Computers in biology and medicine最新文献_第2页

Periodontal bone loss analysis via keypoint detection with heuristic post-processing 启发式后处理关键点检测牙周骨质流失分析。

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.2026.111515

Ryan Banks , Vishal Thengane , María Eugenia Guerrero , Nelly Maria García-Madueño , Yunpeng Li , Hongying Tang , Akhilanand Chaurasia

Objectives: This study proposes a deep learning framework and an annotation methodology for the automatic detection of periodontal bone loss landmarks, associated conditions, and staging.

Methods

192

periapical radiographs were collected and annotated using a stage agnostic methodology, labelling clinically relevant landmarks regardless of disease presence or extent. We propose a heuristic post-processing module that aligns predicted keypoints to tooth boundaries using an auxiliary instance segmentation model. An evaluation metric, Percentage of Relative Correct Keypoints (

P R C K

), is proposed to capture keypoint performance in dental imaging domains. Four donor pose estimation models were adapted with fine-tuning for our keypoint problem.

Results Post-processing improved fine-grained localisation, raising average

P R C K^{0.05}

by

+ 0.028

, but reduced coarse performance for

P R C K^{0.25}

by

- 0.0523

and

P R C K^{0.5}

by

- 0.0345

. Orientation estimation shows excellent performance for auxiliary segmentation when filtered with either stage 1 object detection model. Periodontal staging was detected sufficiently, with the best mesial and distal Dice scores of

0.508

and

0.489

, while furcation involvement and widened periodontal ligament space tasks remained challenging due to scarce positive samples. Scalability is implied with similar validation and external set performance.

Conclusion The annotation methodology enables stage agnostic training with balanced representation across disease severities for some detection tasks. The

P R C K

metric provides a domain-specific alternative to generic pose metrics, while the heuristic post-processing module consistently corrected implausible predictions with occasional catastrophic failures.

Clinical significance: The proposed framework demonstrates the feasibility of clinically interpretable periodontal bone loss assessment, with the potential to reduce diagnostic variability and clinician workload.

目的：本研究提出了一种深度学习框架和注释方法，用于自动检测牙周骨质流失标志、相关条件和分期。方法收集192张根尖周围x线片，采用分期不确定的方法进行注释，标记临床相关标志，无论疾病是否存在或程度如何。我们提出了一个启发式的后处理模块，该模块使用辅助的实例分割模型将预测的关键点对齐到牙齿边界。提出了一种评估指标，相对正确关键点百分比（PRCK），用于捕获牙科成像领域的关键点性能。针对关键点问题，采用四种供体姿态估计模型进行微调。结果后处理改善了细粒度定位，使PRCK0.05平均提高了+0.028，但使PRCK0.25和PRCK0.5的粗粒度定位性能分别降低了-0.0523和-0.0345。当用第一阶段目标检测模型进行滤波时，方向估计在辅助分割方面表现出优异的性能。牙周分期检测充分，近端和远端Dice得分最高，分别为0.508和0.489，但由于阳性样本较少，分叉受累和牙周韧带间隙扩大的任务仍然具有挑战性。可伸缩性隐含在类似的验证和外部集性能中。结论该标注方法能够在某些检测任务中实现跨疾病严重程度均衡表征的阶段不可知论训练。PRCK度量为通用姿态度量提供了特定领域的替代方案，而启发式后处理模块始终如一地纠正了偶尔发生灾难性故障的不可信预测。临床意义：提出的框架证明了临床可解释的牙周骨质流失评估的可行性，具有减少诊断变异性和临床医生工作量的潜力。

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引用次数: 0

IUPAC-induced computational approaches for identifying boosters of small biomolecule functionality: A case study of human tyrosyl-DNA phosphodiesterase 1 (TDP1) inhibitors iupac诱导的用于识别小生物分子功能增强剂的计算方法：人类酪氨酸- dna磷酸二酯酶1 （TDP1）抑制剂的案例研究。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.compbiomed.2026.111531

Mariya L. Ivanova , Nicola Russo , Gueorgui Mihaylov , Konstantin Nikolic

This paper introduces several proof-of-concept (PoC) computational methods intended to offer biochemical researchers straightforward, time- and cost-effective strategies to accelerate their work. While Machine Learning (ML) models were developed, the study's central purpose was to explore approaches for the identification of desirable functional groups/fragments in small biomolecules regarding a specific functionality, which, in this case, was human tyrosyl-DNA phosphodiesterase 1 (TDP1) inhibition. This was achieved primarily by tokenising IUPAC names to generate features. Additionally, the applicability of the CID_SID ML model for predicting TDP1 activity was developed and explored. Since these computational approaches were not experimentally validated due to a lack of appropriate laboratory facilities, they are presented as open proposals for further laboratory investigation.

