"Developing a Panel of Shared Susceptibility Genes as Diagnostic Biomarkers for chronic obstructive pulmonary disease and Heart Failure","authors":"Hailong Li ,&nbsp;Cuncun Huang ,&nbsp;Rong Su ,&nbsp;Meng Wang ,&nbsp;Yanping Ma ,&nbsp;Yafeng Wang ,&nbsp;Bingge Xu ,&nbsp;Kai Liu","doi":"10.1016/j.compbiomed.2025.110657","DOIUrl":"10.1016/j.compbiomed.2025.110657","url":null,"abstract":"<div><h3>Aim</h3><div>Chronic obstructive pulmonary disease (COPD) and heart failure (HF) are closely intertwined comorbidities that present significant clinical challenges due to the poorly understood pathophysiological mechanisms driving their coexistence. In this study, we systematically identified molecular signatures associated with COPD-HF comorbidity through an integrative bioinformatics analysis of multi-omics datasets. Our findings yielded novel diagnostic biomarkers and elucidated the underlying pathophysiological mechanisms.</div></div><div><h3>Methods</h3><div>The total genes that intersect with the differentially expressed genes (DEGs) of COPD patients and the weighted gene coexpression network (WGCNA) module genes were identified by analyzing DEGs between COPD patients and healthy individuals, as well as two HF datasets. To assess the diagnostic potential, a nomogram based on receiver operating characteristic (ROC) curve analysis was developed. Significantly differentially expressed genes were selected from both COPD and HF groups using the machine learning method known as Least Absolute Shrinkage and Selection Operator (LASSO). Additionally, single sample gene set enrichment analysis (ssGSEA) was employed to investigate the immune systems of HF and COPD patients.</div></div><div><h3>Results</h3><div>We identified 2002 DEGs between COPD patients and controls, with 36 overlapping WGCNA module genes; furthermore, a total of 201 DEGs were discovered from two HF datasets. Ultimately, the intersection of HF and COPD-related genes revealed four co-susceptibility genes, including SVEP1, MOXD1, SMOC2, and GNB3, were significantly upregulated in both diseases (P &lt; 0.001) and demonstrated high diagnostic accuracy (AUC&gt;0.85). Mechanistically, Machine learning techniques, specifically LASSO analysis, identified five diagnostic genes in COPD and 24 in HF. Patients with chronic COPD and heart failure exhibited significantly elevated expressions of four co-susceptibility genesco-susceptibility genes. Nomograms demonstrated their diagnostic potential in terms of accuracy and performance. Activated CD8 T cells were found to be highly correlated with SVEP1, MOXD1, and SMOC2 in COPD patients, while SVEP1 showed a significant correlation with 26 immune cell types in heart failure patients, as indicated by the ssGSEA analysis. KEGG analysis indicated WNT, VEGF, and SPHINGOLIPID signaling pathways and the co-susceptibility genes were associated in COPD and HF patients.</div></div><div><h3>Conclusion</h3><div>By utilizing publicly available RNA sequencing data, this study identified a panel of genes that are significantly up-regulated in both COPD and heart failure. Four genes demonstrated high diagnostic value through ROC curve analysis, leading to the development of a nomogram designed to assess each gene's diagnostic potential for patients suffering from COPD and HF.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110657"},"PeriodicalIF":7.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549073","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":"Morphological inhibitors of aggregation-prone amyloid-β conformers: A computational exploration","authors":"Stefano Bosio ,&nbsp;Federico Falchi ,&nbsp;Chiara Rauzi ,&nbsp;Luca Bellucci","doi":"10.1016/j.compbiomed.2025.110545","DOIUrl":"10.1016/j.compbiomed.2025.110545","url":null,"abstract":"<div><div>Alzheimer’s disease is a neurodegenerative disorder characterized by progressive cognitive decline and memory loss. It is associated with the self-assembly of the amyloid-<span><math><mi>β</mi></math></span> peptide, a soluble intrinsically disordered protein naturally present in the brain parenchyma in various alloforms. This study presents a computational approach to identify possible modulators of the monomeric aggregation-prone conformations of amyloid-<span><math><mi>β</mi></math></span>, a critical intermediate in the fibrillation process. A structure-based virtual screening campaign was designed using a structural ensemble to identify potential binders. The workflow included binding site identification, small molecule–peptide docking, protein–protein docking, and molecular dynamics simulations to evaluate interaction stability and aggregation propensity. From this pipeline, a set of novel molecules was identified as capable of interacting with aggregation-prone forms of amyloid-<span><math><mi>β</mi></math></span>, potentially reducing their tendency to form toxic aggregates.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110545"},"PeriodicalIF":7.