Computers in biology and medicine最新文献_第5页

Multi-structure CT radiomics-based consensus model for the diagnosis of pancreatic ductal adenocarcinoma and vascular involvement 基于多结构CT放射组学的胰腺导管腺癌及血管累及诊断共识模型。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-09 DOI: 10.1016/j.compbiomed.2026.111542

Jia Peng , Shiyao Xie , Xinnan Liao , Mengnan Tai , Zixuan Nie , Yaoqi Wang , Zhiyuan Chen , Zheng Wang , Ya Peng

Background

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy, with accurate preoperative assessment of vascular involvement critical for determining resectability and treatment planning. Conventional contrast-enhanced CT relies on qualitative evaluations, leading to interobserver variability and diagnostic uncertainty. Existing radiomics studies for PDAC mostly focus on single anatomical structures and lack organ-level interpretability, limiting clinical translation.

Methods

A retrospective study was conducted using the international PANORAMA CT cohort, with 1488 eligible samples stratified into PDAC diagnosis (1186 cases) and vascular involvement prediction (302 cases) tasks. Standardized radiomic features were extracted from five key structures (artery, vein, pancreatic parenchyma, pancreatic duct, common bile duct) following IBSI guidelines. After LASSO-based dimensionality reduction, six machine learning classifiers were trained for each structure, with top-performing models integrated into structure-specific consensus models. A meta-level consensus model was constructed via stacking, and SHAP analysis was applied for organ-level interpretability. Model performance was evaluated using AUC, accuracy, calibration curves, and decision curve analysis (DCA).

Results

The multi-structure consensus model achieved an AUC of 0.975 (95% CI: 0.956–0.990) with 0.937 accuracy for PDAC diagnosis, and an AUC of 0.868 (95% CI: 0.769–0.952) with 0.803 accuracy for vascular involvement prediction in independent testing cohorts. DeLong tests demonstrated the model significantly outperformed four single-structure models (artery, vein, pancreatic duct, common bile duct) in both tasks (all P < 0.05), with no significant difference compared to the pancreas parenchyma model (PDAC diagnosis: P = 0.078; vascular involvement prediction: P = 0.093). SHAP analysis identified pancreatic parenchyma as the dominant contributor to PDAC diagnosis and arterial features as key for vascular involvement prediction. The model exhibited robust calibration (MAE = 0.01 for PDAC; MAE = 0.02 for vascular involvement) and clinical net benefit via DCA.

Conclusion

The proposed multi-structure CT radiomics consensus model integrates contextual information from multiple pancreatic structures, achieving competitive performance for PDAC diagnosis and vascular involvement prediction. Organ-level SHAP interpretation enhances clinical transparency, offering a reliable tool to support preoperative decision-making in PDAC.

背景：胰腺导管腺癌（Pancreatic ductal adencarcinoma， PDAC）是一种高致死率的恶性肿瘤，术前准确评估血管受累情况对确定可切除性和治疗计划至关重要。传统的对比增强CT依赖于定性评估，导致观察者之间的差异和诊断的不确定性。现有的放射组学研究主要集中在单个解剖结构上，缺乏器官水平的可解释性，限制了临床翻译。方法：采用国际PANORAMA CT队列进行回顾性研究，1488例符合条件的样本分为PDAC诊断（1186例）和血管受累预测（302例）任务。按照IBSI指南提取5个关键结构（动脉、静脉、胰腺实质、胰管、胆总管）的标准化放射学特征。在基于lasso的降维之后，为每个结构训练了六个机器学习分类器，其中表现最好的模型集成到特定于结构的共识模型中。通过堆叠构建了元水平共识模型，并采用SHAP分析对器官水平的可解释性进行分析。使用AUC、精度、校准曲线和决策曲线分析（DCA）评估模型性能。结果：多结构共识模型在PDAC诊断中的AUC为0.975 (95% CI: 0.956-0.990)，准确率为0.937；在独立测试队列中，血管受损伤预测的AUC为0.868 (95% CI: 0.769-0.952)，准确率为0.803。DeLong测试表明，该模型在两项任务中都明显优于四种单结构模型（动脉、静脉、胰管、胆总管）（均为P）。结论：所提出的多结构CT放射组学共识模型集成了来自多个胰腺结构的上下文信息，在PDAC诊断和血管受累预测方面具有竞争力。器官水平的SHAP解释提高了临床透明度，为支持PDAC的术前决策提供了可靠的工具。