本文介绍了几种概念验证（PoC）计算方法，旨在为生化研究人员提供直接，时间和成本效益的策略来加速他们的工作。在开发机器学习（ML）模型的同时，该研究的中心目的是探索识别小生物分子中特定功能所需的官能团/片段的方法，在这种情况下，是人类酪氨酸- dna磷酸二酯酶1 （TDP1）抑制。这主要是通过标记IUPAC名称来生成特征来实现的。此外，开发并探索了CID_SID ML模型预测TDP1活性的适用性。由于缺乏适当的实验室设施，这些计算方法没有得到实验验证，因此它们作为进一步实验室研究的公开建议提出。

引用次数: 0

Clinical pathways discovery for long-term and chronic patients: A process mining approach 长期和慢性患者的临床路径发现：过程挖掘方法。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1016/j.compbiomed.2026.111539

Luca Murazzano , Paolo Landa , Jean–Baptiste Gartner , Mohamed Hakim Raki , André Côté

The increasing burden of chronic respiratory diseases is placing substantial pressure on healthcare systems around the world. Lung diseases, such as chronic obstructive pulmonary disease, pneumonia, lung cancer, and pulmonary fibrosis, rank among the most prevalent and deadly conditions globally. Given the complexity of managing these chronic conditions, there is an urgent need to optimize care processes to meet the growing service demands efficiently and effectively, especially in public healthcare systems where there is a prevalence of elderly patients. This study aims to understand and improve the quality and performance of treatments provided to patients using a process mining approach. By analyzing clinical data collected from 2018 to 2022, the study identifies critical points in the care pathways where improvements can be made. This approach enables the optimization of resource deployment and service configuration to better meet patient needs. As a case study, this method was applied to a specialized hospital facility dedicated to cardiac and respiratory diseases, where actionable insights were uncovered for enhancing clinical pathways. This study allows us to analyze clinical pathways and detect critical points, providing insights to healthcare managers and decision makers. In addition, it highlights the importance of adequate data collection and suggests that future research efforts should prioritize the acquisition of larger and more diverse data sets to enhance the reliability and validity of activity and episodes analyses.

慢性呼吸道疾病的负担日益加重，给世界各地的卫生保健系统带来了巨大压力。慢性阻塞性肺病、肺炎、肺癌和肺纤维化等肺病是全球最普遍和最致命的疾病之一。鉴于管理这些慢性疾病的复杂性，迫切需要优化护理流程，以满足日益增长的服务需求，特别是在老年患者普遍存在的公共医疗保健系统中。本研究旨在了解和提高治疗的质量和性能提供给患者使用过程挖掘方法。通过分析2018年至2022年收集的临床数据，该研究确定了可以进行改进的护理路径中的关键点。这种方法可以优化资源部署和服务配置，以更好地满足患者的需求。作为一个案例研究，该方法被应用于专门用于心脏和呼吸系统疾病的专业医院设施，在那里发现了可操作的见解，以加强临床途径。这项研究使我们能够分析临床路径并检测关键点，为医疗保健管理人员和决策者提供见解。此外，它强调了充分收集数据的重要性，并建议未来的研究工作应优先考虑获取更大、更多样化的数据集，以提高活动和事件分析的可靠性和有效性。

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引用次数: 0

Corrigendum to “Regional attention-enhanced vision transformer for accurate Alzheimer's disease classification using sMRI data” [Comput. Biol. Med. 197 (2025) 111065] “使用sMRI数据精确分类阿尔茨海默病的区域注意力增强视觉转换器”的勘误表[Comput]。医学杂志。医学。197(2025)111065]。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1016/j.compbiomed.2026.111559

Alireza Jomeiri , Ahmad Habibizad Navin , Mahboubeh Shamsi

引用次数: 0

Unobtrusive sleep posture estimation using pressure sensor in home sleep 在家庭睡眠中使用压力传感器进行不显眼的睡眠姿势估计