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549067","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":"Deep learning framework for cardiorespiratory disease detection using smartphone IMU sensors","authors":"Lorenzo Simone ,&nbsp;Luca Miglior ,&nbsp;Vincenzo Gervasi ,&nbsp;Luca Moroni ,&nbsp;Emanuele Vignali ,&nbsp;Emanuele Gasparotti ,&nbsp;Simona Celi","doi":"10.1016/j.compbiomed.2025.110595","DOIUrl":"10.1016/j.compbiomed.2025.110595","url":null,"abstract":"<div><div>Respiratory and cardiovascular diseases represent a significant global health burden, underscoring the need for innovative, accessible, and cost-effective screening solutions. This study introduces a clinically grounded framework for the early detection of cardiorespiratory conditions using commodity smartphones equipped with inertial measurement unit sensors. The proposed method leverages accelerometer and gyroscope data collected under a standardized protocol from five distinct thoracoabdominal regions, enabling the acquisition of respiratory kinematics through non-invasive, low-cost technology suitable for remote health monitoring—particularly in resource-limited settings or during pandemic outbreaks. A dedicated preprocessing pipeline segments the time series into individual breathing cycles, which are then analyzed using a bidirectional recurrent neural network to perform binary classification between healthy individuals and patients with cardiovascular disease. The non-healthy cohort comprised preoperative patients diagnosed with conditions including valvular insufficiency, coronary artery disease, and aortic aneurysm. The model was trained and validated using leave-one-out cross-validation with Bayesian hyperparameter optimization. Experimental results demonstrated robust classification performance, with an average sensitivity of <span><math><mrow><mn>0</mn><mo>.</mo><mn>81</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>02</mn></mrow></math></span>, specificity of <span><math><mrow><mn>0</mn><mo>.</mo><mn>82</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>, F1 score of <span><math><mrow><mn>0</mn><mo>.</mo><mn>81</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>02</mn></mrow></math></span>, and accuracy of <span><math><mrow><mn>80</mn><mo>.</mo><mn>2</mn><mtext>%</mtext><mo>±</mo><mn>3</mn><mo>.</mo><mn>9</mn></mrow></math></span>. On an independent set of unseen healthy individuals, the model achieved a true negative rate of <span><math><mrow><mn>74</mn><mo>.</mo><mn>8</mn><mtext>%</mtext><mo>±</mo><mn>4</mn><mo>.</mo><mn>5</mn></mrow></math></span>, confirming its generalization capability. The proposed framework offers a promising avenue for improving public health, enabling remote monitoring, and supporting clinicians in early diagnosis. Future work should focus on expanding the dataset, refining the methodology for long-term monitoring, and assessing its applicability across diverse clinical and at-home settings.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110595"},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549071","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":"DeepPerfusion: A comprehensible two-branched deep learning architecture for high-precision blood volume pulse extraction based on imaging photoplethysmography","authors":"Matthieu Scherpf,&nbsp;Hannes Ernst,&nbsp;Hagen Malberg,&nbsp;Martin Schmidt","doi":"10.1016/j.compbiomed.2025.110571","DOIUrl":"10.1016/j.compbiomed.2025.110571","url":null,"abstract":"<div><div>Imaging photoplethysmography (iPPG) is a contactless approach for the extraction of the blood volume pulsation (BVP). Analyzing the small intensity changes resulting from fluctuations in light absorption in upper skin layers enables BVP extraction. Inhomogeneous illumination or head movements impede iPPG-based BVP extraction. To mitigate these influences, an important step is the accurate skin segmentation and weighting, which has received insufficient attention in state-of-the-art (SOTA) deep learning-based approaches in particular. Therefore, we propose DeepPerfusion, a two-branched deep learning architecture, that combines precise skin segmentation and weighting as well as BVP extraction into one model. Together with our newly developed patch-based temporal normalization mechanism and our innovative training pipeline, DeepPerfusion achieved highly accurate BVP extraction. We performed a thorough performance analysis and evaluated the mean absolute error (MAE) for heart rate extraction and the signal-to-noise ratio (SNR) on 156 subjects from three publicly available datasets and compared it with nine SOTA approaches that underwent the same training and evaluation pipeline. For the median across subjects of each dataset, DeepPerfusion consistently achieved MAE below 1 beat per minute, outperforming the best SOTA approaches by up to 49<!--> <!-->%. Furthermore, DeepPerfusion achieved high SNR with at least 5.81<!--> <!-->dB which was about two to three times higher compared to the best SOTA approaches. In contrast to SOTA approaches, DeepPerfusion’s performance was consistent, robust and highly precise. This demonstrates DeepPerfusion’s ability to perform high-precision BVP extraction. We expect this to open up new diagnostic applications for iPPG in the future.