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引用次数: 0

Topology-aware multiclass segmentation of the Circle of Willis from MRA and CTA images 基于拓扑感知的MRA和CTA图像中Willis环的多类分割。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.compbiomed.2026.111516

Rachika E. Hamadache, Clara Lisazo, Cansu Yalcin, Uma M. Lal-Trehan Estrada, Valeriia Abramova, Adrià Casamitjana, Arnau Oliver, Xavier Lladó

The Circle of Willis (CoW) is an essential network of arteries that ensures blood flow throughout the brain. From a clinical perspective, evaluating the vessels of the CoW is highly relevant as its angioarchitecture and variants are important biomarkers of neurovascular pathologies. However, achieving a topologically accurate segmentation of these vessels remains challenging due to their anatomical complexity. In this work, we propose a pipeline for the multiclass segmentation of the CoW vessels (13 possible classes), focusing on achieving both topology correctness and segmentation accuracy in magnetic resonance angiography (MRA) and computed tomography angiography (CTA) imaging techniques. We propose a deep learning framework based on the nnUNet model, together with a post-processing block that requires no additional training and that is adapted to the specific multiclass CoW segmentation task. We train and validate our framework using the publicly available TopCoW 2024 dataset (MRA and CTA) and evaluate it on the hidden test set (through an online system) and on an independent subset from the CROWN 2023 challenge dataset (MRA). The obtained results demonstrate the positive impact of our approach, achieving an average Dice (centerline Dice) scores of 0.90 (0.99) for MRA and 0.88 (0.99) for CTA on the in-domain test set, and 0.81 (0.97) on the out-of-domain test set for MRA. These high performances align with state-of-the-art methods, and rank among the top in the TopCoW 2024 challenge. The approach is publicly available for the research community at https://github.com/NIC-VICOROB/CoW-multiclass-segmentation-TopCoW24.

威利斯圈（CoW）是一个重要的动脉网络，确保血液在整个大脑中流动。从临床角度来看，评估CoW的血管是高度相关的，因为其血管结构和变异是神经血管病变的重要生物标志物。然而，由于其解剖复杂性，实现这些血管的拓扑精确分割仍然具有挑战性。在这项工作中，我们提出了一个用于CoW血管多类别分割的管道（13个可能的类别），重点是在磁共振血管成像（MRA）和计算机断层血管成像（CTA）成像技术中实现拓扑正确性和分割准确性。我们提出了一个基于nnUNet模型的深度学习框架，以及一个不需要额外训练的后处理块，该后处理块适用于特定的多类CoW分割任务。我们使用公开可用的TopCoW 2024数据集（MRA和CTA）训练和验证我们的框架，并在隐藏测试集（通过在线系统）和来自CROWN 2023挑战数据集（MRA）的独立子集上对其进行评估。得到的结果证明了我们的方法的积极影响，在域内测试集中，MRA的平均Dice（中心线Dice）得分为0.90 (0.99)，CTA的平均Dice (0.88) (0.99)， MRA的域外测试集中的平均Dice(0.81)（0.97）。这些高性能与最先进的方法相一致，并在TopCoW 2024挑战中名列前茅。该方法可在研究社区的https://github.com/NIC-VICOROB/CoW-multiclass-segmentation-TopCoW24上公开获取。

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引用次数: 0

Network pharmacology and computational-based approaches to activate NRF2 pathway via KEAP1 and GSK-3β inhibition: Exploring the possible molecular insights of mangiferin for Alzheimer's 网络药理学和基于计算的方法通过KEAP1和GSK-3β抑制激活NRF2通路：探索芒果苷治疗阿尔茨海默病的可能分子见解。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.compbiomed.2026.111507

Vishnu Malakar , S.P. Dhanabal , Dhritiman Roy , Chandi C. Malakar , Pratik Khona , Antony Justin

