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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 Pub 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
Research on breast ultrasound images lesion localization and diagnosis based on knowledge-driven and data-driven methods 基于知识驱动和数据驱动方法的乳腺超声图像病灶定位与诊断研究。
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-01-20 DOI: 10.1016/j.compbiomed.2026.111465
Jianqiang Li , Lintao Song , Xiaoling Liu , Yiming Liu , Tianbao Ma , Jun Bai , Qing Zhao , Xi Xu
Breast cancer poses the most significant threat to women’s health, yet early detection through screening can markedly reduce mortality. Ultrasound imaging, with its affordability, non-invasiveness, and efficacy in dense breast tissue, has emerged as a crucial tool for early screening. Recent advancements in computer vision have spurred the development of computer-aided diagnostic systems that focus on the automated localization and diagnosis of breast lesions. However, challenges such as speckle noise, blurred boundaries, and low contrast in ultrasound images impede accurate lesion detection. This review examines recent studies on breast ultrasound lesion localization and diagnosis, emphasizing model feature construction. It provides an overview of the task, available datasets, and evaluation metrics, and outlines selection criteria through a comprehensive literature analysis. The review categorizes models into three groups: domain knowledge-driven, data-driven, and hybrid approaches. It also discusses current challenges and future directions, aiming to enhance the accuracy of breast lesion localization and diagnosis.
乳腺癌对妇女健康构成最严重的威胁,但通过筛查及早发现可显著降低死亡率。超声成像以其可负担性、非侵入性和对致密乳腺组织的有效性,已成为早期筛查的重要工具。计算机视觉的最新进展促进了计算机辅助诊断系统的发展,该系统专注于乳房病变的自动定位和诊断。然而,诸如斑点噪声、模糊边界和超声图像对比度低等挑战阻碍了准确的病变检测。本文综述了近年来乳腺超声病灶定位与诊断的研究进展,重点介绍了模型特征的构建。它提供了任务、可用数据集和评估指标的概述,并通过全面的文献分析概述了选择标准。该综述将模型分为三组:领域知识驱动、数据驱动和混合方法。讨论了当前面临的挑战和未来的发展方向,旨在提高乳腺病变定位和诊断的准确性。
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引用次数: 0
Between minds and machines: A neurocognitive comparison of human and chatbot interaction in language learning 思维与机器之间:语言学习中人类与聊天机器人互动的神经认知比较。
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-01-20 DOI: 10.1016/j.compbiomed.2026.111499
Gamze Turun Ozel , Semin Kazazoglu , Burcak Yavuz , Emir Rusen
This study explores neurocognitive differences between human–human interaction (HHI) and human–chatbot interaction (HCI) during English-speaking tasks using EEG analysis. Results showed that HHI elicited significantly greater neural activation, particularly in the left frontal and temporal regions (F3, F7, T3), which are associated with language processing and social cognition. The F3 site exhibited the strongest difference (HHI: 27.09 vs. HCI: 15.5, p < .001, d = −4.02). EEG band analysis revealed higher delta activity during HCI, indicating lower cortical arousal and attentional engagement, while HHI showed greater alpha and beta power (alpha: 6.5 % vs. 2.1 %; beta: 12.1 % vs. 2.4 %), reflecting enhanced cognitive processing and emotional salience. These patterns extended to central, temporal, and parieto-occipital regions, with consistently stronger beta activity in HHI. Findings suggest that natural human interaction elicits deeper and more distributed neural engagement than chatbot communication, offering key insights into the cognitive and emotional dimensions of technology-mediated language use in educational and social contexts.
