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Oncogenic β-tubulin mutations disrupt nucleotide-dependent allostery and free energy landscape of tubulin dimer 致癌β-微管蛋白突变破坏了核苷酸依赖性变构和微管蛋白二聚体的自由能格局。
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.compbiomed.2026.111512
Thasni Fazil , Sharanya C. Suresh , Ravindar Lavoori , Kathiresan Natarajan
Dynamic instability of microtubules arises from nucleotide-dependent conformational changes within the tubulin dimers; however, little is known about the molecular mechanisms linking specific mutations to microtubule dysfunction. Here, we combined molecular-dynamics simulations with multi-parametric analysis to investigate wild-type and four lung cancer-associated β-tubulin mutations: Q134L, D177H, G269S, and Q426E. GTP-bound tubulin dimers exhibited enhanced flexibility in the H1–S2, T5, M-loop, and H7 regions, and strong correlated motions across longitudinal interfaces were observed consistent with an assembly-competent tubulin dimer conformation. Our analyses show that each mutation perturbs tubulin heterodimer stability through distinct mechanisms. Mutations such as Q134L and Q426E mutations loosened tubulin dimer inter-subunit packing and shifted the H7 helix toward open conformations, producing fragmented shallow free energy basins. D177H mutation preserved global stability but the tubulin dimer skewed toward a compact closed state. G269S mutation promoted tighter packing with heterogeneous conformers. These findings identify the core helix H7 as a central pivot linking nucleotide state, local perturbations, and global conformational equilibria. Principal component and free energy analyses reveal that these mutations shift the conformational equilibrium toward flexible, energetically unfavorable states incompatible with stable microtubule formation. Thus, our results provide atomistic insights into how these mutations remodel long-range allosteric communication within the tubulin dimer, offering a structural framework for comprehending the regulation of microtubule dynamics.
微管的动态不稳定性源于微管蛋白二聚体内核苷酸依赖的构象变化;然而,关于特异性突变与微管功能障碍之间的分子机制知之甚少。在这里,我们将分子动力学模拟与多参数分析相结合,研究了野生型和四种肺癌相关的β-微管蛋白突变:Q134L, D177H, G269S和Q426E。gtp结合的微管蛋白二聚体在H1-S2、T5、M-loop和H7区域表现出更强的灵活性,并且在纵向界面上观察到强烈的相关运动,与装配能力强的微管蛋白二聚体构象一致。我们的分析表明,每个突变通过不同的机制扰乱微管蛋白异源二聚体的稳定性。Q134L和Q426E等突变使微管蛋白二聚体亚基间堆积松散,使H7螺旋向开放构象移动,产生碎片状的浅层自由能盆地。D177H突变保持了整体稳定性,但微管蛋白二聚体倾向于紧凑的封闭状态。G269S突变促进异质构象更紧密的排列。这些发现确定核心螺旋H7是连接核苷酸状态、局部扰动和全局构象平衡的中心支点。主成分分析和自由能分析表明,这些突变将构象平衡转移到与稳定微管形成不相容的柔性、能量不利的状态。因此,我们的研究结果为这些突变如何重塑微管蛋白二聚体内的远程变构通讯提供了原子性的见解,为理解微管动力学的调节提供了结构框架。
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引用次数: 0
Enhancing survival analysis through federated learning in non-IID and scarce data scenarios 通过联邦学习在非iid和稀缺数据场景中增强生存分析。
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-03-01 Epub Date: 2026-02-19 DOI: 10.1016/j.compbiomed.2026.111558
Patricia A. Apellániz, Juan Parras, Santiago Zazo
Integrating Artificial Intelligence (AI) into Survival Analysis (SA) has advanced predictive modeling in healthcare, enabling precise and personalized predictions of time-to-event outcomes, such as patient survival. However, real-world SA datasets often suffer from data scarcity, heterogeneity, and privacy constraints, which limit the applicability of traditional and modern AI methods. To address these challenges, we propose the Federated Synthetic Data Sharing (FedSDS) framework, which integrates synthetic data generation with Federated Learning (FL). For SA, we leverage SAVAE, a state-of-the-art model for complex datasets. Using the Variational Autoencoder-Bayesian Gaussian Mixture model enhanced with artificial inductive bias, FedSDS generates high-quality synthetic data locally and shares them among nodes, enabling collaborative model training without direct data sharing. FedSDS introduces a biased aggregation strategy that aligns synthetic data with local distributions, outperforming traditional FL methods, such as Federated Average. Validated under independent and identically distributed (IID) and non-IID scenarios, FedSDS mitigates data imbalances and heterogeneity, showing significant performance improvements in scarce and heterogeneous data. The proposed framework offers a scalable and privacy-preserving solution for SA in decentralized environments. By enhancing model generalizability and robustness, FedSDS provides a promising path forward for collaborative analytics in healthcare, paving the way for improved patient outcomes and greater adoption of federated techniques in real-world applications.
将人工智能(AI)集成到生存分析(SA)中,可以在医疗保健领域实现先进的预测建模,实现对事件发生时间(如患者生存)结果的精确和个性化预测。然而,现实世界的人工智能数据集经常受到数据稀缺性、异质性和隐私约束的影响,这限制了传统和现代人工智能方法的适用性。为了应对这些挑战,我们提出了联邦合成数据共享(FedSDS)框架,该框架将合成数据生成与联邦学习(FL)集成在一起。对于SA,我们利用SAVAE,这是一种最先进的复杂数据集模型。FedSDS使用人工归纳偏置增强的变分自编码器-贝叶斯高斯混合模型,在本地生成高质量的合成数据并在节点之间共享,实现了无需直接共享数据的协同模型训练。FedSDS引入了一种有偏差的聚合策略,将合成数据与本地分布对齐,优于传统的FL方法,如Federated Average。在独立和同分布(IID)和非IID场景下验证,FedSDS减轻了数据不平衡和异构性,在稀缺和异构数据中显示出显着的性能改进。提出的框架为分散环境中的SA提供了可扩展和隐私保护的解决方案。通过增强模型的通用性和健壮性,FedSDS为医疗保健领域的协作分析提供了一条很有前途的道路,为改善患者治疗效果和在实际应用程序中更多地采用联合技术铺平了道路。
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引用次数: 0
Global trends, regional disparities and key determinants of neonatal sepsis: A pan-database analysis from 1990 to 2021 新生儿败血症的全球趋势、地区差异和关键决定因素:1990年至2021年的泛数据库分析
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-03-01 Epub Date: 2026-02-08 DOI: 10.1016/j.compbiomed.2026.111537
Zhenglin Chang , Wenhan Cao , Qianjun Li , Yuerong Chen , Youpeng Chen , Haiyang Li , Bingsen Chen , Zhiman Liang , Haojie Wu , Xiujin Han , Guohua Zeng , Zhangkai J. Cheng , Baoqing Sun

