Computers in biology and medicine最新文献_第6页

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

MS-CoTF: Multi-scale chain-of-thought fusion for interpretable biological reasoning with large language models MS-CoTF：基于大型语言模型的可解释生物推理的多尺度思维链融合。

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.2026.111467

Zeyuan Song , Xiao-Cong Zhen

Large language models (LLMs) have demonstrated impressive proficiency in various science and engineering applications. However, due to the innate multi-scale property of biological systems, existing LLMs face severe limitations in capturing hierarchical relationships and context-dependent interactions across molecular, cellular, tissue, and systemic levels. These models often lack the architectural mechanisms needed to reason effectively across different biological scales, resulting in reduced accuracy and limited interpretability when applied to complex tasks. Here, we introduce a novel framework named multi-scale chain-of-thought fusion (MS-CoTF), which fuses reasoning at molecular, cellular, tissue, and system scales to enhance accuracy and interpretability when solving biological tasks. Through adaptive reasoning depth control, multi-scale integration, bi-directional flow and dynamic fusion strategies, our MS-CoTF model effectively processes queries of varying complexity, enabling scalable and interpretable reasoning across multiple biological levels. Ablation studies demonstrate that these components function synergistically to enhance model accuracy while simultaneously providing biologically meaningful insights. Furthermore, our MS-CoTF model consistently outperforms state-of-the-art reasoning models by 10–15% across three benchmark problems and two case studies in terms of accuracy, expert ratings, and the capacity to produce reasonable inference chains. Technically, MS-CoTF orchestrates a frozen biomedical LLM backbone with trainable cross-scale modules, employing a precise definition of per-step chain-of-thought (CoT) construction and linking. To ensure rigorous evaluation, we implement an explicit dataset splitting protocol (entity-disjoint and temporal) and utilize the Reasoning Coherence Score strictly as a post-hoc metric to ensure fair comparisons. We further validate the framework through extended baselines, including structure-conditioned and multimodal biomedical LLMs, alongside detailed human evaluation protocols and hallucination stress tests.

大型语言模型（llm）在各种科学和工程应用中表现出令人印象深刻的熟练程度。然而，由于生物系统固有的多尺度特性，现有的llm在捕获跨分子、细胞、组织和系统水平的层次关系和上下文依赖的相互作用方面面临严重的限制。这些模型通常缺乏跨不同生物尺度进行有效推理所需的体系结构机制，导致在应用于复杂任务时准确性降低，可解释性有限。在这里，我们介绍了一个名为多尺度思维链融合（MS-CoTF）的新框架，它融合了分子、细胞、组织和系统尺度的推理，以提高解决生物任务时的准确性和可解释性。通过自适应推理深度控制、多尺度集成、双向流和动态融合策略，我们的MS-CoTF模型有效地处理不同复杂性的查询，实现跨多个生物水平的可扩展和可解释推理。消融研究表明，这些成分协同作用，提高了模型的准确性，同时提供了生物学上有意义的见解。此外，我们的MS-CoTF模型在三个基准问题和两个案例研究中，在准确性、专家评级和产生合理推理链的能力方面，始终比最先进的推理模型高出10-15%。从技术上讲，MS-CoTF编排了一个冷冻的生物医学法学硕士主干，具有可训练的跨尺度模块，采用了每一步思维链（CoT）构建和链接的精确定义。为了确保严格的评估，我们实现了一个明确的数据集分割协议（实体不相交和时间），并严格利用推理一致性评分作为事后指标，以确保公平的比较。我们通过扩展基线进一步验证该框架，包括结构条件和多模态生物医学法学硕士，以及详细的人体评估协议和幻觉压力测试。

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引用次数: 0

Establishment of threshold of human gut microbes and risk assessment system for colorectal cancer 人类肠道微生物阈值及结直肠癌风险评估体系的建立。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-02-15 Epub Date: 2026-01-19 DOI: 10.1016/j.compbiomed.2026.111484

Wu Yinhang , Zhuang Jing , Qu Zhanbo , Liu Jiang , Zhou Qing , Zhang Qi , Jin Yin , Song Jianwen , Wu Wei , Han Shuwen

Background

Being involved in the occurrence of colorectal cancer (CRC), gut microbes are potential targets for early diagnosis of CRC. Defining the threshold of these characteristic bacteria could provide a basis for the clinical application of microorganisms as novel tumor markers for CRC.

