Pub Date : 2026-02-01Epub Date: 2026-01-05DOI: 10.1016/j.compbiomed.2025.111402
Sevak Ram Sahu , Parimita Roy , Ranjit Kumar Upadhyay
The onset and progression of Alzheimer’s disease (AD) have long been strongly associated with obesity and diabetes caused by hyperglycemia, which leads to beta-cell dysfunction and insulin imbalance. This imbalance promotes the release of cytokines and activation of microglia, which play a crucial role in the production of amyloid-beta and neurofibrillary tangles. In this context, we formulate a delayed reaction-diffusion model of obesity induced AD to examine the dynamical behavior of the above biological hypothesis. We investigate the existence and uniqueness of solutions, stability of equilibria (local and global), sensitivity analysis as well as the occurrence of Hopf bifurcation and Turing instability. The findings highlight the importance of insulin diffusion rate, insulin secretion delay, glucose, and beta cell in developing AD and its effective control strategies. Spatiotemporal dynamics such as patchy patterns exhibit how accumulates and spreads in the brain. A higher growth rate of beta cell supports sufficient insulin secretion, which can delay the progression of AD. In contrast, when beta cell growth is impaired, even a slight delay in secretion can accelerate disease progression. This study reveals that maintaining high-calorie food to support sufficient growth of beta cell and insulin for a long-term healthy lifestyle along with targeted anti-amyloid approaches can remarkably delay Alzheimer’s progression.
{"title":"Unraveling the link between beta cell dysfunction, insulin imbalance, and neurodegeneration in Alzheimer’s disease","authors":"Sevak Ram Sahu , Parimita Roy , Ranjit Kumar Upadhyay","doi":"10.1016/j.compbiomed.2025.111402","DOIUrl":"10.1016/j.compbiomed.2025.111402","url":null,"abstract":"<div><div>The onset and progression of Alzheimer’s disease (AD) have long been strongly associated with obesity and diabetes caused by hyperglycemia, which leads to beta-cell dysfunction and insulin imbalance. This imbalance promotes the release of cytokines and activation of microglia, which play a crucial role in the production of amyloid-beta and neurofibrillary tangles. In this context, we formulate a delayed reaction-diffusion model of obesity induced AD to examine the dynamical behavior of the above biological hypothesis. We investigate the existence and uniqueness of solutions, stability of equilibria (local and global), sensitivity analysis as well as the occurrence of Hopf bifurcation and Turing instability. The findings highlight the importance of insulin diffusion rate, insulin secretion delay, glucose, and beta cell in developing AD and its effective control strategies. Spatiotemporal dynamics such as patchy patterns exhibit how <span><math><msub><mi>A</mi><mrow><mi>β</mi></mrow></msub></math></span> accumulates and spreads in the brain. A higher growth rate of beta cell supports sufficient insulin secretion, which can delay the progression of AD. In contrast, when beta cell growth is impaired, even a slight delay in secretion can accelerate disease progression. This study reveals that maintaining high-calorie food to support sufficient growth of beta cell and insulin for a long-term healthy lifestyle along with targeted anti-amyloid approaches can remarkably delay Alzheimer’s progression.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111402"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145910863","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}
Pub Date : 2026-02-01Epub Date: 2026-01-06DOI: 10.1016/j.compbiomed.2025.111436
Navneet Roshan, Rahul Pandit
Delayed afterdepolarizations (DADs), which occur during the diastolic phase of a cardiomyocyte action potential (AP), are frequently observed under specific pathophysiological conditions. The synchronization of DAD-capable myocytes can effectively overcome the inherent source-sink mismatch with adjacent normal myocytes, so it is an important mechanism for the genesis of premature ventricular contractions (PVCs). Our study elucidates the role of mechano-electrical feedback in modulating this critical source-sink requirement and its interplay with diffusional anisotropy in cardiac tissue. We combine the ten Tusscher-Panfilov 06 (TP06) electrophysiological model for human ventricular myocytes with cardiac-tissue-mechanics models and account for spontaneous calcium releases (SCRs) and the randomness associated with these and with the disordered arrangement of DAD myocytes in a clump. Our work leads to a quantification of the time dependence of the intracellular cytosolic calcium transient and the active-contraction ratio , without and with SCRs and shows explicitly how the parameter , which controls the strength of the SCRs, affects DADs and . We then illustrate the spatiotemporal evolution of the PVCs in the presence of domain deformation and a clump of DAD-capable myocytes. In a cable-type domain we demonstrate how contraction and elongation of the domain, the coupling interval of the DADs with the previous AP, and the width of the probability distribution function (PDF) of the coupling interval influence the source-size requirement for the formation of PVCs. We then extend our cable results to two dimensions (2D) by calculating the effect of the mechano-electric feedback on the origin and evolution of DAD-induced PVCs in 2D tissue. Our work leads to the following insights on the interplay of mechanical deformation and electrophysiology in DAD-induced PVCs: (1): mechano-electrical feedback markedly reduces the source requirement, which we quantify by the size of a clump of DAD-myocytes; (2): an extended coupling interval of DADs, relative to the preceding AP, reduces the source requirement; (3): sparse distribution of DAD-myocytes in a clump and the distribution in their coupling intervals, along with mechano-electric feedback, reduces the chances of the DAD-induced PVCs; (4): this suppression of PVCs is more pronounced in 2D than in 1D domains.