Mangifera indica has been utilized as an adjunct therapy for Alzheimer's disease (AD) due to its anti-Alzheimer's phytoconstituents. However, the underlying molecular mechanisms remain largely elusive. This research aimed to investigate the mechanism of action of Mangifera indica phytoconstituents in AD therapy. Anti-Alzheimer's phytoconstituents were identified from the literature and database, their related targets and associated pathways relevant to AD. Protein-protein interaction (PPI) networks were constructed using the STRING database and visualised through Cytoscape software. Target cluster module analysis was performed using the MCODE plugin in Cytoscape. Additionally, Gene Ontology and KEGG analyses were conducted to identify targets associated with Mangifera indica and AD. Furthermore, computational studies were conducted using AutoDock Vina tools, GROMACS, and Gaussian software. In this study, 15 active phytoconstituents and their 157 common targets were analysed. Based on topological parameters such as degree, closeness, and betweenness, the top five targets: Nrf2, Keap1, GSK-3β, APP, and PTPN1 were identified as critical nodes associated with regulation of Nrf2 signalling involving Keap1 and GSK-3β in the context of AD therapy. Molecular docking, MD simulations (1000 ns), PCA, DFT, and MM-PBSA analyses of Nrf2, Keap1, and GSK-3β demonstrated that the compound Mangiferin exhibited favourable predicted binding, stable interaction behaviour, and consistent equilibrium dynamics in comparison with reference ligands. This research highlights that Mangifera indica-related AD therapy involves a complex interplay of multiple phytoconstituents, molecular targets, and signalling pathways and offers significant molecular insights of Mangifera indica into potential antioxidant, anti-inflammatory, and neuroprotective mechanisms relevant to neuronal cells.

芒果因其抗阿尔茨海默病的植物成分而被用作阿尔茨海默病（AD）的辅助治疗。然而，潜在的分子机制在很大程度上仍然难以捉摸。本研究旨在探讨芒果属植物成分在AD治疗中的作用机制。从文献和数据库中鉴定出抗阿尔茨海默病的植物成分，以及与AD相关的相关靶点和相关途径。蛋白质-蛋白质相互作用（PPI）网络使用STRING数据库构建，并通过Cytoscape软件可视化。使用Cytoscape中的MCODE插件进行目标簇模块分析。此外，还进行了Gene Ontology和KEGG分析，以确定与芒果和AD相关的靶点。此外，使用AutoDock Vina工具、GROMACS和高斯软件进行计算研究。本研究分析了15种植物活性成分及其157个共同靶点。基于拓扑参数，如程度、紧密度和间性，我们确定了前5个靶点：Nrf2、Keap1、GSK-3β、APP和PTPN1是AD治疗中涉及Keap1和GSK-3β的Nrf2信号调控的关键节点。对Nrf2、Keap1和GSK-3β的分子对接、MD模拟（1000 ns）、PCA、DFT和MM-PBSA分析表明，与参考配体相比，芒果苷具有良好的预测结合、稳定的相互作用行为和一致的平衡动力学。本研究强调了芒果相关的AD治疗涉及多种植物成分、分子靶点和信号通路的复杂相互作用，并为芒果提供了与神经元细胞相关的潜在抗氧化、抗炎和神经保护机制的重要分子见解。

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引用次数: 0

Exploring natural products as Bcl-2 inhibitors for acute myeloid leukemia therapy using In vitro, STD-NMR spectroscopy, and In silico approaches 利用体外、STD-NMR波谱和计算机方法探索天然产物作为Bcl-2抑制剂用于急性髓性白血病治疗。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.compbiomed.2026.111506

Noor Rahman , Humaira Zafar , Thirugnanasambandam Rajendran , Ruby Sharif , Ahmed Almehdi , Atia tul-Wahab , Sumbla Sheikh , M. Iqbal Choudhary

Acute myeloid leukemia (AML) is the predominant form of acute leukemia, affecting elderly individuals, typically diagnosed at an average age of 68 years. AML cells rely on the Bcl-2 protein for their survival. Overexpression of Bcl-2 protein in various cancer types renders it as a potential candidate for targeted therapies. The present study aimed to identify natural compounds as Bcl-2 inhibitors using in vitro, biophysical, and integrated computational approaches. The MTT assay was performed for cell proliferation, followed by apoptosis and gene expression analysis. STD-NMR spectroscopy, molecular docking and molecular dynamics simulations were performed for protein-ligand interactions. In the in vitro anti-proliferative assay, three natural compounds, gossypol (1), camptothecin (2), and jaceidin (3), were found active against the HL-60 cell line with IC₅₀ concentrations of 1.634 ± 0.072, 0.137 ± 0.029, and 13.492 ± 2.292 μM, respectively. These compounds triggered apoptosis and decreased cellular viability in a dose-dependent manner. The gene expression analysis of Bax, Bcl-2, and Caspase 3 in HL-60 cells revealed that these compounds induce apoptosis by regulating essential apoptotic genes. Among the three identified potential hits, only gossypol (1) was buffer soluble and subjected to STD-NMR experiment to evaluate its protein-ligand interactions. Furthermore, molecular docking, binding free energies and MD simulation analyses demonstrated stable interactions of these compounds with the Bcl-2 protein. The three natural products showed potent to significant activity, effectively inducing apoptosis in the HL-60 cell line. Hence, this study identifies three potential lead candidates for drug discovery against Bcl-2-related cancers after further mechanistic and pre-clinical studies.