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534710","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 reliable WMH segmentation under domain shift: An application study using maximum entropy regularization to improve uncertainty estimation","authors":"Franco Matzkin ,&nbsp;Agostina Larrazabal ,&nbsp;Diego H. Milone ,&nbsp;Jose Dolz ,&nbsp;Enzo Ferrante","doi":"10.1016/j.compbiomed.2025.110639","DOIUrl":"10.1016/j.compbiomed.2025.110639","url":null,"abstract":"<div><h3>Background</h3><div>Accurate segmentation of white matter hyperintensities (WMH) is crucial for clinical decision-making, particularly in the context of multiple sclerosis. However, domain shifts, such as variations in MRI machine types or acquisition parameters, pose significant challenges to model calibration and uncertainty estimation. This comparative study investigates the impact of domain shift on WMH segmentation, proposing maximum-entropy regularization techniques to enhance model calibration and uncertainty estimation. The purpose is to identify errors appearing after model deployment in clinical scenarios using predictive uncertainty as a proxy measure, since it does not require ground-truth labels to be computed.</div></div><div><h3>Methods</h3><div>We conducted experiments using a classic U-Net architecture and evaluated maximum entropy regularization schemes to improve model calibration under domain shift on two publicly available datasets: the WMH Segmentation Challenge and the 3D-MR-MS dataset. Performance is assessed with Dice coefficient, Hausdorff distance, expected calibration error, and entropy-based uncertainty estimates.</div></div><div><h3>Results</h3><div>Entropy-based uncertainty estimates can anticipate segmentation errors, both in-distribution and out-of-distribution, with maximum-entropy regularization further strengthening the correlation between uncertainty and segmentation performance, while also improving model calibration under domain shift.</div></div><div><h3>Conclusions</h3><div>Maximum-entropy regularization improves uncertainty estimation for WMH segmentation under domain shift. By strengthening the relationship between predictive uncertainty and segmentation errors, these methods allow models to better flag unreliable predictions without requiring ground-truth annotations. Additionally, maximum-entropy regularization contributes to better model calibration, supporting more reliable and safer deployment of deep learning models in multi-center and heterogeneous clinical environments.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110639"},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534707","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":"Transformer attention-based neural network for cognitive score estimation from sMRI data","authors":"Songheng Li ,&nbsp;Yanteng Zhang ,&nbsp;Congyu Zou ,&nbsp;Lipei Zhang ,&nbsp;Fei Li ,&nbsp;Qiang Liu","doi":"10.1016/j.compbiomed.2025.110579","DOIUrl":"10.1016/j.compbiomed.2025.110579","url":null,"abstract":"<div><div>Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia and forecasting Alzheimer’s disease (AD). Some existing deep learning methods often depend on anatomical priors, overlooking individual-specific structural differences during AD progression. To address these limitations, this work proposes a deep neural network that incorporates Transformer attention to jointly predict multiple cognitive scores, including ADAS, CDRSB, and MMSE. The architecture first employs a 3D convolutional neural network backbone to encode sMRI, capturing preliminary local structural information. Then an improved Transformer attention block integrated with 3D positional encoding and 3D convolutional layer to adaptively capture discriminative imaging features across the brain, thereby focusing on key cognitive-related regions effectively. Finally, an attention-aware regression network enables the joint prediction of multiple clinical scores. Experimental results demonstrate that our method outperforms some existing traditional and deep learning methods based on the ADNI dataset. Further qualitative analysis reveals that the dementia-related brain regions identified by the model hold important biological significance, effectively enhancing the performance of cognitive score prediction. Our code is publicly available at: <span><span>https://github.com/lshsx/CTA_MRI</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110579"},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534711","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":"Advancing breast cancer prediction using blockchain-secured hybrid genetic algorithm","authors":"Monu Bhagat ,&nbsp;Ujjwal Maulik","doi":"10.1016/j.compbiomed.2025.110622","DOIUrl":"10.1016/j.compbiomed.2025.110622","url":null,"abstract":"<div><div>Feature selection using evolutionary algorithms-a well-liked technique for choosing pertinent characteristics in huge datasets is explored. In machine learning, feature selection (FS) is a key phase that helps to boost model efficiency, decrease overfitting, and improve model accuracy. Breast cancer (BC) prediction algorithms can be improved to attain more accuracy while protecting patient privacy and maintaining data integrity by utilizing blockchain. The suggested model incorporates the system with the blockchain method for smart contracts, and it evaluates and compares various learning algorithms and classifiers for the identification of breast cancer. Many different machine learning algorithm techniques were tested on the Wisconsin Diagnosis Breast Cancer data set. The accuracy obtained in different classifier such as XGBoost, AdaBoost, Logistic Regression, Linear SVM, Random Forest, KNN, Gradient Boosting, Radial SVM and Decision