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-12 DOI: 10.1016/j.compbiomed.2026.111551

Jonghyun Hong , Jungmin Koh , Jinyoung Kim , Hyunchan Ryu , Dahye Lee , Hyun Bin Kwon , Byunghun Choi , Heesu Park , Kwang Suk Park , Heenam Yoon

Purpose

Sleep posture is associated with various physiological indicators and significantly influences sleep health and quality. Although several methods for posture estimation have been proposed, most have been evaluated using data from controlled laboratory environments. This study proposes a method for determining sleep posture in real-world settings using pressure sensor data.

Methods

The approach was developed based on data collected from 22 participants in a laboratory setting using a 7 × 14 array of force-sensitive resistors (FSR). We employed a support vector machine to classify four sleep postures—supine, left-lateral, right-lateral, and prone—based on six extracted features related to area, curvature, and row length ratio. The algorithm was subsequently evaluated using FSR data recorded from ten participants sleeping freely in their home environments.

Results

The performance results demonstrated an accuracy of 78.1% and a Cohen's kappa of 0.71 for the laboratory data. When applied to the home-environment data, the method achieved an accuracy of 86.1% and a Cohen's kappa of 0.76 for the classification of the four sleep postures.

Conclusion

These findings indicate that the model trained in a laboratory setting maintained high performance in real-world conditions, supporting the feasibility of implementing sleep monitoring technologies in daily life and clinical contexts. This study contributes to the development of noninvasive, long-term sleep monitoring systems and highlights the potential for future clinical applications in embedded systems and hospital environments through the use of feature-based models with high explainability.

目的睡眠姿势与多种生理指标相关，对睡眠健康和质量有重要影响。虽然已经提出了几种姿态估计方法，但大多数方法都是使用受控实验室环境的数据进行评估的。本研究提出了一种利用压力传感器数据在现实环境中确定睡眠姿势的方法。方法采用7 × 14力敏电阻器阵列（FSR），在实验室环境中收集了22名参与者的数据。基于提取的6个与面积、曲率和行长比相关的特征，我们使用支持向量机对仰卧、左侧卧、右侧卧和俯卧4种睡眠姿势进行分类。随后，该算法使用10名参与者在家庭环境中自由睡眠时记录的FSR数据进行评估。结果实验数据的准确率为78.1%,Cohen’s kappa为0.71。当应用于家庭环境数据时，该方法对四种睡眠姿势的分类准确率为86.1%，科恩kappa为0.76。这些发现表明，在实验室环境中训练的模型在现实环境中保持了较高的性能，支持了在日常生活和临床环境中实施睡眠监测技术的可行性。这项研究促进了无创、长期睡眠监测系统的发展，并强调了通过使用具有高度可解释性的基于特征的模型，在嵌入式系统和医院环境中未来临床应用的潜力。

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引用次数: 0

External validation of GDM risk prediction models using a machine learning reciprocal model-exchange framework 使用机器学习互惠模型交换框架的GDM风险预测模型的外部验证

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-11 DOI: 10.1016/j.compbiomed.2026.111547

Mark Germaine , Yitayeh Belsti , Amy O'Higgins , Brendan Egan , Helena Teede , Graham Healy , Joanne Enticott

Background

Although many risk prediction models have been developed, very few undergo external validation, primarily due to issues with data access. Therefore, we implemented a reciprocal model-exchange approach to facilitate external validation and demonstrate its use with gestational diabetes mellitus (GDM) prediction models.

Objective

To assess the robustness and generalisability of two independently developed GDM risk prediction models using a reciprocal model-exchange framework.

Methods

Two independently developed GDM risk prediction models were externally validated using a reciprocal model-exchange. The saved model's corresponding variable types and data pre-processor were exchanged. The Monash CatBoost model was validated using Irish data at Dublin City University (DCU), and the DCU logistic-regression GDM model was validated using Australian data at Monash University. Performance was assessed using discrimination, calibration and decision curve analysis. Model fairness was assessed.