Objective

To sort out and define the threshold of related bacteria and the ecological characteristics of gut bacteria.

Methods

A total of 8021 fecal samples from healthy people and 497 from CRC patients in the public database were collected to analyse the reference range. CRC-related bacteria and gut microbial characteristics were screened by literature review and analysed. CRC related bacteria and 5–95 % medians of gut microbial characteristics in healthy populations were used as reference value. 16S rRNA Miseq sequencing (175 CRC patients and 175 healthy people) and PacBio sequencing (200 CRC patients and 200 healthy people) were used to detect stool DNA sequence. The community composition of gut microbiota between CRC and healthy subjects was plotted; the species differences were analysed by Lefse analysis. R studio software was used to analyse CRC-related bacteria and gut microbial characteristics.

Results

A total of 218 CRC-associated bacteria and 15 gut microbial characteristics, such as enterotypes and Firmicutes/Bacteroidetes ratio, were reviewed and analysed. A 5–95 % threshold for these 218 CRC-associated bacteria and 15 gut microbiome signatures was developed to provide criteria for the normal range of gut bacteria. The CRC evaluation intelligent system software was developed and it could quickly calculate the value of 218 CRC related bacteria and 15 gut microbial characteristics using sequencing data, and assess whether they are within the threshold. And this software has the function of predicting CRC risk. The accuracy of CRC risk assessment ranged from 89.14 % to 91.50 %.

Conclusion

We established, for the first time, quantitative thresholds for CRC-associated bacteria and have driven advances in microbial risk prediction for CRC.

背景：肠道微生物参与结直肠癌（CRC）的发生，是CRC早期诊断的潜在靶点。确定这些特征细菌的阈值可以为微生物作为结直肠癌新型肿瘤标志物的临床应用提供依据。目的：整理和界定相关细菌的阈值及肠道细菌的生态特性。方法：收集公共数据库中健康人群粪便样本8021份，结直肠癌患者粪便样本497份，分析其参考范围。通过文献综述筛选crc相关菌群及肠道微生物特征并进行分析。以健康人群中CRC相关细菌和5- 95%肠道微生物特征中位数作为参考值。采用16S rRNA Miseq测序（175例结直肠癌患者和175例健康人）和PacBio测序（200例结直肠癌患者和200例健康人）检测粪便DNA序列。绘制结直肠癌与健康人肠道菌群的群落组成；采用Lefse分析法分析物种差异。采用R studio软件分析crc相关菌群及肠道微生物特征。结果：对218种crc相关细菌和15种肠道微生物特征（如肠道类型和厚壁菌门/拟杆菌门比例）进行了综述和分析。为218种crc相关细菌和15种肠道微生物组特征制定了5- 95%的阈值，为肠道细菌的正常范围提供了标准。开发CRC评估智能系统软件，利用测序数据快速计算218种CRC相关细菌和15种肠道微生物特征值，并评估其是否在阈值范围内。该软件具有预测结直肠癌风险的功能。CRC风险评估准确率为89.14% ~ 91.50%。结论：我们首次建立了CRC相关细菌的定量阈值，并推动了CRC微生物风险预测的进展。