本研究利用脑电图分析探讨了英语任务中人机交互(HHI)和人机聊天交互(HCI)的神经认知差异。结果表明,HHI引起了显著更大的神经激活,特别是在与语言加工和社会认知相关的左额叶和颞叶区域(F3, F7, T3)。F3位点表现出最大的差异(HHI: 27.09 vs. HCI: 15.5, p
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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 Pub 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。
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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 Pub 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
Transformer-based feature extraction approach for hematopoietic cancer subtype classification 基于变压器特征提取的造血癌亚型分类方法。
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-01-19 DOI: 10.1016/j.compbiomed.2026.111466
Kwang Ho Park , Younghee Lee , Wei Ding , Kwang Sun Ryu , Keun Ho Ryu
Accurate classification of hematopoietic cancer subtypes remains challenging due to the multipotent nature of hematopoietic cells and the absence of definitive genetic markers. To address this, we propose a Transformer-based Autoencoder that captures compact and biologically informative embeddings from gene expression data. Specifically, our method employs multi-head self-attention in the encoder to learn complex nonlinear interactions among genes, with a reconstruction decoder that enforces biological feature retention. We benchmarked our approach against four widely-used feature extraction methods—Principal Component Analysis, Non-negative Matrix Factorization, Autoencoder, and Variational Autoencoder—using transcriptomic data from five hematopoietic cancer subtypes in The Cancer Genome Atlas, totaling 2452 samples. Data were split 60:20:20 into training, validation, and test sets with stratification, and feature-extractor hyperparameters were chosen on the validation set. Each method produced 100-dimensional feature vectors, subsequently evaluated using eight multi-class classifiers: Light Gradient Boosting Machine, Extreme Gradient Boosting, Logistic Regression, Random Forest, Decision Tree, Support Vector Machine, and Neural Networks. On the independent test set, the Transformer-based Autoencoder embeddings combined with Light Gradient Boosting Machine achieved F1-score: 0.969, accuracy: 0.986, precision: 0.975, recall: 0.964, specificity: 0.996, G-mean: 0.980, and balanced accuracy: 0.954. For context, we additionally included a supervised tabular Transformer (FT-Transformer) as a reference; while strong, it is not directly comparable to our unsupervised feature extractor. To enhance interpretability and clinical relevance, we applied Shapley Additive exPlanations to identify the twenty most influential genes contributing to subtype discrimination. This analysis revealed key biomarkers related to endoplasmic reticulum function, antigen processing, and ribonucleic acid regulation. These findings demonstrate that transformer-based unsupervised feature extraction substantially improves predictive accuracy and yields valuable biological insights for complex hematologic malignancies. Overall, the study supports attention-driven representation learning for tabular biomedical data and motivates future work in generative/self-supervised representations for gene expression.
由于造血细胞的多能性和缺乏明确的遗传标记,对造血癌亚型的准确分类仍然具有挑战性。为了解决这个问题,我们提出了一个基于转换器的自编码器,它可以从基因表达数据中捕获紧凑的生物信息嵌入。具体来说,我们的方法在编码器中使用多头自注意来学习基因之间复杂的非线性相互作用,并使用重建解码器来强制保留生物特征。我们将我们的方法与四种广泛使用的特征提取方法——主成分分析、非负矩阵分解、自编码器和变分自编码器——进行基准测试,使用来自癌症基因组图谱中五种造血癌症亚型的转录组数据,共计2452个样本。数据以60:20:20的比例被分层分成训练集、验证集和测试集,并在验证集上选择特征提取器超参数。每种方法产生100维特征向量,随后使用8个多类分类器进行评估:光梯度增强机、极端梯度增强机、逻辑回归、随机森林、决策树、支持向量机和神经网络。在独立测试集上,基于变压器的自编码器嵌入组合光梯度增强机的f1得分为0.969,准确率为0.986,精密度为0.975,召回率为0.964,特异性为0.996,g均值为0.980,平衡准确率为0.954。出于上下文考虑,我们还包括了一个受监督的表格变压器(FT-Transformer)作为参考;虽然它很强大,但不能直接与我们的无监督特征提取器相比较。为了提高可解释性和临床相关性,我们应用Shapley加性解释来确定20个对亚型歧视最有影响的基因。该分析揭示了与内质网功能、抗原加工和核糖核酸调节相关的关键生物标志物。这些发现表明,基于变压器的无监督特征提取大大提高了预测准确性,并为复杂的血液恶性肿瘤提供了有价值的生物学见解。总的来说,该研究支持了表格生物医学数据的注意驱动表征学习,并激励了基因表达的生成/自我监督表征的未来工作。
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引用次数: 0
Helical static-mixer insert for pediatric and neonatal gas blending: RANS-CFD comparison of commercial and in-house monolithic designs 用于儿科和新生儿气体混合的螺旋静态混合器插入:商业和内部单片设计的ranss - cfd比较。
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-01-19 DOI: 10.1016/j.compbiomed.2026.111475
Shirley Ferraz Crispilho , Paulo Cesar Duarte Junior , Martin Poulsen Kessler , Rudolf Huebner , Altibano Ortenzi
