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Glycemic Control and Diabetes Duration in Relation to Subsequent Myocardial Infarction among Patients with Coronary Heart Disease and Type 2 Diabetes

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Biomedical and Environmental Sciences 2025年第1期38卷 27-36页

作者： Furong Li Yan Dou Chunbao Mo Shuang Wang Jing Zheng Dongfeng Gu Fengchao Liang School of Public Health and Emergency Management Southern University of Science and TechnologySchool of MedicineShenzhen 518055GuangdongChina Shenzhen Key Laboratory of Cardiovascular Health and Precision Medicine Southern University of Science and TechnologyShenzhen 518055GuangdongChina Department of Epidemiology National Center for Cardiovascular DiseasesFuwai HospitalChinese Academy of Medical Sciences&Peking Union Medical College/National Center for Cardiovascular DiseasesBeijing 100037China Shenzhen Health Development Research and Data Management Center Shenzhen 518106GuangdongChina

Objective This study aimed to investigate the impact of glycemic control and diabetes duration on subsequent myocardial infarction(MI)in patients with both coronary heart disease(CHD)and type 2 diabetes(T2D).Methods We conducted a retrospective cohort study of 33,238 patients with both CHD and T2D in shenzhen,*** were categorized into 6 groups based on baseline fasting plasma glucose(FPG)levels and diabetes duration(from the date of diabetes diagnosis to the baseline date)to examine their combined effects on subsequent *** proportional hazards regression models were used,with further stratification by age,sex,and comorbidities to assess potential *** Over a median follow-up of 2.4 years,2,110 patients experienced *** to those with optimal glycemic control(FPG<6.1 mmol/L)and shorter diabetes duration(<10 years),the fullyadjusted hazard ratio(HR)(95%Confidence Interval[95%CI])for those with a diabetes duration of≥10 years and FPG>8.0 mmol/L was 1.93(95%CI:1.59,2.36).The combined effects of FPG and diabetes duration on MI were largely similar across different age,sex,and comorbidity groups,although the excess risk of MI associated with long-term diabetes appeared to be more pronounced among those with atrial *** Our study indicates that glycemic control and diabetes duration significant influence the subsequent occurrence of MI in patients with both CHD and *** management strategies emphasizing strict glycemic control may be particularly beneficial for patients with longer diabetes duration and atrial fibrillation.

关键词： Coronary heart disease Type 2 diabetes Myocardial infarction Diabetes duration Fasting plasma glucose

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Nnmamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

Nnmamba: 3D Biomedical Image Segmentation, Classification an...

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IEEE International Symposium on Biomedical Imaging

作者： Haifan Gong Luoyao Kang Yitao Wang Yihan Wang Xiang Wan Xusheng Wu Haofeng Li Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong (Shenzhen) Shenzhen China Shenzhen Health Development Research and Data Management Center Shenzhen China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In biomedical image analysis, developing architectures that effectively capture long-range dependencies is crucial. Traditional Convolutional Neural Networks (CNNs) are constrained by their local receptive fields, while Transformers, though proficient in global context integration, are computationally demanding for high-dimensional medical images. Here, we present nnMamba, a novel architecture that combines the strengths of CNNs with the long-range modeling capabilities of State Space Models (SSMs). We introduce the Mamba-In-Convolution with Channel-Spatial Siamese learning (MICCSS) block to model long-range voxel relationships. Additionally, we implement channel scaling and channel-sequential learning methods to enhance performance in dense prediction and classification tasks. Extensive experiments on seven datasets demonstrate that nnMamba outperforms current state-of-the-art methods in 3D image segmentation, classification, and landmark detection. nnMamba effectively integrates CNNs' local representation with SSMs' global con-text processing, establishing a new benchmark for long-range dependency modeling in medical image analysis. Code is available at https://***/lhaof/nnMamba.

关键词： Learning systems Solid modeling Image segmentation Three-dimensional displays Image analysis Computational modeling Biological system modeling Computer architecture Transformers Biomedical imaging

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Natural polyphenols as novel interventions for aging and age-related diseases: Exploring efficacy, mechanisms of action and implications for future research

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Chinese Herbal Medicines 2025年第2期17卷 279-291页

作者： Wenze Wu Yan Mi Qingqi Meng Ning Li Wei Li Pu Wang Yue Hou Liaoning Key Laboratory of Bioresource Research and Development National Frontiers Science Center for Industrial Intelligence and Systems OptimizationKey Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationCollege of Life and Health SciencesNortheastern University Key Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang City School of Traditional Chinese Materia MedicaShenyang Pharmaceutical University Faculty of Pharmaceutical Sciences Toho University College of Pharmacy Shenzhen Technology University

Natural polyphenols are a group of components widely found in traditional Chinese medicines and have been demonstrated to delay or prevent the development of aging and age-related diseases in recent years. As far as we know, the studies of natural polyphenols in aging and aging-related diseases have never been extensively reviewed. In the present paper, we reviewed recent advances of natural polyphenols in aging and common age-related diseases and the current technological methods to improve the bioavailability of natural polyphenols. The results showed that natural polyphenols have the potential to prevent or treat aging and common age-related diseases through multiple ***, structural modifications, and matrix processing could provide strong technical support for the development of natural polyphenols to prevent or treat aging and age-related diseases. In conclusion, natural polyphenols have important potential in the prevention and treatment of aging and age-related diseases.