{"title":"Establishment of threshold of human gut microbes and risk assessment system for colorectal cancer","authors":"Wu Yinhang , Zhuang Jing , Qu Zhanbo , Liu Jiang , Zhou Qing , Zhang Qi , Jin Yin , Song Jianwen , Wu Wei , Han Shuwen","doi":"10.1016/j.compbiomed.2026.111484","DOIUrl":"10.1016/j.compbiomed.2026.111484","url":null,"abstract":"<div><h3>Background</h3><div>Being involved in the occurrence of colorectal cancer (CRC), gut microbes are potential targets for early diagnosis of CRC. Defining the threshold of these characteristic bacteria could provide a basis for the clinical application of microorganisms as novel tumor markers for CRC.</div></div><div><h3>Objective</h3><div>To sort out and define the threshold of related bacteria and the ecological characteristics of gut bacteria.</div></div><div><h3>Methods</h3><div>A total of 8021 fecal samples from healthy people and 497 from CRC patients in the public database were collected to analyse the reference range. CRC-related bacteria and gut microbial characteristics were screened by literature review and analysed. CRC related bacteria and 5–95 % medians of gut microbial characteristics in healthy populations were used as reference value. 16S rRNA Miseq sequencing (175 CRC patients and 175 healthy people) and PacBio sequencing (200 CRC patients and 200 healthy people) were used to detect stool DNA sequence. The community composition of gut microbiota between CRC and healthy subjects was plotted; the species differences were analysed by Lefse analysis. R studio software was used to analyse CRC-related bacteria and gut microbial characteristics.</div></div><div><h3>Results</h3><div>A total of 218 CRC-associated bacteria and 15 gut microbial characteristics, such as enterotypes and Firmicutes/Bacteroidetes ratio, were reviewed and analysed. A 5–95 % threshold for these 218 CRC-associated bacteria and 15 gut microbiome signatures was developed to provide criteria for the normal range of gut bacteria. The CRC evaluation intelligent system software was developed and it could quickly calculate the value of 218 CRC related bacteria and 15 gut microbial characteristics using sequencing data, and assess whether they are within the threshold. And this software has the function of predicting CRC risk. The accuracy of CRC risk assessment ranged from 89.14 % to 91.50 %.</div></div><div><h3>Conclusion</h3><div>We established, for the first time, quantitative thresholds for CRC-associated bacteria and have driven advances in microbial risk prediction for CRC.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111484"},"PeriodicalIF":6.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146008911","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

Generative AI in medicine: A thorough examination of applications, challenges, and future perspectives 医学中的生成式人工智能：对应用、挑战和未来前景的全面考察。

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-02-15 Epub Date: 2026-01-19 DOI: 10.1016/j.compbiomed.2026.111469

S. Jayasrilakshmi, Ansuman Mahapatra

Generative AI, an artificial intelligence, significantly transforms the healthcare sector. Recent breakthroughs in Generative AI include the use of language models and leveraging modern pre-trained Transformer models such as ChatGPT, Bard, LLaMA, DALL-E, and Bing. In medical applications, the advent of Large Language Models (LLMs) is a significant tool for predicting diseases, identifying risk factors, and enhancing diagnostic accuracy by analyzing a massive volume of unevenly distributed medical resources. This study provides a comprehensive review of existing literature on the use of LLMs in healthcare. It elucidates the ‘status quo’ of language models for general readers, healthcare professionals, and researchers. Specifically, this study investigates the capabilities of LLMs, including the transformation of healthcare consultation, enhancement of patient management and treatment, evolution of medical education, optimal resource utilization, and advancement of clinical research. The article organizes the literature based on human organs that will help readers quickly find relevant LLM applications for specific medical fields. The outcome of this survey will help medical professionals, researchers, and the healthcare industry understand the benefits, challenges, observed limitations, future challenges and applications of LLMs in healthcare.

生成式人工智能是一种人工智能，它极大地改变了医疗保健行业。生成式人工智能的最新突破包括使用语言模型和利用现代预训练的Transformer模型，如ChatGPT、Bard、LLaMA、dal - e和Bing。在医疗应用中，大型语言模型（llm）的出现是通过分析大量分布不均匀的医疗资源来预测疾病、识别风险因素和提高诊断准确性的重要工具。本研究提供了一个全面的文献综述现有的法学硕士在医疗保健的使用。它为普通读者、医疗保健专业人员和研究人员阐明了语言模型的“现状”。具体而言，本研究考察法学硕士的能力，包括医疗咨询的转变、患者管理和治疗的提高、医学教育的演变、资源的优化利用和临床研究的进步。文章以人体器官为基础组织文献，帮助读者快速找到特定医学领域的相关LLM应用。这项调查的结果将帮助医疗专业人员、研究人员和医疗保健行业了解法学硕士在医疗保健领域的优势、挑战、观察到的限制、未来的挑战和应用。

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引用次数: 0

A hybrid hierarchical transformer model for ECG classification and age prediction 一种用于心电分类和年龄预测的混合层次变压器模型。

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.2026.111462

Pedro Dutenhefner , Turi Rezende , José Geraldo Fernandes , Diogo Tuler , Gabriela M.M. Paixão , Gisele Pappa , Antønio Ribeiro , Wagner Meira Jr.