Objective

Neonatal sepsis (NS) poses a significant global health challenge, with mortality rates ranging from 11% to 19%. Despite its substantial burden, there is currently a lack of systematic understanding of the global epidemiological trends and influencing factors of NS.

Study design

We conducted a comprehensive pan-database analysis integrating data from 18 international databases across 201 countries (1990-2021). Through advanced statistical modeling, including correlation analyses, risk parsimonious modeling, and confounder adjustments, we examined temporal trends, regional disparities, and key determinants of NS.

Results

While NS prevalence increased annually due to improved detection, age-standardized rates showed consistent declines. For NS incidence, novel correlates included European ancestry (strongest), systolic/diastolic blood pressure, and inverse associations with Human Development Index. We developed a parsimonious model incorporating diastolic blood pressure, Global Hunger Index, and European ancestry, which showed strong cross-regional predictive capability (r = 0.727). For mortality, socioeconomic factors were primary correlates: positive associations with Global Hunger Index and food insecurity, and inverse associations with Inequality Adjusted HDI.

Conclusion

This first comprehensive global analysis reveals that NS outcomes are determined by both medical and socioeconomic factors. While blood pressure metrics and genetic factors influence incidence, mortality is primarily driven by socioeconomic determinants. These findings suggest that reducing NS burden requires a dual approach: enhancing medical care while addressing fundamental socioeconomic disparities, particularly in resource-limited regions.
新生儿败血症(NS)是全球健康面临的重大挑战,其死亡率从11%到19%不等。尽管其负担沉重,但目前对NS的全球流行病学趋势和影响因素缺乏系统的了解。研究设计:我们进行了全面的泛数据库分析,整合了来自201个国家的18个国际数据库(1990-2021)的数据。通过先进的统计模型,包括相关分析、风险简约模型和混杂因素调整,我们研究了时间趋势、区域差异和NS的关键决定因素。结果:虽然由于检测水平的提高,NS患病率逐年上升,但年龄标准化率呈持续下降趋势。对于NS发病率,新的相关因素包括欧洲血统(最强)、收缩压/舒张压,以及与人类发展指数呈负相关。我们建立了一个包含舒张压、全球饥饿指数和欧洲血统的简约模型,显示出很强的跨区域预测能力(r = 0.727)。对于死亡率,社会经济因素是主要相关因素:与全球饥饿指数和粮食不安全呈正相关,与不平等调整的人类发展指数呈负相关。结论:这是第一次全面的全球分析,揭示了NS结果是由医学和社会经济因素共同决定的。虽然血压指标和遗传因素影响发病率,但死亡率主要由社会经济决定因素驱动。这些研究结果表明,减少NS负担需要双重途径:加强医疗保健,同时解决基本的社会经济差距,特别是在资源有限的地区。
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引用次数: 0
Hentriacontane alleviates streptozotocin-induced Alzheimer's disease-like conditions in rats: In silico and in vivo investigations revealed the unifying principles 亨三康烷减轻大鼠链脲佐菌素诱导的阿尔茨海默病样疾病:计算机和体内研究揭示了统一的原则。
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-03-01 Epub Date: 2026-02-09 DOI: 10.1016/j.compbiomed.2026.111513
Sagar A. More, Awez Sikkalgar, Nayna Chourasiya, Yogeeta O. Agrawal, Sameer N. Goyal, Kartik T. Nakhate, Mohd Usman Mohd Siddique, Sumit S. Rathod
Intracerebroventricular (ICV) streptozotocin (STZ) deveops Alzheimer's disease (AD)-like conditions in rodents, which are characterized by insulin resistance, tau pathology, and neurodegeneration. Hentriacontane, a natural compound found in various sources, including beeswax, possesses anti-inflammatory and antioxidant properties. In the present investigation, we performed in silico molecular docking, molecular dynamics, MMGBSA, PCA, and FEL analysis of hentriacontane and rivastigmine with acetylcholinesterase (AchE). Further, we assessed the in vivo neuroprotective effects of hentriacontane in an ICV-STZ-induced AD-like condition in rats. STZ (3 