急性髓性白血病（AML）是急性白血病的主要形式，影响老年人，通常在平均年龄68岁诊断。AML细胞依靠Bcl-2蛋白存活。Bcl-2蛋白在各种癌症类型中的过表达使其成为靶向治疗的潜在候选者。本研究旨在利用体外、生物物理和综合计算方法鉴定天然化合物作为Bcl-2抑制剂。MTT法检测细胞增殖，然后进行细胞凋亡和基因表达分析。对蛋白质与配体的相互作用进行了STD-NMR光谱、分子对接和分子动力学模拟。在体外抗增殖实验中，棉酚(1)、喜树碱(2)和紫花苷(3)对HL-60细胞株的IC50浓度分别为1.634±0.072、0.137±0.029和13.492±2.292 μM。这些化合物以剂量依赖的方式引发细胞凋亡和降低细胞活力。对HL-60细胞中Bax、Bcl-2和Caspase 3的基因表达分析表明，这些化合物通过调控凋亡必需基因诱导细胞凋亡。在确定的三个潜在命中点中，只有棉酚(1)是缓冲可溶的，并进行了STD-NMR实验来评估其蛋白质与配体的相互作用。此外，分子对接、结合自由能和MD模拟分析表明，这些化合物与Bcl-2蛋白具有稳定的相互作用。三种天然产物均表现出显著的活性，能有效诱导HL-60细胞株凋亡。因此，本研究在进一步的机制和临床前研究后，确定了三种潜在的bcl -2相关癌症药物开发的主要候选药物。

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引用次数: 0

Systems medicine approach unravels MMP2 and NOTCH3 as key mediators of cigarette smoke-induced airway remodelling in COPD 系统医学方法揭示了MMP2和NOTCH3作为香烟烟雾诱导的COPD气道重塑的关键介质。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.compbiomed.2026.111508

Anupama Dubey , Md Shamim Akhtar , Anamika , Suneel Kateriya , Umesh C.S. Yadav

Background and aim

Cigarette smoking is known to cause airway remodelling leading to loss of lung plasticity, a key feature of chronic obstructive pulmonary disease (COPD). Despite the availability of several disease management approaches, an effective cure is elusive due to a lack of clear molecular insight into COPD pathogenesis. Thus, utilizing bioinformatics tools, this study aimed to identify crucial hub genes in COPD pathogenesis and validate them using in-vitro experiments and COPD patient samples.

Study methodology

In-silico identification of molecular interactions was analysed using bioinformatics tools like String, GEO datasets, CTD, Genecards, Disgenet, Opentargets, and Cytoscape. Airway epithelial cells (AECs) were exposed to different concentrations of cigarette smoke extract (CSE), followed by assessments of fibrosis and EMT-related parameters and markers using cellular and molecular biology techniques such as the MTT assay, AO/EtBr assay, trypan blue assay, the migration and invasion assays, morphological analysis, immunoblotting, immunocytochemistry, and RT-qPCR. Further, key genes expression and cytokines profile were assessed in PBMCs and plasma from COPD patients and healthy volunteers via RT-qPCR and ELISA, respectively.

Key findings

Four online databases (CTD, Genecards, Opentargets, and Disgenet) and a clinical dataset from the Gene Expression Omnibus were utilized to identify upregulated differentially expressed genes (DEGs). Subsequently, ten hub genes for COPD were identified using MCODE and cytohubba indices of Cytoscape, of which NOTCH3 and matrix metalloprotease (MMP) 2 were selected for further validation owing to their crucial role in COPD. CSE exposure of AECs caused alteration in cellular morphology, induced fibrous phenotype, upregulation of fibrosis and EMT markers, and increased expression of NOTCH3 and MMP2. Furthermore, chemical inhibition of MMP2 downregulated NOTCH3, suggesting NOTCH pathway upregulation by CSE-induced MMP2 activation. Inhibition of either MMP2 or NOTCH3 reversed CSE-induced fibrotic or EMT-related changes in AECs. PBMCs derived from COPD patients showed modulation of NOTCH3 and MMP2. JAG1, a NOTCH ligand, and many inflammatory markers were also significantly upregulated in COPD patient samples compared to healthy volunteers.