延迟去极化（DADs）发生在心肌细胞动作电位（AP）舒张期，在特定的病理生理条件下经常观察到。具有dad能力的肌细胞的同步化可以有效地克服与相邻正常肌细胞固有的源池失配，是室性早搏发生的重要机制。我们的研究阐明了机电反馈在调节这一关键源库需求中的作用及其与心脏组织扩散各向异性的相互作用。我们将人类心室肌细胞的十个Tusscher-Panfilov 06 （TP06）电生理模型与心脏组织力学模型相结合，并解释了自发钙释放（SCRs）以及与这些和DAD肌细胞在团块中无序排列相关的随机性。我们的工作导致了细胞内胞质钙瞬态Cai和主动收缩比λo的时间依赖性的量化，没有和有SCRs，并明确显示了参数gspon，控制SCRs的强度，如何影响DADs和λo。然后，我们说明了在结构域变形和一团具有dad能力的肌细胞的存在下，室性早搏的时空演变。在电缆型结构域中，我们展示了结构域的收缩和伸长、dad与前一个AP的耦合间隔以及耦合间隔的概率分布函数（PDF）的宽度如何影响pvc形成的源尺寸要求。然后，我们通过计算机电反馈对二维组织中dad诱导的室性早搏的起源和演变的影响，将我们的电缆结果扩展到二维（2D）。我们的工作导致了以下关于机械变形和电生理在dad诱导的室性早搏中的相互作用的见解：(1)：机电反馈显着降低了源需求，我们通过dad肌细胞团的大小来量化；(2)：相对于之前的AP，延长了dad的耦合间隔，减少了对源的需求；(3): dad -肌细胞的稀疏分布及其耦合间隔的分布，以及机电反馈，减少了dad -肌细胞诱发早搏的机会；(4)：这种对室性早搏的抑制在二维结构域中比在一维结构域中更为明显。
{"title":"Mechano-electric feedback reduces the occurrence of delayed-afterdepolarization-driven focal activity","authors":"Navneet Roshan, Rahul Pandit","doi":"10.1016/j.compbiomed.2025.111436","DOIUrl":"10.1016/j.compbiomed.2025.111436","url":null,"abstract":"<div><div>Delayed afterdepolarizations (DADs), which occur during the diastolic phase of a cardiomyocyte action potential (AP), are frequently observed under specific pathophysiological conditions. The synchronization of DAD-capable myocytes can effectively overcome the inherent source-sink mismatch with adjacent normal myocytes, so it is an important mechanism for the genesis of premature ventricular contractions (PVCs). Our study elucidates the role of <em>mechano-electrical feedback</em> in modulating this critical source-sink requirement and its interplay with diffusional anisotropy in cardiac tissue. We combine the ten Tusscher-Panfilov 06 (TP06) electrophysiological model for human ventricular myocytes with cardiac-tissue-mechanics models and account for spontaneous calcium releases (SCRs) and the randomness associated with these and with the disordered arrangement of DAD myocytes in a clump. Our work leads to a quantification of the time dependence of the intracellular cytosolic calcium transient <span><math><mi>C</mi><msub><mi>a</mi><mtext>i</mtext></msub></math></span> and the active-contraction ratio <span><math><msub><mi>λ</mi><mtext>o</mtext></msub></math></span>, without and with SCRs and shows explicitly how the parameter <span><math><msub><mi>g</mi><mrow><mtext>spon</mtext></mrow></msub></math></span>, which controls the strength of the SCRs, affects DADs and <span><math><msub><mi>λ</mi><mtext>o</mtext></msub></math></span>. We then illustrate the spatiotemporal evolution of the PVCs in the presence of domain deformation and a clump of DAD-capable myocytes. In a cable-type domain we demonstrate how contraction and elongation of the domain, the coupling interval of the DADs with the previous AP, and the width of the probability distribution function (PDF) of the coupling interval influence the source-size requirement for the formation of PVCs. We then extend our cable results to two dimensions (2D) by calculating the effect of the mechano-electric feedback on the origin and evolution of DAD-induced PVCs in 2D tissue. Our work leads to the following insights on the interplay of mechanical deformation and electrophysiology in DAD-induced PVCs: (1): mechano-electrical feedback markedly reduces the source requirement, which we quantify by the size of a clump of DAD-myocytes; (2): an extended coupling interval of DADs, relative to the preceding AP, reduces the source requirement; (3): sparse distribution of DAD-myocytes in a clump and the distribution in their coupling intervals, along with mechano-electric feedback, reduces the chances of the DAD-induced PVCs; (4): this suppression of PVCs is more pronounced in 2D than in 1D domains.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111436"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145916856","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}
We present a high-performance predictive framework for anticancer peptide (ACP) identification, based on a stacking ensemble learning approach that synergistically combines convolutional neural networks and transformer models using a random forest as a meta-classifier. This architecture is driven by conjoint sequence representations that integrate both one-hot encoding and pre-trained evolutionary scale modeling embeddings, enabling the extraction of complementary local and global features from peptide sequences. Our proposed model achieved a robust accuracy of 88.9% on the primary ACP data set, while maintaining competitive or superior performance across multiple external benchmark data sets, with accuracies ranging from 83.2% to 95.2%, highlighting its strong generalization capability on par with the state-of-the-art models. To demonstrate translational relevance, the model was applied to a curated set of clinically approved and candidate ACPs, producing probabilistic scores to support experimental prioritization. To further enhance model interpretability, SHapley Additive exPlanations analysis was employed, revealing lysine as a consistently influential residue, alongside other positively charged and hydrophobic amino acids. These findings not only corroborate known mechanistic insights into ACP-membrane interactions but also highlight the utility of model-derived feature importance in guiding peptide design. Taken together, this work introduces a robust, interpretable, and generalizable approach for computational ACP prediction, offering valuable implications for peptide-based anticancer drug discovery. To enhance the accessibility and translational potential of our model, we developed an interactive web-based prediction tool, named ACPredictor, for the identification of ACPs. This platform is freely available at https://acpredictor.streamlit.app/.