mg/kg/ICV) was injected into male Sprague-Dawley rats. Cognitive functions were evaluated by Barnes-Maze (BM), novel object recognition test (NORT), and passive avoidance test (PAT). Hentriacontane (3 and 5 mg/kg) and rivastigmine (1 mg/kg) were given intraperitoneally for 14 days. Brain-derived neurotrophic factor (BDNF), AchE, oxidative stress parameters including GSH, MDA, SOD, and CAT, and proinflammatory cytokines including IL-6, TNF-α, IL-1β, and NF-ҡB were measured via ELISA. Further, we have also estimated the BACE1 and NO levels. Histopathological evaluation was conducted using hematoxylin and eosin staining. In silico molecular docking, dynamics, and post-dynamics data revealed promising binding affinities of hentriacontane for AchE. Further, hentriacontane attenuated ICV-STZ-induced cognitive deficit in BM, NORT, and PAT. Additionally, altered oxidative stress, proinflammatory, and cell signalling parameters were restored. Histopathology revealed that the hentriacontane-treated group showed significant restoration of the small pyramidal cells in the CA1 and CA2 regions of the brain. Hentriacontane demonstrated neuroprotective effects by modulation of AchE, leading to improved cognitive functions as evidenced by in silico and in vivo investigations.
脑室内(ICV)链脲佐菌素(STZ)在啮齿动物中发展为阿尔茨海默病(AD)样疾病,其特征是胰岛素抵抗、tau病理和神经变性。亨三康烷是一种天然化合物,存在于各种来源,包括蜂蜡中,具有抗炎和抗氧化特性。在本研究中,我们用乙酰胆碱酯酶(AchE)对hentriacontane和rivastigming进行了硅分子对接、分子动力学、MMGBSA、PCA和FEL分析。此外,我们评估了亨三康烷对icv - stz诱导的ad样大鼠的体内神经保护作用。雄性sd大鼠注射STZ (3 mg/kg/ICV)。采用Barnes-Maze (BM)、新目标识别测试(NORT)和被动回避测试(PAT)评估认知功能。Hentriacontane(3和5 mg/kg)和rivastigming (1 mg/kg)腹腔注射14 d。ELISA法检测脑源性神经营养因子(BDNF)、乙酰胆碱酯酶(AchE)、氧化应激参数GSH、MDA、SOD、CAT和促炎因子IL-6、TNF-α、IL-1β、NF-ҡB。此外,我们还估计了BACE1和NO水平。采用苏木精和伊红染色进行组织病理学评价。硅分子对接、动力学和后动力学数据显示,三康烷对乙酰胆碱具有良好的结合亲和力。此外,hentriacontane减轻了icv - stz诱导的BM, NORT和PAT的认知缺陷。此外,氧化应激、促炎和细胞信号参数的改变也得以恢复。组织病理学显示,hentriacontan处理组显示出大脑CA1和CA2区域的小锥体细胞的显著恢复。Hentriacontane通过调节AchE显示出神经保护作用,导致认知功能的改善,这在计算机和体内研究中得到了证明。
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引用次数: 0
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 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。
{"title":"Multi-structure CT radiomics-based consensus model for the diagnosis of pancreatic ductal adenocarcinoma and vascular involvement","authors":"Jia Peng ,&nbsp;Shiyao Xie ,&nbsp;Xinnan Liao ,&nbsp;Mengnan Tai ,&nbsp;Zixuan Nie ,&nbsp;Yaoqi Wang ,&nbsp;Zhiyuan Chen ,&nbsp;Zheng Wang ,&nbsp;Ya Peng","doi":"10.1016/j.compbiomed.2026.111542","DOIUrl":"10.1016/j.compbiomed.2026.111542","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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).</div></div><div><h3>Results</h3><div>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 &lt; 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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"204 ","pages":"Article 111542"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146156501","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}
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 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 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 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 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 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
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Computers in biology and medicine
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