Significance

Our multi-level holistic approach, combining in-silico and in-vitro studies elucidated that MMP2 and NOTCH3 could be key mediators in CSE-induced airway epithelial cell remodelling, which was also confirmed through COPD patients’ sample analysis. We, thus, identify MMP2 and NOTCH3 as important gene targets for controlling CS-induced COPD pathophysiology.

背景和目的：众所周知，吸烟会导致气道重塑，导致肺可塑性丧失，这是慢性阻塞性肺疾病（COPD）的一个关键特征。尽管有几种疾病管理方法，但由于缺乏对COPD发病机制的明确的分子认识，有效的治疗是难以捉摸的。因此，利用生物信息学工具，本研究旨在确定COPD发病机制中的关键枢纽基因，并通过体外实验和COPD患者样本对其进行验证。研究方法：使用生物信息学工具（如String、GEO数据集、CTD、Genecards、Disgenet、Opentargets和Cytoscape）分析分子相互作用的计算机鉴定。将气道上皮细胞（AECs）暴露于不同浓度的香烟烟雾提取物（CSE）中，然后使用细胞和分子生物学技术（如MTT测定、AO/EtBr测定、特trypan blue测定、迁移和侵袭测定、形态学分析、免疫印迹、免疫细胞化学和RT-qPCR）评估纤维化和emt相关参数和标志物。此外，通过RT-qPCR和ELISA分别评估COPD患者和健康志愿者外周血和血浆中的关键基因表达和细胞因子谱。主要发现：四个在线数据库（CTD、Genecards、Opentargets和Disgenet）和来自基因表达Omnibus的临床数据集被用于鉴定上调的差异表达基因（DEGs）。随后，我们利用Cytoscape的MCODE和cytohubba指数鉴定了10个COPD枢纽基因，其中NOTCH3和基质金属蛋白酶（matrix metalloprotease， MMP） 2因其在COPD中的重要作用而被选中进行进一步验证。CSE暴露于AECs导致细胞形态改变，纤维表型诱导，纤维化和EMT标志物上调，NOTCH3和MMP2表达增加。此外，MMP2的化学抑制下调了NOTCH3，表明cse诱导的MMP2激活上调了NOTCH通路。抑制MMP2或NOTCH3均可逆转cse诱导的aec纤维化或emt相关变化。来自COPD患者的pbmc显示NOTCH3和MMP2的调节。与健康志愿者相比，COPD患者样本中的JAG1、NOTCH配体和许多炎症标志物也显著上调。意义：我们采用多层次的整体方法，结合计算机和体外研究，阐明了MMP2和NOTCH3可能是cse诱导的气道上皮细胞重构的关键介质，这也通过COPD患者的样本分析得到了证实。因此，我们确定MMP2和NOTCH3是控制cs诱导的COPD病理生理的重要基因靶点。

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引用次数: 0

A hybrid swin transformer–BiLSTM framework and ensemble learning for multimodal brain stroke detection and risk prediction 一种用于多模态脑卒中检测和风险预测的混合型旋转变压器- bilstm框架和集成学习。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.compbiomed.2026.111518

Md.Mahfuz Ahmed , Md.Maruf Hossain , Md.Rakibul Hasan Rakib , Ronok Hashan , Md.Touhid Hasan Nirob , Md.Khairul Islam