Electrocardiograms (ECGs) play a crucial role in cardiovascular healthcare, requiring effective analytical models. ECG analysis is inherently hierarchical, involving multiple temporal scales from individual waveforms to intervals within heartbeats, and finally to the distances between heartbeats. Convolutional Neural Networks (CNNs) have demonstrated strong performance in ECG classification tasks due to their inductive bias toward local connectivity and translation invariance. In other domains, Transformers have emerged as powerful models for capturing long-range dependencies. This paper introduces HiT-NeXt, a hybrid hierarchical model designed to capture both local morphological patterns and global temporal dependencies by combining CNNs with transformer blocks featuring restricted attention windows. The model incorporates ConvNeXt-based convolutional layers to extract local features and perform patch merging, enabling hierarchical representation learning. Transformer blocks are constrained with local attention windows and leverage relative contextual positional encoding to incorporate positional information effectively into embeddings, enhancing robustness to translations in ECG signal patterns. Experimental results demonstrate that HiT-NeXt outperforms state-of-the-art methods on tasks including ECG abnormality classification and cardiological age prediction, achieving superior performance compared to both existing models and cardiologist evaluations.²

心电图（ECGs）在心血管保健中起着至关重要的作用，需要有效的分析模型。ECG分析本质上是分层的，涉及从单个波形到心跳间隔的多个时间尺度，最后到心跳之间的距离。卷积神经网络（cnn）由于其对局部连通性和平移不变性的归纳偏见，在心电分类任务中表现出了很强的性能。在其他领域，变形金刚已经成为捕获远程依赖关系的强大模型。本文介绍了HiT-NeXt，这是一种混合层次模型，旨在通过将cnn与具有限制注意窗口的变形块相结合来捕获局部形态模式和全局时间依赖性。该模型结合了基于convnext的卷积层来提取局部特征并执行补丁合并，从而实现分层表示学习。变压器块受到局部注意窗口的约束，并利用相对上下文位置编码将位置信息有效地嵌入到嵌入中，增强了对心电信号模式转换的鲁棒性。实验结果表明，HiT-NeXt在ECG异常分类和心脏年龄预测等任务上优于最先进的方法，与现有模型和心脏病专家评估相比，都取得了更好的性能。

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引用次数: 0

Dementia severity index: A threshold-based approach to classifying dementia levels using resting state EEG 痴呆严重程度指数：一种基于阈值的方法来分类痴呆水平使用静息状态脑电图

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-02-15 Epub Date: 2026-01-27 DOI: 10.1016/j.compbiomed.2026.111505

Shivani Ranjan , Robin Badal , Pramod Yadav , Lalan Kumar

Background

Alzheimer’s Disease (AD) and FrontoTemporal Dementia (FTD) are dementia conditions that often overlap clinically, leading to misdiagnoses. Traditional questionnaires are subjective and time-intensive, while neuroimaging is costly and less accessible. EEG-based methods offer a cost-effective alternative but primarily focus on spectral and source analyses, with a limited exploration into quantitative range identification for differentiating dementia states.

Methods

This study presents a threshold-based approach to dementia-level classification using resting-state EEG. In particular, an algorithm is presented for threshold computation followed by Dementia Severity Index (DSI) formulation. Two potential biomarkers for cognitive decline that capture band-specific alterations are explored. These biomarkers form the basis of the DSI, categorizing individuals into AD, FTD, or Healthy Control (HC). The classification performance of the proposed DSI is evaluated comprehensively using multiple machine learning classifiers and subject validation strategies.

Results

The proposed DSI-based approach achieves classification accuracies of 81.62% using kNN. The approach reliability is validated across three diverse EEG datasets and through threshold variation analysis. Furthermore, the relationship between EEG features and cognitive performance is analyzed using Spearman’s correlation. A significant correlation of 0.79 and 0.62 is obtained between predicted and actual MMSE.

Conclusion

The proposed DSI effectively differentiates AD, FTD, and HC, providing a robust threshold-based framework for dementia assessment. It enhances interpretability by assigning quantitative values to dementia states and reduces subjective reliance. This study offers a potential EEG-based biomarker suitable for clinical settings, offering minimal stress to patients during assessments.