{"title":"Accurate prediction of anticancer peptides using a stacking ensemble of convolutional and transformer models with conjoint sequence representations","authors":"Huynh Anh Duy , Phurinut Khampasri , Pimmada Janthanet , Patlissa Pattiyamongkhonkul , Tarapong Srisongkram","doi":"10.1016/j.compbiomed.2026.111463","DOIUrl":"10.1016/j.compbiomed.2026.111463","url":null,"abstract":"<div><div>We present a high-performance predictive framework for anticancer peptide (ACP) identification, based on a stacking ensemble learning approach that synergistically combines convolutional neural networks and transformer models using a random forest as a meta-classifier. This architecture is driven by conjoint sequence representations that integrate both one-hot encoding and pre-trained evolutionary scale modeling embeddings, enabling the extraction of complementary local and global features from peptide sequences. Our proposed model achieved a robust accuracy of 88.9% on the primary ACP data set, while maintaining competitive or superior performance across multiple external benchmark data sets, with accuracies ranging from 83.2% to 95.2%, highlighting its strong generalization capability on par with the state-of-the-art models. To demonstrate translational relevance, the model was applied to a curated set of clinically approved and candidate ACPs, producing probabilistic scores to support experimental prioritization. To further enhance model interpretability, SHapley Additive exPlanations analysis was employed, revealing lysine as a consistently influential residue, alongside other positively charged and hydrophobic amino acids. These findings not only corroborate known mechanistic insights into ACP-membrane interactions but also highlight the utility of model-derived feature importance in guiding peptide design. Taken together, this work introduces a robust, interpretable, and generalizable approach for computational ACP prediction, offering valuable implications for peptide-based anticancer drug discovery. To enhance the accessibility and translational potential of our model, we developed an interactive web-based prediction tool, named <em>ACPredictor</em>, for the identification of ACPs. This platform is freely available at <span><span>https://acpredictor.streamlit.app/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111463"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974291","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}
Pub Date : 2026-02-01Epub Date: 2026-01-15DOI: 10.1016/j.compbiomed.2026.111476
F. Saritha , R. Aswath Kumar , K.V. Dileep
Progesterone (P4) is a steroid hormone involved in the regulation of female reproductive functions. The endogenous progesterone receptor (PR), a member of the nuclear receptor family of ligand-dependent transcription regulators responsible for P4 action in the body through the ‘ligand binding domain’ (LBD). PR isoforms, PR-A and PR-B, are encoded by a single gene, PGR and variations in this gene can disrupt cellular signaling. In the current study, putative disease-causing mutations on PR has been identified through computationally and its mechanistic effects were explored using structural bioinformatics tools. Studies suggested that 11 of 66 missense variants (within the LBD) induce structural destabilization and were identified as potentially deleterious. Our ensemble docking suggested that these variations have a limited impact on P4 binding, however they significantly disrupt the binding of co-activators as evident by the protein-peptide docking. The binding of co-activators to the PR is the determining factor for the P4 signaling. Finally, based on the free energy of binding, we proposed two variations such as R869H and C798Y could cause myoma and progesterone tolerance conditions respectively. These findings were further validated through the use of allostery predictions. Our results reveal distinct mechanisms by which PR mutations modulate receptor function, laying the framework for future mechanistic studies and therapeutic development for PR-associated reproductive disorders.