Results

The prevalence of GDM was 21.1% in the Australian cohort and 11.7% in the Irish cohort. The Monash model's AUC dropped from 0.93 to 0.77, while the DCU model's AUC fell from 0.82 to 0.69. Calibration estimates confirmed systematic risk misestimation; each model tends to over or under-predict GDM probabilities outside its training domain, with calibration-in-the-large of −0.573 for the Monash model and 0.17 for the DCU model; slopes were 1.278 and 0.55 respectively. Both models showed performance variability across ethnic groups, with lower performance for Southeast/Northeast Asians and both performed better with increasing parity and among women without a prior GDM diagnosis.

Conclusions

Each model's performance decreased upon external validation, and the fairness evaluations on the different sub-categories (ethnicities; parity and previous GDM) provided evidence on the areas to be addressed in model recalibration/updating before deployment can be progressed. This reciprocal model-exchange approach provides a solution to facilitating external validations, which are notably lacking in the current literature but are necessary to advance the risk prediction field.

虽然已经开发了许多风险预测模型，但很少经过外部验证，主要是由于数据访问问题。因此，我们实施了一种互惠模型交换方法来促进外部验证，并证明其在妊娠糖尿病（GDM）预测模型中的应用。目的利用模型交换框架评价两种独立开发的GDM风险预测模型的稳健性和通用性。方法采用模型互换对两个独立开发的GDM风险预测模型进行外部验证。交换保存的模型对应的变量类型和数据预处理。莫纳什CatBoost模型使用都柏林城市大学（DCU）的爱尔兰数据进行了验证，DCU的logistic回归GDM模型使用莫纳什大学的澳大利亚数据进行了验证。使用鉴别、校准和决策曲线分析对性能进行评估。评估模型的公平性。结果GDM的患病率在澳大利亚队列为21.1%，在爱尔兰队列为11.7%。莫纳什模型的AUC从0.93下降到0.77，而DCU模型的AUC从0.82下降到0.69。校正估计确认系统风险误估；每个模型都倾向于高估或低估其训练域外的GDM概率，Monash模型的校准值为- 0.573，DCU模型的校准值为0.17；斜率分别为1.278和0.55。这两种模型都显示了不同种族的表现差异，东南亚/东北亚人的表现较低，随着性别的增加和没有先前诊断过GDM的女性的表现更好。结论在外部验证时，每个模型的性能都有所下降，不同子类别（种族、平价和以前的GDM）的公平性评估为模型重新校准/更新提供了证据，然后才能进行部署。这种相互的模型交换方法提供了一种促进外部验证的解决方案，这在当前文献中是明显缺乏的，但对于推进风险预测领域是必要的。

{"title":"External validation of GDM risk prediction models using a machine learning reciprocal model-exchange framework","authors":"Mark Germaine , Yitayeh Belsti , Amy O'Higgins , Brendan Egan , Helena Teede , Graham Healy , Joanne Enticott","doi":"10.1016/j.compbiomed.2026.111547","DOIUrl":"10.1016/j.compbiomed.2026.111547","url":null,"abstract":"<div><h3>Background</h3><div>Although many risk prediction models have been developed, very few undergo external validation, primarily due to issues with data access. Therefore, we implemented a reciprocal model-exchange approach to facilitate external validation and demonstrate its use with gestational diabetes mellitus (GDM) prediction models.</div></div><div><h3>Objective</h3><div>To assess the robustness and generalisability of two independently developed GDM risk prediction models using a reciprocal model-exchange framework.</div></div><div><h3>Methods</h3><div>Two independently developed GDM risk prediction models were externally validated using a reciprocal model-exchange. The saved model's corresponding variable types and data pre-processor were exchanged. The Monash CatBoost model was validated using Irish data at Dublin City University (DCU), and the DCU logistic-regression GDM model was validated using Australian data at Monash University. Performance was assessed using discrimination, calibration and decision curve analysis. Model fairness was assessed.</div></div><div><h3>Results</h3><div>The prevalence of GDM was 21.1% in the Australian cohort and 11.7% in the Irish cohort. The Monash model's AUC dropped from 0.93 to 0.77, while the DCU model's AUC fell from 0.82 to 0.69. Calibration estimates confirmed systematic risk misestimation; each model tends to over or under-predict GDM probabilities outside its training domain, with calibration-in-the-large of −0.573 for the Monash model and 0.17 for the DCU model; slopes were 1.278 and 0.55 respectively. Both models showed performance variability across ethnic groups, with lower performance for Southeast/Northeast Asians and both performed better with increasing parity and among women without a prior GDM diagnosis.</div></div><div><h3>Conclusions</h3><div>Each model's performance decreased upon external validation, and the fairness evaluations on the different sub-categories (ethnicities; parity and previous GDM) provided evidence on the areas to be addressed in model recalibration/updating before deployment can be progressed. This reciprocal model-exchange approach provides a solution to facilitating external validations, which are notably lacking in the current literature but are necessary to advance the risk prediction field.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"204 ","pages":"Article 111547"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171747","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