tree are 99%, 99.06%, 99.35%, 99.47%, 98.88%, 96.84%, 98.71%, 96.31% and 96.43%. The accuracy of each algorithm in determining whether a tumor was benign or malignant was demonstrated to be higher than 96.31%. The combination of GA and Linear SVM achieved the highest accuracy of 99.47%. In general, though, XGBoost outperforms the competition whether you use GA or not. Because of this, supervised machine learning techniques will be extremely useful in cancer research, especially for purposes of early diagnosis and prognosis. To solve the hazards of data breaches, unauthorized access, and tampering are present with traditional centralized systems, we have developed a safe and impenetrable system that protects patient data throughout the prediction process by integrating blockchain with machine learning.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110622"},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534706","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":"EnsemPred-ACP: Combining machine and deep learning to improve anticancer peptide prediction","authors":"Minjun Kwon ,&nbsp;Yong Eun Jang ,&nbsp;Ji Su Hwang ,&nbsp;Seok Gi Kim ,&nbsp;Nimisha Pradeep George ,&nbsp;Shaherin Basith ,&nbsp;Gwang Lee","doi":"10.1016/j.compbiomed.2025.110668","DOIUrl":"10.1016/j.compbiomed.2025.110668","url":null,"abstract":"<div><div>Anticancer peptide (ACP) has emerged as potent therapeutic agents owing to its ability to selectively target cancer cells while minimising toxicity to healthy cells. However, the accurate computational prediction of ACP remains challenging because of the complex molecular mechanisms underlying cancer. In this study, we introduce EnsemPred-ACP, an innovative ensemble framework that combines machine learning (ML) and deep learning (DL) approaches to enhance ACP prediction. Our primary innovation is the introduction of binary profile features (BPF) to augment pre-trained protein embeddings, thereby capturing position-specific patterns crucial for ACP identification. The framework used a dual-pipeline architecture; ML models processed handcrafted sequence features and embeddings, whereas DL models handled BPF-enhanced embeddings. Upon evaluation with independent datasets, EnsemPred-ACP achieved an accuracy of 0.863, sensitivity of 0.897, and specificity of 0.830, notably outperforming existing methods. The model demonstrated a strong generalisation performance, achieving an area under the receiver operating characteristic curve of 0.93. Ablation studies on independent datasets further highlighted the substantial impact of BPF, enhancing the prediction accuracy by 2.5 % and 11.1 % when integrated with ESM2 and ProtT5 embeddings, respectively. These results demonstrate the effectiveness of our integrated approach in accurately identifying potential therapeutic peptides, thereby contributing to the advancement of peptide-based cancer therapeutics.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110668"},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549072","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":"Influence of pre-bending on primary fixation stability in one-segmental mandibular reconstruction","authors":"Philipp Ruf ,&nbsp;Özgür Cebeci ,&nbsp;Vincenzo Orassi ,&nbsp;Claudius Steffen ,&nbsp;Georg N. Duda ,&nbsp;Max Heiland ,&nbsp;Sara Checa ,&nbsp;Carsten Rendenbach","doi":"10.1016/j.compbiomed.2025.110686","DOIUrl":"10.1016/j.compbiomed.2025.110686","url":null,"abstract":"<div><h3>Background</h3><div>The fixation of osseous free flaps for segmental mandible reconstruction after resection is most commonly performed with patient-specific 3D printed or conventional load-bearing reconstruction plates.</div><div>The main challenge with conventional plates is the step of manual bending to adjust the plate to the specific mandible of the patient. To date, the influence of this permanent plate deformation on the biomechanical conditions within the healing regions remains unknown.</div><div>The present study aimed to investigate the effect of plate pre-bending on intersegmental strains, known to influence the healing outcome.</div></div><div><h3>Methods</h3><div>To achieve this, biomechanical finite element models were developed to simulate plate pre-bending and biting in a one-segmental mandibular reconstruction. The biomechanics induced within the healing region were compared between a pre-stressed conventional reconstruction plate and a customized conventional reconstruction plate.</div></div><div><h3>Results</h3><div>Higher stresses were predicted in the pre-stressed plate. However, the mechanical strains within the healing regions were not influenced by plate pre-bending.</div></div><div><h3>Conclusions</h3><div>The increased levels of mechanical strains under both pre-stressed and customized conventional plates in comparison to common patient-specific plates could be a reason for the higher rates of osseous union under conventional fixation. Since customized conventional reconstruction plates additionally presented elastic stresses and include the advantages of patient-specific plates, those plates are biomechanically and clinically promising.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110686"},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549069","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}