Stroke is one of the leading causes of mortality and long-term disability worldwide, primarily resulting from the sudden disruption of cerebral blood flow. Early and accurate diagnosis plays a crucial role in minimizing neurological damage and improving recovery outcomes. This study proposes a comprehensive multimodal framework integrating a hybrid Swin Transformer–Bidirectional Long Short-Term Memory (SwinT–BiLSTM) model and an ensemble learning-based classifier for automated stroke detection and risk prediction from medical image and tabular clinical data. This study utilizes two brain stroke Computed Tomography (CT) datasets, including a primary dataset named BrSCTHD-2025, collected from hospitals in Dhaka and Faridpur, Bangladesh, and a secondary Kaggle CT dataset. In addition, a primary clinical tabular dataset was collected from Kushtia Medical College Hospital for multimodal analysis. The proposed SwinT–BiLSTM model efficiently extracts global spatial and sequential dependencies from CT images, while the ensemble classifier predicts stroke risk based on clinical and lifestyle parameters. Experimental results demonstrate that the model achieves 98% accuracy with an AUC of 1.00 on the BrSCTHD-2025 dataset and 97% accuracy with an AUC of 0.99 on the secondary Kaggle dataset, outperforming standalone SwinT by 2.5% and Convolutional Neural Network (CNN) architectures such as VGG16 and ResNet50 by 3%–4%. The ensemble classifier trained on tabular data achieved 80.36% accuracy, identifying critical stroke risk factors such as heart disease, prolonged sitting duration, and cholesterol level. Furthermore, Explainable Artificial Intelligence (XAI) techniques such as LIME, SHAP, enhanced Grad-CAM, and attention maps enhance interpretability by identifying the most influential visual and clinical features. Overall, the proposed SwinT–BiLSTM–Ensemble framework establishes a robust foundation for accurate, interpretable, and clinically reliable stroke diagnosis and personalized risk assessment in real-world healthcare environments.

中风是世界范围内导致死亡和长期残疾的主要原因之一，主要由脑血流突然中断引起。早期和准确的诊断在减少神经损伤和提高康复效果方面起着至关重要的作用。本研究提出了一个综合的多模式框架，集成了混合Swin变压器-双向长短期记忆（SwinT-BiLSTM）模型和基于集成学习的分类器，用于从医学图像和表格临床数据中自动检测和预测中风风险。本研究使用了两个脑卒中计算机断层扫描（CT）数据集，包括一个名为BrSCTHD-2025的主要数据集，收集自孟加拉国达卡和法里德普尔的医院，以及一个次要的Kaggle CT数据集。此外，从库什蒂亚医学院医院收集了一个主要的临床表格数据集，用于多模式分析。所提出的SwinT-BiLSTM模型有效地从CT图像中提取全局空间和顺序依赖关系，而集成分类器根据临床和生活方式参数预测中风风险。实验结果表明，该模型在BrSCTHD-2025数据集上达到98%的准确率，AUC为1.00，在次要Kaggle数据集上达到97%的准确率，AUC为0.99，比独立的SwinT高2.5%，比VGG16和ResNet50等卷积神经网络（CNN）架构高3%-4%。在表格数据上训练的集成分类器识别出心脏病、久坐时间和胆固醇水平等关键中风危险因素的准确率达到80.36%。此外，可解释的人工智能（XAI）技术，如LIME、SHAP、增强型Grad-CAM和注意图，通过识别最具影响力的视觉和临床特征，提高了可解释性。总体而言，所提出的SwinT-BiLSTM-Ensemble框架为真实医疗环境中准确、可解释和临床可靠的脑卒中诊断和个性化风险评估奠定了坚实的基础。

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引用次数: 0

Parameter sensitivity and critical transition anticipation in bistable toxin-antitoxin dynamics 双稳态毒素-抗毒素动力学的参数敏感性和临界过渡预测。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-07 DOI: 10.1016/j.compbiomed.2026.111535

Shankha Narayan Chattopadhyay , Inayat Ullah Irshad , Ajeet K. Sharma , Arvind Kumar Gupta

Toxin–antitoxin systems are central to bacterial persistence, promoting drug tolerance and infection relapse, and therefore demand a clear mechanistic understanding of their regulation. It is thus intriguing to investigate the possible routes to persister cell formation through mathematical modelling and to assess whether their emergence can be anticipated using statistical measures. For this dual purpose, a mathematical model describing the fundamental biochemical interactions among the operon, mRNA, toxin, antitoxin, and two associated protein complexes is considered in this study. The uncertainty in the steady-state behaviour of the deterministic model outcomes is analysed using two complementary forms of global sensitivity analysis. Both these techniques identify six key parameters that substantially influence transcription, translation, and the turnover of antitoxins. Among these, the parameter controlling the quadratic repression of antitoxin through toxin binding has opposite effects on the two species, thereby driving hysteresis between alternate physiological states. Intrinsic noise is introduced into the deterministic model via the chemical master equation. Subsequent Gillespie simulations reveal a critical transition from normal to persister cells, which is then detected using twelve multivariate statistical indicators within moving- and expanding-window frameworks. Sensitivity analyses define hyperparameter ranges that ensure reliable predictions, and robustness tests across repeated simulations show consistent performance for most moving-window indicators, except for some variance–covariance and information-based measures. The expanding-window approach reveals different types of warnings—flickering, sustained, and spurious—quantified by true-positive rates, lead times, and total warning counts. Together, these results demonstrate that multivariate measures can reliably predict critical transitions and provide a solid framework for understanding the loss of resilience in complex biological systems.