{"title":"Unravelling the structural impact of progesterone receptor mutations in myoma and progesterone intolerance through computational modeling","authors":"F. Saritha , R. Aswath Kumar , K.V. Dileep","doi":"10.1016/j.compbiomed.2026.111476","DOIUrl":"10.1016/j.compbiomed.2026.111476","url":null,"abstract":"<div><div>Progesterone (P4) is a steroid hormone involved in the regulation of female reproductive functions. The endogenous progesterone receptor (PR), a member of the nuclear receptor family of ligand-dependent transcription regulators responsible for P4 action in the body through the ‘ligand binding domain’ (LBD). PR isoforms, PR-A and PR-B, are encoded by a single gene, PGR and variations in this gene can disrupt cellular signaling. In the current study, putative disease-causing mutations on PR has been identified through computationally and its mechanistic effects were explored using structural bioinformatics tools. Studies suggested that 11 of 66 missense variants (within the LBD) induce structural destabilization and were identified as potentially deleterious. Our ensemble docking suggested that these variations have a limited impact on P4 binding, however they significantly disrupt the binding of co-activators as evident by the protein-peptide docking. The binding of co-activators to the PR is the determining factor for the P4 signaling. Finally, based on the free energy of binding, we proposed two variations such as R869H and C798Y could cause myoma and progesterone tolerance conditions respectively. These findings were further validated through the use of allostery predictions. Our results reveal distinct mechanisms by which PR mutations modulate receptor function, laying the framework for future mechanistic studies and therapeutic development for PR-associated reproductive disorders.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111476"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974290","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}
Pub Date : 2026-02-01Epub Date: 2026-01-03DOI: 10.1016/j.compbiomed.2025.111419
Xhesina Hita , Farrukh Javed , Stefano Lodi
Accurate and early detection of Acute Lymphoblastic Leukemia (ALL) is critical for timely intervention and improved patient outcomes. However, the development of reliable deep learning models for hematological image analysis is challenged by limited data availability, dataset bias, and the need for trustworthy predictions in clinical settings. In this study, we propose a Bayesian deep learning framework that integrates transfer learning, data augmentation, and uncertainty quantification for robust classification of leukemic and healthy lymphocytes from peripheral blood smear images. Three widely used convolutional neural network architectures, InceptionV3, VGG16, and ResNet50, pretrained on ImageNet are fine-tuned on the ALL-IDB2 dataset and extended with Monte Carlo dropout to enable Bayesian inference. Model performance is evaluated using 10-fold cross-validation on both original and augmented datasets, with accuracy, sensitivity, specificity, Youden’s index, and Brier score used as evaluation metrics. Among the evaluated models, VGG16 demonstrates the most consistent improvements under data augmentation, achieving the highest accuracy (), Youden’s index () and Brier score (), while ResNet50 shows strong but more moderate gains. In contrast, InceptionV3 exhibits limited sensitivity to augmentation and comparatively lower robustness. Beyond average predictive performance, Bayesian uncertainty analysis reveals that misclassifications and borderline predictions are consistently associated with elevated predictive entropy and mutual information. Saliency map inspection further indicates that high-uncertainty cases correspond to diffuse or non-localized attention patterns, suggesting reliance on spurious contextual features rather than stable morphological cues. These findings highlight the importance of uncertainty-aware predictions for identifying cases that may require expert pathological review. Overall, the proposed framework combines strong diagnostic performance with interpretable uncertainty estimates, supporting its role as a transparent and clinically trustworthy tool for AI-assisted leukemia screening.
{"title":"Reliable leukemia detection via transfer-enhanced Bayesian CNNs","authors":"Xhesina Hita , Farrukh Javed , Stefano Lodi","doi":"10.1016/j.compbiomed.2025.111419","DOIUrl":"10.1016/j.compbiomed.2025.111419","url":null,"abstract":"<div><div>Accurate and early detection of Acute Lymphoblastic Leukemia (ALL) is critical for timely intervention and improved patient outcomes. However, the development of reliable deep learning models for hematological image analysis is challenged by limited data availability, dataset bias, and the need for trustworthy predictions in clinical settings. In this study, we propose a Bayesian deep learning framework that integrates transfer learning, data augmentation, and uncertainty quantification for robust classification of leukemic and healthy lymphocytes from peripheral blood smear images. Three widely used convolutional neural network architectures, InceptionV3, VGG16, and ResNet50, pretrained on ImageNet are fine-tuned on the ALL-IDB2 dataset and extended with Monte Carlo dropout to enable Bayesian inference. Model performance is evaluated using 10-fold cross-validation on both original and augmented datasets, with accuracy, sensitivity, specificity, Youden’s index, and Brier score used as evaluation metrics. Among the evaluated models, VGG16 demonstrates the most consistent improvements under data augmentation, achieving the highest accuracy (<span><math><mn>98.65</mn><mi>%</mi><mo>±</mo><mn>0.09</mn></math></span>), Youden’s index (<span><math><mn>0.97</mn><mo>±</mo><mn>0.001</mn></math></span>) and Brier score (<span><math><mn>0.035</mn><mo>±</mo><mn>0.010</mn></math></span>), while ResNet50 shows strong but more moderate gains. In contrast, InceptionV3 exhibits limited sensitivity to augmentation and comparatively lower robustness. Beyond average predictive performance, Bayesian uncertainty analysis reveals that misclassifications and borderline predictions are consistently associated with elevated predictive entropy and mutual information. Saliency map inspection further indicates that high-uncertainty cases correspond to diffuse or non-localized attention patterns, suggesting reliance on spurious contextual features rather than stable morphological cues. These findings highlight the importance of uncertainty-aware predictions for identifying cases that may require expert pathological review. Overall, the proposed framework combines strong diagnostic performance with interpretable uncertainty estimates, supporting its role as a transparent and clinically trustworthy tool for AI-assisted leukemia screening.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111419"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883525","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}