Accurate blending of oxygen and air in pediatric and neonatal respiratory support depends on compact connectors that promote efficient mixing without generating excessive pressure drop or dead volume. In current clinical practice, commercially available T-shaped connectors are often used as passive mixers, but their internal geometry was not originally optimized for this purpose. In this work, the original commercial connector (Geometry A), an in-house modified multi-part connector incorporating a static insert (Geometry B), and a new monolithic helical static-mixer insert (Geometry C) were evaluated under identical flow conditions. Three-dimensional Reynolds-averaged Navier–Stokes computational fluid dynamics simulations were performed to represent oxygen–nitrogen mixing in high-flow nasal cannula circuits, considering realistic flow rates and boundary conditions. For each geometry, mixture quality at the outlet was assessed from the spatial distribution of species mass fraction, hydraulic performance was quantified by the device pressure drop, and residence-time behavior for the helical insert was obtained from a transient scalar-pulse simulation. Geometry B improved outlet homogeneity relative to the commercial connector but required several assembled parts, which complicates handling and sterilization. Geometry C, designed as a monolithic helical static mixer, produced more uniform gas mixing than both previous configurations while maintaining pressure drops within ranges compatible with pediatric and neonatal use. These results indicate that the proposed helical insert has the potential to replace the current multi-part in-house adaptation and to offer a more effective alternative to standard commercial connectors when implemented as a monolithic medical-grade component.
儿科和新生儿呼吸支持中氧气和空气的准确混合取决于紧凑的连接器,该连接器可促进有效混合,而不会产生过大的压降或死体积。在目前的临床实践中,市售的t型连接器通常用作无源混合器,但其内部几何形状最初并未针对此目的进行优化。在这项工作中,在相同的流动条件下评估了原始的商业连接器(几何A)、内部改进的包含静态插入(几何B)的多部件连接器和新的单片螺旋静态混合器插入(几何C)。考虑实际流量和边界条件,采用三维reynolds -平均Navier-Stokes计算流体动力学模拟了高流量鼻插管循环中的氧氮混合。对于每种几何形状,通过物种质量分数的空间分布来评估出口的混合质量,通过装置压降来量化水力性能,并通过瞬态标量脉冲模拟获得螺旋插入的停留时间行为。相对于商业连接器,几何B改善了出口均匀性,但需要几个组装部件,这使得处理和灭菌变得复杂。Geometry C被设计为单片螺旋静态混合器,比之前的两种配置产生更均匀的气体混合,同时将压降保持在适合儿科和新生儿使用的范围内。这些结果表明,所提出的螺旋插入物有可能取代目前的多部分内部适配,并在作为单片医疗级组件实施时,为标准商用连接器提供更有效的替代方案。
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引用次数: 0
Multimodal diagnosis of Parkinson’s disease with an internet-based collaborative agent architecture of medical language models 基于互联网的医学语言模型协同代理体系结构的帕金森病多模态诊断。
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-01-19 DOI: 10.1016/j.compbiomed.2026.111468
Eugenio Peixoto Junior , Felipe Cordeiro de Sousa , Junxin Chen , David Camacho , Stephen Rathinaraj Benjamin , Victor Hugo C. de Albuquerque
Parkinson’s disease (PD) remains one of the most prevalent neurodegenerative disorders, where delays in diagnosis compromise therapeutic outcomes and increase healthcare costs. Conventional unimodal approaches, based on voice, sensors, or imaging, face critical limitations, including small datasets, lack of reproducibility, and high infrastructure demands. To address these challenges, the proposed multimodal agent-based architecture integrates medical language models, audio signals, and neuroimaging, and is supported by data–machine learning pipelines and an edge–cloud infrastructure. The system leverages ensemble learning, large and vision language models, and Retrieval-Augmented Generation (RAG) to enhance clinical decision support. The transparency of the model was supported by explainability techniques (SHapley Additive exPlanations, permutation importance, partial dependence, and individual conditional expectation), which highlighted the main audio and sensor variables responsible for the predictions. Experimental evaluation confirmed the effectiveness of multimodal fusion. When integrated, the architecture achieved robust performance, with an accuracy of 0.86, an F1-score above 0.88, ROC-AUC greater than 0.93, and both sensitivity and specificity above 0.89. Calibration and hypothesis tests were validated by a low Brier score of 0.205 and an Expected Calibration Error of 0.151, while Decision Curve Analysis confirmed clinical relevance by minimizing false negatives, critical for early screening, and reducing redundant interventions. Multimodal fusion produced accurate, well-calibrated, and interpretable risk estimates for PD screening; larger prospective studies and cost-effectiveness analyses are needed to consolidate clinical applicability.