Analysis of EEG univariate features for epileptic seizures 癫痫发作的脑电图单变量特征分析。

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.2026.111517

Sergio E. Sánchez-Hernández , Ricardo A. Salido-Ruiz , Stewart R. Santos-Arce , Radu Ranta

The diagnosis of epilepsy and the identification of seizures are subject to multiple challenges. Hence, it is relevant to identify EEG features that allow for differentiation between seizure intervals and between patients and healthy subjects. Several studies have explored the search for biomarkers by utilizing signal processing techniques, although some of these studies have been applied to limited datasets. In this study, a set of seven univariate features in the time and frequency domains was calculated for scalp EEG recordings. These recordings were collected from four EEG datasets (patients=180, controls=100, seizures=613), and two types of experiments were performed: a comparison between healthy subjects and the pre-ictal (one min before seizure onset) and ictal intervals, and a comparison between seizure stages (pre-ictal, ictal and post-ictal). Variations in the normalized power spectral density were the most reliable indicator of seizure activity (

p - v a l u e < 0.05

). Mobility, complexity, and approximate entropy also changed significantly, with entropy-based measurements decreasing during seizures, indicating a reduction in EEG irregularity (

p - v a l u e < 0.05

). The results highlighted the importance of combining spectral, statistical, and entropy-based features for a more comprehensive understanding of seizures. Although some common patterns were identified, distinct behaviors were observed between datasets. Future work will benefit from a diverse and curated dataset; therefore, causes of dissimilarities can be unequivocally identified.

癫痫的诊断和癫痫发作的识别受到多重挑战。因此，识别脑电图特征以区分癫痫发作间隔和患者与健康受试者之间是相关的。一些研究已经探索了利用信号处理技术寻找生物标志物，尽管其中一些研究已应用于有限的数据集。在这项研究中，计算了头皮EEG记录的时间和频率域的7个单变量特征。这些记录来自4个脑电图数据集（患者=180，对照组=100，癫痫发作=613），并进行了两种类型的实验：健康受试者与癫痫发作前（癫痫发作前1分钟）和癫痫发作间隔的比较，以及癫痫发作阶段（癫痫发作前，癫痫发作和癫痫发作后）的比较。归一化功率谱密度的变化是癫痫发作活动（p值）最可靠的指标

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引用次数: 0

A semi-automated modelling pipeline to predict the mechanics of multiple sclerosis lesion afflicted brains from magnetic resonance images 半自动化建模管道，预测多发性硬化症病变的机制，从磁共振图像折磨大脑。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.compbiomed.2026.111519

Adam C Szekely-Kohn , Diana Cruz De Oliveira , Marco Castellani , Michael Douglas , Zubair Ahmed , Daniel M Espino

Multiple Sclerosis (MS) is a demyelinating and degenerative autoimmune disease that affects the brain and spinal cord. Its causes, mechanisms, and outcomes are yet to be fully understood. One relatively unexplored area is the understanding of changes in brain biomechanics during MS disease progression, despite the likelihood that demyelination significantly alters the overall mechanical structure of the brain. Such changes have the potential to hinder the propagation of nerve signals essential for cognition and motor function. The aim of this work was to create a computational model to explore the mechanics of brains with MS, separating the brain into grey matter, white matter and lesions. Changes were observed when the surface of the brain was subjected to a ramped uniform pressure tangential to the faces of a finite element model, generated from patient- and time-specific MRI scans. The resulting displacements, stresses and strains can all be gauged using the model. The key benefit of this study was to observe the impact of changes in tissue morphology in real brains using non-invasive methods. Ensuring the accuracy of the axiomatic input tissue parameters of the models was critically important, as exploring the range of values from literature, adjusted by their error margins, revealed a significant variability in outcomes, especially in the case of volumetric strain of lesions. The model has the potential to track changes in mechanical tissue properties assuming the availability of a longitudinal dataset, and if further developed, has the potential to serve as the foundation for creating a digital twin. This could enhance medical practice and provide a non-invasive approach to advancing the understanding of MS and its progression on a patient-specific basis.