Pub Date : 2026-02-01Epub Date: 2026-01-07DOI: 10.1016/j.compbiomed.2026.111451
Jerine Peter Simon , Huiming Bao , Zhanyu Niu , Hongyang Man , Shouliang Dong
Cannabinoid receptors 1 and 2 (CNR1 and CNR2) play an important role in the endocannabinoid system and intracellular pathways. Their beneficial effects became destroyed in the event of some mutation. Identifying protective drugs for mutation is highly necessary to eliminate its destruction. The research aims to understand the role of chimeric peptides in CNR1 and CNR2 nsSNPs. The research objectives are to find destructive nsSNPs through in-silico prediction and protein-only state molecular dynamics simulation. To understand the role of chimeric peptides on interaction with nsSNPs through docking analysis and protein-ligand state molecular dynamics simulation. To validate in-silico findings through in-vitro cell expression experiments. The research methods involved 36 in-silico online prediction tools, simulation for 100 ns, 29 trajectory analysis modules, three docking programs, and western blotting techniques. Four CNR1 and six CNR2 nsSNPs were shortlisted as destructive nsSNPs using in-silico tools. Further, CNR1 N134T and CNR2 I298N nsSNPs were shortlisted as highly destructive nsSNPs using simulation and docking analysis. Beneficial peptides were shortlisted using docking analysis and simulation with wild type. This research found that the chimeric peptide MP-13 has the best binding affinity and dynamics properties with wild-type and nsSNPs. It also almost restores dynamics properties and improves binding affinity in nsSNPs. It is most effective in influencing CNR1 and CNR2 signaling responses despite nsSNPs. Based on in-silico and in-vitro analysis, the research concludes that MP-13 has the strongest effect on CNR1 and CNR2 nsSNPs. The workflow of the present research is represented in a graphical illustration.
{"title":"In-silico and In-vitro role of chimeric peptide on the impact of nsSNPs in human Cannabinoid receptors 1 and 2","authors":"Jerine Peter Simon , Huiming Bao , Zhanyu Niu , Hongyang Man , Shouliang Dong","doi":"10.1016/j.compbiomed.2026.111451","DOIUrl":"10.1016/j.compbiomed.2026.111451","url":null,"abstract":"<div><div>Cannabinoid receptors 1 and 2 (CNR1 and CNR2) play an important role in the endocannabinoid system and intracellular pathways. Their beneficial effects became destroyed in the event of some mutation. Identifying protective drugs for mutation is highly necessary to eliminate its destruction. The research aims to understand the role of chimeric peptides in CNR1 and CNR2 nsSNPs. The research objectives are to find destructive nsSNPs through <em>in-silico</em> prediction and protein-only state molecular dynamics simulation. To understand the role of chimeric peptides on interaction with nsSNPs through docking analysis and protein-ligand state molecular dynamics simulation. To validate <em>in-silico</em> findings through <em>in-vitro</em> cell expression experiments. The research methods involved 36 <em>in-silico</em> online prediction tools, simulation for 100 ns, 29 trajectory analysis modules, three docking programs, and western blotting techniques. Four CNR1 and six CNR2 nsSNPs were shortlisted as destructive nsSNPs using <em>in-silico</em> tools. Further, CNR1 N134T and CNR2 I298N nsSNPs were shortlisted as highly destructive nsSNPs using simulation and docking analysis. Beneficial peptides were shortlisted using docking analysis and simulation with wild type. This research found that the chimeric peptide MP-13 has the best binding affinity and dynamics properties with wild-type and nsSNPs. It also almost restores dynamics properties and improves binding affinity in nsSNPs. It is most effective in influencing CNR1 and CNR2 signaling responses despite nsSNPs. Based on <em>in-silico</em> and <em>in-vitro</em> analysis, the research concludes that MP-13 has the strongest effect on CNR1 and CNR2 nsSNPs. The workflow of the present research is represented in a graphical illustration.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111451"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923032","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}
Fusing transrectal ultrasound (TRUS) and magnetic resonance (MR) images has significantly improved the accuracy of prostate cancer detection during targeted biopsies. However, automatic MR-TRUS registration remains challenging due to the large anatomical and appearance differences between the two imaging modalities. This study introduces a fully automatic, weakly supervised deep learning (DL) approach for predicting dense displacement fields (DDFs) between 3D MR and 3D TRUS images without requiring segmentation images. The proposed method consists of two main stages: (1) a dl-based preprocessing step that aligns the prostate centroids of the MR and TRUS images to improve initialization, and (2) a UNet-inspired registration network (RegResUNet) combined with a spatial transformer layer (STL) that directly predicts voxel-level DDFs from the aligned 3D input images. The network is trained using weak supervision based on anatomical masks, enabling accurate registration without ground truth DDFs. Experimental results demonstrate that the proposed method achieves substantial improvements over rigid baseline registration, with an average surface registration error (SRE) of 0.97 0.85 mm and an average Dice similarity coefficient (DSC) of 0.93 0.02. Notably, the proposed network outperforms several state-of-the-art non-rigid registration models while maintaining computational efficiency. The whole pipeline eliminates the need for intermediate manual and time-consuming segmentation steps. The automatic and robust capabilities of the proposed approach, combined with its short inference time, highlight its potential for clinical use in prostate cancer interventions, reducing human factors, ensuring consistent results, and contributing to improved patient comfort by minimizing procedure time.