帕金森氏病(PD)仍然是最普遍的神经退行性疾病之一,其中诊断延误损害治疗结果并增加医疗保健费用。基于语音、传感器或成像的传统单模方法面临着严重的局限性,包括数据集小、缺乏可重复性和对基础设施的高要求。为了应对这些挑战,提出的基于多模态代理的架构集成了医学语言模型、音频信号和神经成像,并由数据机器学习管道和边缘云基础设施提供支持。该系统利用集成学习、大型和视觉语言模型以及检索增强生成(RAG)来增强临床决策支持。可解释性技术(SHapley Additive explanation,排列重要性,部分依赖性和个人条件期望)支持了模型的透明度,这些技术突出了负责预测的主要音频和传感器变量。实验评价证实了多模态融合的有效性。集成后,该体系结构具有良好的性能,准确率为0.86,f1评分在0.88以上,ROC-AUC大于0.93,灵敏度和特异性均在0.89以上。校准和假设检验的Brier评分为0.205,预期校准误差为0.151,而决策曲线分析通过最大限度地减少假阴性来证实临床相关性,这对早期筛查至关重要,并减少冗余干预。多模式融合为帕金森病筛查提供了准确、校准良好、可解释的风险评估;需要更大规模的前瞻性研究和成本效益分析来巩固临床适用性。
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引用次数: 0
Unveiling the renal therapeutic potential of Nypa fruticans leaves: An integrated experimental and in silico approach 揭示果叶的肾脏治疗潜力:一个综合的实验和计算机方法。
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-01-19 DOI: 10.1016/j.compbiomed.2026.111482
Farhana Islam , Mostafa Kamal , Shoeb Ahmad , Masum Shahriar , Fariya Islam Rodru , Md. Nazmul Hasan , Md. Nazmul Hasan Zilani , Md. Ataur Rahman , Shahad Saif Khandker , Saquiba Yesmine
Chronic kidney disease (CKD) is a progressive, irreversible disorder associated with renal dysfunction, inflammation, and oxidative stress. Given the limitations of current therapies, this study assessed the renal curative effects of Nypa fruticans ethyl acetate leaf extract (EaNFL) in a gentamicin-induced nephrotoxicity rat model. GC‒MS and HPLC analyses identified 23 bioactive compounds in EaNFL, including rosmarinic acid, quercetin, and (−)-epicatechin, which were selected based on ADMET profiling, Lipinski's rule, and DFT analysis. These compounds were further investigated through computational studies against two renal targets: the AT1 receptor (PDB ID: 4YAY) and SGLT2 (PDB ID: 7VSI). Treatment with EaNFL, particularly at 400 mg/kg body weight and in combination therapy, significantly improved renal function and normalized biochemical and hematological parameters, likely due to its potent antioxidant and anti-inflammatory properties. Histopathological data supported these findings, showing reduced tubular necrosis, glomerular damage, and inflammation, especially in the high-dose groups. DFT analysis revealed that rosmarinic acid had the highest HOMO–LUMO energy gap (ΔE = 0.1314 eV), suggesting high chemical reactivity and potential biological compatibility. Molecular docking identified quercetin, rosmarinic acid, and (−)-epicatechin as the top binders, with rosmarinic acid showing the strongest affinity and forming a stable complex, as confirmed by 100 ns MDS. Taken together, the in vivo and in silico results indicate that EaNFL offers renoprotective benefits by targeting the RAAS and glucose transport pathways while also mitigating oxidative stress and inflammation. These findings demonstrate its therapeutic potential and warrant further investigation into its bioactive constituents and potential clinical use in renal treatment.