多发性硬化症（MS）是一种影响大脑和脊髓的脱髓鞘和退行性自身免疫性疾病。其原因、机制和结果尚不完全清楚。尽管脱髓鞘可能会显著改变大脑的整体力学结构，但一个相对未开发的领域是对MS疾病进展过程中大脑生物力学变化的理解。这种变化有可能阻碍对认知和运动功能至关重要的神经信号的传播。这项工作的目的是创建一个计算模型，用MS来探索大脑的机制，将大脑分为灰质、白质和病变。当大脑表面受到与有限元素模型切向的倾斜均匀压力时，观察到变化，这是由患者和特定时间的MRI扫描产生的。由此产生的位移、应力和应变都可以使用该模型进行测量。这项研究的主要好处是使用非侵入性方法观察真实大脑组织形态变化的影响。确保模型的公理输入组织参数的准确性是至关重要的，因为从文献中探索值的范围，通过其误差范围进行调整，揭示了结果的显著可变性，特别是在病变体积应变的情况下。假设纵向数据集的可用性，该模型有可能跟踪机械组织特性的变化，如果进一步开发，有可能作为创建数字双胞胎的基础。这可以加强医疗实践，并提供一种非侵入性的方法来促进对MS及其在患者特异性基础上的进展的理解。

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引用次数: 0

Translational perspectives on brain-heart interplay: From methodologies to clinical applications 脑-心相互作用的翻译视角：从方法论到临床应用。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.compbiomed.2026.111522

Matteo Saibene , Ying Gu , Martin Ballegaard , Tobias Søren Andersen , Jakob Eyvind Bardram , Sadasivan Puthusserypady

The nervous and cardiovascular systems are intricately interconnected in both healthy and diseased conditions. The field of Neurocardiology, which focuses on the complex interaction between the nervous and cardiovascular systems, has grown rapidly over the past years. In addition, growing evidence shows that alterations in Brain-Heart Interplay (BHI) may contribute to neurological and cardiovascular disorders. BHI has great potential as a valuable biomarker to detect autonomic dysfunction, and reveal the mechanisms underlying conditions including sleep-related autonomic disorders and neurodegenerative diseases. However, the physiological characterization of linear or non-linear brain-heart relationships remains unclear. This review presents a comprehensive analysis of the current methods used to characterize BHI across multiple domains, with an emphasis on Electroencephalogram (EEG), Electrocardiogram (ECG), or Photoplethysmogram (PPG). Such non-invasive modalities allow for long-term and time-varying assessment of cortical and autonomic activity. In this work, we review how BHI modulates across various physiological and pathological states and highlight key findings from recent studies. In addition, we review potential data-driven methods to examine complex BHI, including measures of synchronization, directionality, and information transfer. As a result, studying BHI using these strategies offers a new angle on the complex and dynamic interaction of cortical and autonomic processes. This provides an opportunity to understand the intricate interplay between brain and heart functions, and has the potential to advance diagnosis, monitoring, and therapeutic interventions in neurocardiac conditions.

在健康和患病的情况下，神经和心血管系统都是错综复杂地相互联系的。神经心脏病学研究的重点是神经系统和心血管系统之间复杂的相互作用，近年来发展迅速。此外，越来越多的证据表明，脑心相互作用（BHI）的改变可能导致神经和心血管疾病。BHI作为一种有价值的生物标志物，在检测自主神经功能障碍、揭示睡眠相关自主神经障碍和神经退行性疾病的潜在机制方面具有很大的潜力。然而，线性或非线性脑-心关系的生理特征仍不清楚。本文综述了目前用于跨多个领域表征BHI的方法的综合分析，重点是脑电图（EEG），心电图（ECG）或光容积图（PPG）。这种非侵入性模式允许对皮层和自主神经活动进行长期和时变的评估。在这项工作中，我们回顾了BHI如何调节各种生理和病理状态，并强调了最近研究的关键发现。此外，我们回顾了潜在的数据驱动方法来检查复杂的BHI，包括同步、方向性和信息传递的措施。因此，利用这些策略研究BHI提供了一个新的角度来研究皮层和自主神经过程的复杂和动态的相互作用。这为了解大脑和心脏功能之间复杂的相互作用提供了机会，并有可能推进神经心脏疾病的诊断、监测和治疗干预。