融合经直肠超声（TRUS）和磁共振（MR）图像在靶向活检中显著提高了前列腺癌检测的准确性。然而，由于两种成像方式之间存在巨大的解剖和外观差异，自动MR-TRUS配准仍然具有挑战性。本研究引入了一种全自动、弱监督深度学习（DL）方法，用于预测3D MR和3D TRUS图像之间的密集位移场（ddf），而不需要分割图像。提出的方法包括两个主要阶段：(1)基于dl的预处理步骤，对MR和TRUS图像的前列腺质心进行对齐，以改善初始化；(2)基于unet的配准网络（RegResUNet）结合空间转换层（STL），直接从对齐的3D输入图像中预测体素级ddf。该网络使用基于解剖掩模的弱监督进行训练，实现了准确的配准，而不需要ground truth ddf。实验结果表明，与刚性基线配准相比，该方法取得了显著的改进，平均表面配准误差（SRE）为0.97±0.85 mm，平均Dice相似系数（DSC）为0.93±0.02。值得注意的是，所提出的网络在保持计算效率的同时优于几种最先进的非刚性配准模型。整个管道消除了中间手工和耗时的分割步骤的需要。所提出的方法的自动和强大的能力，加上其短的推断时间，突出了其在前列腺癌干预的临床应用潜力，减少了人为因素，确保了结果的一致性，并通过减少手术时间来提高患者的舒适度。
{"title":"Automating prostate biopsy guidance: A robust CNN approach for non-rigid 3D/3D MR-TRUS image registration","authors":"Thi Thao Ho , Clément Beitone , Jocelyne Troccaz , Sandrine Voros","doi":"10.1016/j.compbiomed.2025.111423","DOIUrl":"10.1016/j.compbiomed.2025.111423","url":null,"abstract":"<div><div>Fusing transrectal ultrasound (TRUS) and magnetic resonance (MR) images has significantly improved the accuracy of prostate cancer detection during targeted biopsies. However, automatic MR-TRUS registration remains challenging due to the large anatomical and appearance differences between the two imaging modalities. This study introduces a fully automatic, weakly supervised deep learning (DL) approach for predicting dense displacement fields (DDFs) between 3D MR and 3D TRUS images without requiring segmentation images. The proposed method consists of two main stages: (1) a <span>dl</span>-based preprocessing step that aligns the prostate centroids of the MR and TRUS images to improve initialization, and (2) a UNet-inspired registration network (RegResUNet) combined with a spatial transformer layer (STL) that directly predicts voxel-level DDFs from the aligned 3D input images. The network is trained using weak supervision based on anatomical masks, enabling accurate registration without ground truth DDFs. Experimental results demonstrate that the proposed method achieves substantial improvements over rigid baseline registration, with an average surface registration error (SRE) of 0.97 <span><math><mo>±</mo></math></span> 0.85 mm and an average Dice similarity coefficient (DSC) of 0.93 <span><math><mo>±</mo></math></span> 0.02. Notably, the proposed network outperforms several state-of-the-art non-rigid registration models while maintaining computational efficiency. The whole pipeline eliminates the need for intermediate manual and time-consuming segmentation steps. The automatic and robust capabilities of the proposed approach, combined with its short inference time, highlight its potential for clinical use in prostate cancer interventions, reducing human factors, ensuring consistent results, and contributing to improved patient comfort by minimizing procedure time.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111423"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923037","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}
Protein palmitoylation, a reversible post-translational lipid modification, has been implicated in regulating cancer cell signaling and progression; however, its role in prostate cancer (PCa) remains unclear. In this study, we comprehensively analyzed palmitoylation-related genes (PRGs) in PCa by integrating bulk transcriptomic, single-cell RNA sequencing, and spatial transcriptomic datasets. Unsupervised consensus clustering based on PRG expression identified two molecular subtypes with distinct prognoses, immune infiltration profiles, and pathway activities. Differential expression and weighted gene co-expression network analyses revealed five key feature genes, among which KIFC2 was highly expressed in tumor cells and correlated with poor clinical outcomes. The KIFC2 was predominantly enriched in high-grade adenocarcinoma regions. Functional experiments demonstrated that silencing KIFC2 significantly inhibited proliferation and promoted apoptosis in PC3 and DU145 prostate cancer cell lines. Additionally, high KIFC2 expression was associated with increased cell cycle progression and oncogenic signaling pathways, including KRAS and PI3K-AKT. Collectively, these results suggest that palmitoylation and KIFC2 play critical roles in PCa progression and may serve as promising biomarkers and therapeutic targets.