毒素-抗毒素系统是细菌持续存在、促进药物耐受性和感染复发的核心，因此需要对其调控有明确的机制理解。因此，通过数学建模来研究持久性细胞形成的可能途径，并评估它们的出现是否可以用统计方法来预测，这是很有趣的。为了实现这一双重目的，本研究考虑了一个描述操纵子、mRNA、毒素、抗毒素和两种相关蛋白复合物之间基本生化相互作用的数学模型。利用全局灵敏度分析的两种互补形式分析了确定性模型结果的稳态行为中的不确定性。这两种技术都确定了六个关键参数，这些参数实质上影响了抗毒素的转录、翻译和周转。其中，通过毒素结合控制抗毒素二次抑制的参数对两种产生相反的作用，从而驱动交替生理状态之间的滞后。本征噪声通过化学主方程引入确定性模型。随后的Gillespie模拟揭示了从正常细胞到持久细胞的关键转变，然后使用12个多变量统计指标在移动和扩展窗口框架内进行检测。敏感性分析定义了确保可靠预测的超参数范围，重复模拟的鲁棒性测试显示，除了一些方差协方差和基于信息的测量外，大多数移动窗口指标的性能都是一致的。扩展窗口方法揭示了不同类型的警告——闪烁、持续和虚假——通过真阳性率、前置时间和总警告计数进行量化。总之，这些结果表明，多变量测量可以可靠地预测关键转变，并为理解复杂生物系统中恢复力的丧失提供坚实的框架。

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引用次数: 0

Comment on: “Multimodal large language models as assistance for evaluation of thyroid-associated ophthalmopathy” 评论：“辅助甲状腺相关性眼病评估的多模态大语言模型”。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.compbiomed.2026.111534

Joonhyeon Park , Kyubo Shin , Jongchan Kim , Jaemin Park , Jae Hoon Moon , JaeSang Ko

引用次数: 0

Molecular modelling assisted identification of novel Benzoxazole derivatives as hit molecules targeting Mycobacterial Membrane Protein Large 3 (MmpL3) 分子模型辅助鉴定新型苯并恶唑衍生物作为分枝杆菌膜蛋白大3 （MmpL3）靶向分子。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-01 DOI: 10.1016/j.compbiomed.2026.111521

Rupesh Chikhale , Vikramsinh Sardarsinh Suryawanshi , Shweta Sharma , Vivek Kumar Gupta , Pramod B. Khedekar

MmpL3 protein plays a vital role in cell wall synthesis in Mycobacterium. Novel benzoxazole carboxamide derivatives were designed to inhibit cell wall formation by targeting the MmpL3 and combat tuberculosis. Fourteen benzoxazole carboxamide derivatives (BXZ-I to BXZ-XIV) were synthesised, and their structures were confirmed using both experimental and computational methods. Techniques such as molecular docking, ADME, toxicity prediction, deep learning-based docking, and molecular dynamics simulation were used to analyse these compounds. Molecules with promising antimycobacterial activity were selected for MDS, MM-GBSA, and FEP analyses. BXZ-IX and BXZ-XIV exhibited potent activity against Mycobacterium smegmatis, with a minimum inhibitory concentration (MIC) of 15.62 μg/mL, compared with SQ109 (standard MmpL3 inhibitor), which had an MIC of 10.0 μg/mL. Overall, ten of the selected benzoxazole compounds significantly inhibited the growth of M. smegmatis, with MICs ranging from 15.62 to 62.5 μg/mL in laboratory tests, demonstrating greater effectiveness against the MmpL3 protein.