慢性肾脏疾病(CKD)是一种进行性、不可逆的疾病,与肾功能障碍、炎症和氧化应激有关。鉴于现有治疗方法的局限性,本研究在庆大霉素引起的肾毒性大鼠模型中评估了木果乙酸乙酯叶提取物(EaNFL)的肾脏疗效。GC-MS和HPLC分析鉴定了EaNFL中23个生物活性化合物,包括迷迭香酸、槲皮素和(-)-表儿茶素,这些化合物是根据ADMET谱图、Lipinski规则和DFT分析筛选出的。这些化合物通过对两个肾脏靶点的计算研究进一步研究:AT1受体(PDB ID: 4YAY)和SGLT2 (PDB ID: 7VSI)。EaNFL治疗,特别是在400 mg/kg体重和联合治疗中,显著改善肾功能和正常化生化和血液学参数,可能是由于其有效的抗氧化和抗炎特性。组织病理学数据支持这些发现,显示小管坏死、肾小球损伤和炎症减少,特别是在高剂量组。DFT分析显示迷迭香酸具有最高的HOMO-LUMO能隙(ΔE = 0.1314 eV),具有较高的化学反应活性和潜在的生物相容性。分子对接鉴定槲皮素、迷迭香酸和(-)-表儿茶素为顶部结合物,其中迷迭香酸亲和力最强,形成稳定的配合物,经100 ns MDS证实。综上所述,体内和硅实验结果表明,EaNFL通过靶向RAAS和葡萄糖运输途径提供肾保护作用,同时还能减轻氧化应激和炎症。这些发现证明了其治疗潜力,值得进一步研究其生物活性成分和潜在的临床应用于肾脏治疗。
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引用次数: 0
Multi-task non-contact ballistocardiogram-based vital signs monitoring in acupuncture 多任务非接触式心电图在针刺生命体征监测中的应用。
IF 6.3 2区 医学 Q1 BIOLOGY Pub Date : 2026-01-17 DOI: 10.1016/j.compbiomed.2026.111461
Truong Tien Vo , Quy Phuong Le , Trong Nhan Nguyen , Jaeyeop Choi , Sudip Mondal , Byeongil Lee , Junghwan Oh
The study introduces an innovative approach for efficient vital signs monitoring in acupuncture by combining multi-channel ballistocardiogram (BCG) signals and multi-task learning, taking advantage of the polyvinylidene fluoride (PVDF) film sensor and deep neural networks. The proposed system utilizes non-contact under-mattress BCG signals and deep learning for heart rate (HR), respiration rate (RR) estimation and lying posture detection. A custom-designed data-logger captures the signal from a BCG sensor located under the patient’s back for data acquisition, and integrates Gated Recurrent Unit (GRU) and Multi-head Self-Attention (MHSA) deep learning mechanisms for efficient HR, RR estimation and posture classification. In experiments with 25 participants, the proposed method achieved 98.7% accuracy for activity recognition and 97.6% for lying posture classification. In HR and RR estimation, the best case of mean absolute error (MAE) for HR achieves 0.77 beats per minute (bpm) in the right lateral posture, while the best value of MAE for RR is 0.43 breaths per minute (brpm) in the seated posture, compared to an FDA-approved device. The results demonstrate the high performance of multi-task learning for vital signs estimation and posture classification with our BCG-based system. This work establishes an innovative and practical pathway for medical assistance tools in non-contact monitoring and management.
本研究提出了一种结合多通道BCG信号和多任务学习,利用聚偏氟乙烯(PVDF)薄膜传感器和深度神经网络的针刺生命体征高效监测创新方法。该系统利用非接触式床垫下BCG信号和深度学习进行心率(HR)、呼吸速率(RR)估计和躺姿检测。定制设计的数据记录仪从患者背部下方的BCG传感器捕获信号进行数据采集,并集成门控循环单元(GRU)和多头自注意(MHSA)深度学习机制,实现有效的HR、RR估计和姿势分类。在25人的实验中,该方法的活动识别准确率为98.7%,躺姿分类准确率为97.6%。在HR和RR估计中,与fda批准的设备相比,右侧卧位HR的最佳平均绝对误差(MAE)达到0.77次/分钟(bpm),而坐姿RR的最佳平均绝对误差(MAE)为0.43次/分钟(brpm)。结果表明,基于bcg的多任务学习系统在生命体征估计和姿势分类方面具有很高的性能。这项工作为医疗援助工具在非接触监测和管理方面开辟了一条创新和实用的途径。
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Computers in biology and medicine
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