{"title":"Molecular characterization of palmitoylation in prostate cancer reveals KIFC2 as a prognostic biomarker and potential therapeutic target","authors":"Liang Huang , Shusuan Jiang , Fuhua Zeng , Gongqian Zeng , Hong Shan","doi":"10.1016/j.compbiomed.2026.111450","DOIUrl":"10.1016/j.compbiomed.2026.111450","url":null,"abstract":"<div><div>Protein palmitoylation, a reversible post-translational lipid modification, has been implicated in regulating cancer cell signaling and progression; however, its role in prostate cancer (PCa) remains unclear. In this study, we comprehensively analyzed palmitoylation-related genes (PRGs) in PCa by integrating bulk transcriptomic, single-cell RNA sequencing, and spatial transcriptomic datasets. Unsupervised consensus clustering based on PRG expression identified two molecular subtypes with distinct prognoses, immune infiltration profiles, and pathway activities. Differential expression and weighted gene co-expression network analyses revealed five key feature genes, among which KIFC2 was highly expressed in tumor cells and correlated with poor clinical outcomes. The KIFC2 was predominantly enriched in high-grade adenocarcinoma regions. Functional experiments demonstrated that silencing KIFC2 significantly inhibited proliferation and promoted apoptosis in PC3 and DU145 prostate cancer cell lines. Additionally, high KIFC2 expression was associated with increased cell cycle progression and oncogenic signaling pathways, including KRAS and PI3K-AKT. Collectively, these results suggest that palmitoylation and KIFC2 play critical roles in PCa progression and may serve as promising biomarkers and therapeutic targets.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111450"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145951441","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}
Pub Date : 2026-02-01Epub Date: 2026-01-10DOI: 10.1016/j.compbiomed.2026.111464
Stefan Borik , Miriam Zemanikova , Patrik Procka , Jan Seleng , Michal Labuda , Aymen A. Alian
Background
Skin prick testing (SPT) remains the gold standard for allergy diagnostics, yet it relies on subjective assessment of wheal size and provides limited insight into the underlying vascular mechanisms. In this study, the histamine positive control within the SPT framework was used as a standardized model to characterize frequency-specific microvascular responses using photoplethysmography imaging (PPGI).
Methods
PPGI data were analyzed from 16 healthy volunteers who underwent SPT with histamine and saline control sites. Vascular oscillations were analyzed in the cardiac (0.6–2.5 Hz), respiratory (0.15–0.6 Hz), and myogenic (0.05–0.15 Hz) frequency bands using 1-min segments to characterize time-resolved changes across the post-application stages.
Results
A significant suppression of myogenic oscillations was detected at the histamine site, with an 80 % reduction observed 10–15 min post-application and a 60 % reduction at 15–20 min relative to baseline. Respiratory-band energy showed no significant changes during the experiment. No significant changes were observed at the negative control sites. Skin blood-volume-oscillation maps supported these observations, showing localized reduction of myogenic vasomotion in the affected areas.
Conclusion
Histamine-induced allergic responses produce localized, frequency-specific alterations in cutaneous blood flow, characterized by a sustained suppression of myogenic oscillations accompanied with flare-related hyperemia. These results support the hypothesis of “vascular locking,” wherein vasodilation attenuates smooth muscle contractility, and highlight the potential of PPGI to enhance the diagnostic resolution of allergy testing by capturing vascular dynamics across multiple frequency bands.