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引用次数: 0

Overcoming structural complexity in Galectin-3BP through an integrative computational antibody design workflow 通过集成计算抗体设计工作流克服半乳糖凝集素- 3bp的结构复杂性

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-13 DOI: 10.1016/j.compbiomed.2026.111550

Andrielly H.S. Costa , Eduardo M. Gaieta , Aline O. Albuquerque , Julia S. Souza , Diego S. Almeida , Jean V. Sampaio , Patrick England , Geraldo R. Sartori , João H.M. Silva

Galectin-3 binding protein (Gal-3BP) is a clinically relevant oncology target, with overexpression associated with poor prognosis across multiple tumor types. However, its therapeutic exploration has been hindered by extensive glycosylation, conformational heterogeneity, and context-dependent oligomerization, which restrict epitope accessibility. Antibody-based strategies remain promising for targeting such complex proteins, yet their development is costly and experimentally demanding. To address these challenges, we established an integrative in-silico workflow tailored to the specific structural and biophysical features of Gal-3BP combining validated methodologies of structural prediction, molecular dynamics (MD) simulations, and antibody engineering. By mapping Gal-3BP across oligomeric states and characterizing its N-glycan conformational diversity, we identified two glycan-free epitopes within the BACK domain, termed E1 and E2. Scaffold selection using 3D Zernike descriptors–based similarity search identified BDBV-43 as a compatible candidate for E1. For E2, which lacked similarity-based matches, naïve repertoire mining retrieved the unmatured antibody E2-Ab1, broadening the set of viable templates. Engineering approaches included point mutations in BDBV-43 and full CDR swapping in E2-Ab1. Iterative refinement yielded variants with improved interaction profiles and robust stability during heated MD simulations. Furthermore, Gaussian accelerated MD (GaMD) revealed reorganized conformational landscapes together with modest shifts in the underlying free-energy profiles for the engineered antibodies relative to their native scaffolds, in line with the interpretative limits of GaMD reweighting. Collectively, this study positions Gal-3BP as a tractable therapeutic target and presents optimized antibody candidates capable of engaging epitopes minimally affected by glycan shielding, illustrating the potential of integrative computational pipelines for antibody design against structurally complex proteins.

半乳糖凝集素-3结合蛋白（Galectin-3 binding protein, Gal-3BP）是临床相关的肿瘤靶点，在多种肿瘤类型中过表达与预后不良相关。然而，其治疗探索受到广泛的糖基化，构象异质性和上下文依赖性寡聚化的阻碍，这些限制了表位的可及性。以抗体为基础的策略仍然有希望靶向这种复杂的蛋白质，但它们的开发成本高，实验要求高。为了应对这些挑战，我们根据Gal-3BP的特定结构和生物物理特征，结合结构预测、分子动力学（MD）模拟和抗体工程的验证方法，建立了一个集成的硅芯片工作流程。通过绘制Gal-3BP的低聚态图谱并表征其n-聚糖构象多样性，我们在BACK结构域中鉴定了两个无聚糖表位，称为E1和E2。使用基于3D Zernike描述符的相似性搜索进行支架选择，确定BDBV-43为E1的兼容候选。对于缺乏基于相似性匹配的E2， naïve库挖掘检索到未成熟的抗体E2- ab1，扩大了可行模板集。工程方法包括BDBV-43的点突变和E2-Ab1的全CDR交换。在加热MD模拟过程中，迭代改进产生了具有改进的相互作用剖面和鲁棒稳定性的变体。此外，高斯加速MD （GaMD）揭示了重组的构象景观，以及工程抗体相对于其天然支架的潜在自由能谱的适度变化，符合GaMD重加权的解释限制。总的来说，这项研究将Gal-3BP定位为一个易于处理的治疗靶点，并提出了优化的抗体候选物，能够最小程度地参与受聚糖屏蔽影响的表位，说明了针对结构复杂蛋白的抗体设计的综合计算管道的潜力。

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