MmpL3蛋白在分枝杆菌细胞壁合成中起重要作用。设计了新的苯并恶唑羧酰胺衍生物，通过靶向MmpL3抑制细胞壁形成并对抗结核病。合成了14个苯并恶唑类羧酰胺衍生物（BXZ-I ~ BXZ-XIV），并通过实验和计算方法对其结构进行了确证。分子对接、ADME、毒性预测、基于深度学习的对接、分子动力学模拟等技术对这些化合物进行了分析。选择具有抑菌活性的分子进行MDS、MM-GBSA和FEP分析。BXZ-IX和BXZ-XIV对耻垢分枝杆菌具有较强的抑制活性，最小抑制浓度（MIC）为15.62 μg/mL，而标准MmpL3抑制剂SQ109的MIC为10.0 μg/mL。总体而言，所选的10种苯并恶唑化合物显著抑制耻垢分枝杆菌的生长，在实验室测试中mic范围为15.62至62.5 μg/mL，显示出对MmpL3蛋白更有效。

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引用次数: 0

HybridDeepSynergy: A hybrid deep learning model integrating CNN, LSTM, and attention mechanisms for cancer drug synergy prediction HybridDeepSynergy：一个集成CNN、LSTM和注意机制的混合深度学习模型，用于癌症药物协同作用预测。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-02-15 Epub Date: 2026-01-21 DOI: 10.1016/j.compbiomed.2026.111471

Sajid Naveed , Mujtaba Husnain , Najah Alsubaie

A variety of AI-based approaches have been employed to analyze complex genomic datasets. Predicting the synergy of drug combinations is a critical step toward optimizing cancer treatment by identifying the most effective drug pairs. This study presents HybridDeepSynergy, a novel hybrid deep learning model that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Transformer attention mechanisms to predict drug synergy across diverse drug combinations and cancer cell lines. The model is designed to enhance precision medicine and cancer treatment outcomes.

HybridDeepSynergy leverages CNNs to capture local feature interactions, LSTMs to model sequential dependencies, and attention mechanisms to extract long-range relationships within the data. The model was trained and evaluated on a comprehensive dataset containing numerous drug combinations, using five established synergy scoring models: Bliss Independence (BLISS), Zero Interaction Potency (ZIP), Loewe Additivity (LOEWE), Highest Single Agent (HSA), and General Synergy (S).

Our model demonstrated superior performance compared to existing approaches, achieving a lower Root Mean Squared Error (RMSE = 3.911) and Mean Absolute Error (MAE = 2.922), along with higher coefficients of determination (

R^{2}

= 0.953), Pearson correlation (0.917), and Spearman correlation (0.886). These results confirm its predictive efficiency and consistency across multiple synergy scoring models. Furthermore, the incorporation of attention mechanisms provides interpretability by highlighting significant features associated with drug resistance.

Future work will focus on incorporating additional cancer datasets, enhancing model predictive capabilities, and validating the approach in clinical settings to support personalized medicine. The findings suggest that HybridDeepSynergy has the potential to substantially improve treatment strategies for cancer and may be applicable to other disease contexts.

各种基于人工智能的方法已被用于分析复杂的基因组数据集。预测药物组合的协同作用是通过确定最有效的药物对来优化癌症治疗的关键一步。本研究提出了一种新型混合深度学习模型HybridDeepSynergy，该模型集成了卷积神经网络（cnn）、长短期记忆（LSTM）和变压器注意机制，用于预测不同药物组合和癌细胞系之间的药物协同作用。该模型旨在提高精准医疗和癌症治疗效果。HybridDeepSynergy利用cnn捕获局部特征交互，lstm建模顺序依赖关系，以及注意机制提取数据中的远程关系。该模型在包含多种药物组合的综合数据集上进行训练和评估，使用五种已建立的协同评分模型：Bliss Independence （Bliss）、Zero Interaction Potency （ZIP）、Loewe Additivity （Loewe）、Highest Single Agent （HSA）和General synergy (S)。与现有方法相比，我们的模型表现出更优越的性能，实现了更低的均方根误差（RMSE = 3.911）和平均绝对误差（MAE = 2.922），以及更高的决定系数（R2 = 0.953）， Pearson相关性（0.917）和Spearman相关性（0.886）。这些结果证实了它在多个协同评分模型之间的预测效率和一致性。此外，通过强调与耐药性相关的重要特征，注意机制的结合提供了可解释性。未来的工作将集中于整合更多的癌症数据集，增强模型预测能力，并在临床环境中验证该方法，以支持个性化医疗。研究结果表明，HybridDeepSynergy具有显著改善癌症治疗策略的潜力，并可能适用于其他疾病。

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引用次数: 0