{"title":"Photoplethysmography imaging reveals frequency-specific microvascular responses to histamine in human skin","authors":"Stefan Borik , Miriam Zemanikova , Patrik Procka , Jan Seleng , Michal Labuda , Aymen A. Alian","doi":"10.1016/j.compbiomed.2026.111464","DOIUrl":"10.1016/j.compbiomed.2026.111464","url":null,"abstract":"<div><h3>Background</h3><div>Skin prick testing (SPT) remains the gold standard for allergy diagnostics, yet it relies on subjective assessment of wheal size and provides limited insight into the underlying vascular mechanisms. In this study, the histamine positive control within the SPT framework was used as a standardized model to characterize frequency-specific microvascular responses using photoplethysmography imaging (PPGI).</div></div><div><h3>Methods</h3><div>PPGI data were analyzed from 16 healthy volunteers who underwent SPT with histamine and saline control sites. Vascular oscillations were analyzed in the cardiac (0.6–2.5 Hz), respiratory (0.15–0.6 Hz), and myogenic (0.05–0.15 Hz) frequency bands using 1-min segments to characterize time-resolved changes across the post-application stages.</div></div><div><h3>Results</h3><div>A significant suppression of myogenic oscillations was detected at the histamine site, with an 80 % reduction observed 10–15 min post-application and a 60 % reduction at 15–20 min relative to baseline. Respiratory-band energy showed no significant changes during the experiment. No significant changes were observed at the negative control sites. Skin blood-volume-oscillation maps supported these observations, showing localized reduction of myogenic vasomotion in the affected areas.</div></div><div><h3>Conclusion</h3><div>Histamine-induced allergic responses produce localized, frequency-specific alterations in cutaneous blood flow, characterized by a sustained suppression of myogenic oscillations accompanied with flare-related hyperemia. These results support the hypothesis of “vascular locking,” wherein vasodilation attenuates smooth muscle contractility, and highlight the potential of PPGI to enhance the diagnostic resolution of allergy testing by capturing vascular dynamics across multiple frequency bands.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111464"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145951492","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}
Pub Date : 2026-02-01Epub Date: 2026-01-02DOI: 10.1016/j.compbiomed.2025.111433
Hailah M. Almohaimeed , Amany I. Almars , Nada Alkhorayef , Ahmed M. Basri , Fayez Alsulaimani , Muhammad Shahbaz , Fahad M. Alshabrmi , Sarfaraz Alam , Tahir Muhammad , Muhammad Shahab
Enterovirus D68 (EV-D68) is an enterovirus known for causing respiratory infections, as well as flaccid myelitis, meningitis and encephalitis. Despite the efforts, no licensed vaccine against EV-D68 is currently available. Vaccine development efforts are ongoing; however, the process is complex and requires extensive clinical validation. In contrast, immunoinformatic is a rapidly expanding area with the potential to significantly influence the therapeutic interventions and vaccine development for infectious diseases. Herein, immunoinformatic and reverse vaccinology strategies were utilized to design a multi-epitope vaccine construct targeting EV-D68 virus. In this connection, three virulent proteins were selected for analysis based on their immunogenic characteristics. Further, B-cells and T-cells epitopes were predicted and connected through suitable linkers and adjuvant. The predicted T-cell epitopes within the vaccine construct exhibited a significant worldwide population coverage. Moreover, Robetta was utilized to predict the 3D structure of the vaccine construct. Subsequently the molecular docking simulation of construct was employed to study the molecular interactions by using Toll-like receptors as target proteins and further subjected to MD simulation. The results reveal the stability of the vaccine-receptor complex throughout the simulation. Finally, in silico cloning showed potential for the predicted vaccine within the Escherichia coli expression system. These findings provide valuable insights that may guide subsequent experimental studies and contribute meaningfully to the early phases of EV-D68 vaccine research and development. By streamlining candidate selection and optimizing design parameters, our findings holds promise for accelerating the transition from computational predictions to effective vaccine formulations.
{"title":"Designing of a multi-epitope vaccine targeting enterovirus D68: An integrated immunoinformatic and reverse vaccinology approach","authors":"Hailah M. Almohaimeed , Amany I. Almars , Nada Alkhorayef , Ahmed M. Basri , Fayez Alsulaimani , Muhammad Shahbaz , Fahad M. Alshabrmi , Sarfaraz Alam , Tahir Muhammad , Muhammad Shahab","doi":"10.1016/j.compbiomed.2025.111433","DOIUrl":"10.1016/j.compbiomed.2025.111433","url":null,"abstract":"<div><div>Enterovirus D68 (EV-D68) is an enterovirus known for causing respiratory infections, as well as flaccid myelitis, meningitis and encephalitis. Despite the efforts, no licensed vaccine against EV-D68 is currently available. Vaccine development efforts are ongoing; however, the process is complex and requires extensive clinical validation. In contrast, immunoinformatic is a rapidly expanding area with the potential to significantly influence the therapeutic interventions and vaccine development for infectious diseases. Herein, immunoinformatic and reverse vaccinology strategies were utilized to design a multi-epitope vaccine construct targeting EV-D68 virus. In this connection, three virulent proteins were selected for analysis based on their immunogenic characteristics. Further, B-cells and T-cells epitopes were predicted and connected through suitable linkers and adjuvant. The predicted T-cell epitopes within the vaccine construct exhibited a significant worldwide population coverage. Moreover, Robetta was utilized to predict the 3D structure of the vaccine construct. Subsequently the molecular docking simulation of construct was employed to study the molecular interactions by using Toll-like receptors as target proteins and further subjected to MD simulation. The results reveal the stability of the vaccine-receptor complex throughout the simulation. Finally, <em>in silico</em> cloning showed potential for the predicted vaccine within the <em>Escherichia coli</em> expression system. These findings provide valuable insights that may guide subsequent experimental studies and contribute meaningfully to the early phases of EV-D68 vaccine research and development. By streamlining candidate selection and optimizing design parameters, our findings holds promise for accelerating the transition from computational predictions to effective vaccine formulations.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111433"},"PeriodicalIF":6.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882871","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}
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