阿尔茨海默病（AD）和额颞叶痴呆（FTD）是临床上经常重叠的痴呆疾病，导致误诊。传统的问卷调查是主观的、耗时的，而神经成像既昂贵又不易获得。基于脑电图的方法提供了一种具有成本效益的替代方法，但主要侧重于光谱和源分析，对区分痴呆状态的定量范围识别的探索有限。方法提出了一种基于阈值的静息状态脑电图痴呆水平分类方法。特别是，提出了一种阈值计算算法，然后制定痴呆严重程度指数（DSI）。探索了两个潜在的认知衰退生物标志物，它们可以捕获特定波段的改变。这些生物标志物构成了DSI的基础，将个体分为AD、FTD或健康控制（HC）。使用多个机器学习分类器和主题验证策略对所提出的DSI的分类性能进行了综合评估。结果基于dsi的kNN分类准确率达到81.62%。通过三种不同的EEG数据集和阈值变异分析验证了该方法的可靠性。在此基础上，利用Spearman相关分析了脑电特征与认知能力的关系。预测MMSE与实际MMSE的相关系数分别为0.79和0.62。结论提出的DSI可有效区分AD、FTD和HC，为痴呆评估提供了一个稳健的基于阈值的框架。它通过为痴呆状态分配定量值来增强可解释性，并减少主观依赖。这项研究提供了一种潜在的适合临床环境的基于脑电图的生物标志物，在评估过程中为患者提供最小的压力。

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引用次数: 0

Machine learning guided structural dynamics identifies translation elongation factor 1 (EEF1A1) as an immunological biomarker and marine natural products as therapeutic leads for rheumatoid arthritis with major depressive disorder 机器学习引导结构动力学识别翻译延伸因子1 （EEF1A1）作为免疫生物标志物和海洋天然产物作为类风湿关节炎伴重度抑郁症的治疗线索

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine

Pub Date : 2026-02-15 Epub Date: 2026-01-16 DOI: 10.1016/j.compbiomed.2026.111480

Santhiya Panchalingam , Govindaraju Kasivelu , Manikandan Jayaraman , Jeyakanthan Jeyaraman

Rheumatoid arthritis (RA) is a systemic autoimmune disease that predominantly affects synovial joints, especially those of the hands, elbows, wrists, knees, and shoulders. RA frequently co-occurs with major depressive disorder (MDD), amplifying disease burden and complicating clinical outcomes. This study employed a multi-step integrative bioinformatics and structural biology framework to identify candidate molecular biomarkers for RA and MDD. Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on the epitranscriptomic dataset. These analyses identified immune-regulatory gene modules that were significantly associated with both phenotypes. Least absolute shrinkage and selection operator (LASSO) regression was applied to select strong, statistically significant biomarkers. The methylated biomarker EEF1A1 was identified, and its structure predicted via AlphaFold, was subjected to in silico structure-based virtual screening (SBVS) against the Comprehensive Marine Natural Product Database (CMNPD). Four marine natural products (CMNPD17984, CMNPD27318, CMNPD26200, and CMNPD26011) showed significant binding affinity for EEF1A1. Furthermore, EEF1A1-MNP complexes were simulated for 150 ns using GROMACS, and PCA-based free energy landscape (FEL) analyses were performed to characterize the dynamic behavior and identify energy minima. This integrated computational approach provides a comprehensive platform for biomarker discovery and validation in RA and MDD, with potential applications in early diagnosis, therapeutic targeting, and precision medicine.

类风湿性关节炎（RA）是一种系统性自身免疫性疾病，主要影响滑膜关节，特别是手、肘关节、手腕、膝盖和肩膀。RA经常与重度抑郁症（MDD）共同发生，加重了疾病负担并使临床结果复杂化。本研究采用多步骤综合生物信息学和结构生物学框架来确定RA和MDD的候选分子生物标志物。对表转录组数据集进行差异基因表达分析和加权基因共表达网络分析（WGCNA）。这些分析确定了与两种表型显著相关的免疫调节基因模块。最小绝对收缩和选择算子（LASSO）回归应用于选择强的，具有统计学意义的生物标志物。鉴定了甲基化的生物标志物EEF1A1，并通过AlphaFold预测了其结构，并针对综合海洋天然产品数据库（CMNPD）进行了基于硅结构的虚拟筛选（SBVS）。四种海洋天然产物（CMNPD17984、CMNPD27318、CMNPD26200和CMNPD26011）对EEF1A1具有显著的结合亲和力。此外，利用GROMACS对EEF1A1-MNP配合物进行了150 ns的模拟，并进行了基于pca的自由能景观（FEL）分析，以表征其动态行为并识别能量最小值。这种综合计算方法为RA和MDD的生物标志物发现和验证提供了全面的平台，在早期诊断、治疗靶向和精准医学方面具有潜在的应用前景。

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引用次数: 0