关键词： aging cancer cardiovascular disease natural polyphenols neurodegenerative diseases

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Multi-Modal Multi-Kernel Graph Learning for Autism Prediction and Biomarker Discovery

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks Bioinformatics

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Predicting CRISPR Cas9 Off-Target Activities With a Two-Stage Deep Learning Framework

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 956-962页

作者： Tianshan Zhang Bei Jiang Guang Yang Zhijie Bao Jiajie Peng AI for Science Interdisciplinary Research Center School of Computer Science Northwestern Polytechnical University Xi'an China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Shanxi China The National Engineering Laboratory for In tegrated Aerospace Ground Ocean Big Data Application Technology Shanxi China Tianjin second people's hospital Tianjin China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China

The CRISPR-Cas9 system, found across bacteria and archaea, enables efficient genome engineering in eukaryotic cells. There is a challenge that Cas9 guide RNA may cause off-target activities. Although numbers of methods have been proposed to predict off-target activities in guide RNA designing, this procedure involves numerous potential off-target sites, which causes label imbalance problem. To address this problem, we developed a deep learning framework, named CAF-Net (Cas9 Augmentation and Finetune Network), for predicting off-target activities of CRISPR-Cas9. First, we pretrain an embedding model to extract features from target and guide sequence pairs. Subsequently, data augmentation is applied to these features. And finally, the model is finetuned with synthetic samples. Evaluation results demonstrate its performance on published datasets.

关键词： RNA Bioinformatics Feature extraction DNA Genomics Convolutional neural networks Computational modeling Transformers Predictive models CRISPR

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Nitrate ameliorates myelin loss and cognitive impairment in Alzheimer's disease through upregulation of neuronal sialin and subsequent inhibition of TPPP phosphorylation

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Science Bulletin 2025年第8期70卷 1224-1229页

作者： Chen, Xinyue Hu, Geng Chang, Lirong Li, Xiaoyu Tang, Yi Wu, Yan Zhang, Ran Zhang, Chunmei Wang, Jinsong Wang, Xue Wang, Songlin Department of Biochemistry and Molecular Biology School of Basic Medical Sciences Beijing Laboratory of Oral Health Capital Medical University Beijing100069 China Key Laboratory of Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing100191 China Beijing Key Laboratory of Neural Regeneration and Repair Department of Anatomy School of Basic Medical Sciences Beijing Institute of Brain Disorders Capital Medical University Beijing100069 China Innovation Center for Neurological Disorders Department of Neurology Xuanwu Hospital Capital Medical University Beijing100053 China Beijing Key Laboratory of Traditional Chinese Veterinary Medicine Department of Veterinary Medicine Animal Science and Technology College Beijing University of Agriculture Beijing102206 China Department of Oral Pathology Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing100081 China National Human Brain Bank for Development and Function Institute of Basic Medical Sciences Chinese Academy of Medical Sciences School of Basic Medicine Peking Union Medical College Beijing10005 China Laboratory of Homeostatic Medicine School of Medicine Southern University of Science and Technology Shenzhen518055 China Research Units of Tooth Development and Regeneration Chinese Academy of Medical Sciences Beijing100700 China

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Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models 24

Forecasting Influenza-like Illness in Compulsory Education S...

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13th International Conference on Bioinformatics and Biomedical Science, ICBBS 2024

作者： Chen, Ying Luo, Jinsong Liang, Deyi Xie, Yewei Zhou, Yi Wu, Dan Cheng, Weibin Tang, Weiming Jing, Fengshi Faculty of Data Science City University of Macau China School of Automation Central South University Hunan Changsha China Institute for Hospital Management Tsinghua University Guangdong Shenzhen China Guangdong Women and Children's Hospital Guangdong Guangzhou China Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Singapore Zhuhai Center for Disease Control and Prevention Guangdong Zhuhai China School of Public Health Nanjing Medical University Jiangsu Nanjing China School of Data Science City University of Hong Kong Hong Kong Hong Kong Guangdong Second Provincial General Hospital Guangdong Guangzhou China School of Medicine University of North Carolina at Chapel Hill Chapel HillNC United States

ISBN: (纸本)9798400717956

This study investigates the predictive utility of Google search queries for forecasting influenza-like illness (ILI) in compulsory education schools in Macau. The increasing availability of online data offers a novel approach to health surveillance, potentially improving the timeliness and accuracy of ILI predictions in educational settings. We employed three machine learning models: extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and ridge regression (Ridge), to forecast the ILI-caused absence rate in kindergartens, primary schools, and middle schools one and two weeks in advance in Macau. The covariates for these models include Google search queries and historical ILI data. This is Macau's first study to apply machine learning methodologies and Internet big data to the surveillance of ILI in educational institutions. Our approach offers a feasible and computationally efficient method for forecasting ILI in Macau's compulsory education schools. This methodology could be adapted for use in other regions with limited influenza data resources, providing a valuable tool for public health planning and response. © 2024 Copyright held by the owner/author(s).

关键词： Biomedical data analysis

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Classification of Medical Image Notes for Image Labeling by Using MinBERT

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Tsinghua Science and Technology 2023年第4期28卷 613-627页

作者： Bokai Yang Yujie Yang Qi Li Denan Lin Ye Li Jing Zheng Yunpeng Cai Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China Shenzhen Health Development Research and Data Management Center Shenzhen 518055China

The lack of labeled image data poses a serious challenge to the application of artificial intelligence(AI)in medical image *** image notes contain valuable patient information that could be used to label images for machine learning ***,most image note texts are unstructured with heterogeneity and short-paragraph characters,which fail traditional keyword-based *** utilized a deep learning approach to recover missing labels for medical image notes automatically by using a combination of deep word embedding and deep neural network *** encoder representations from transformers trained on medical image notes corpus(MinBERT)were *** applied the proposed techniques to two typical classification tasks:Medical image type identification and clinical diagnosis *** two methods significantly outperformed baseline methods and presented high accuracies of 99.56%and 99.72%in image type identification and of 94.56%and 92.45%in clinical diagnosis *** analysis further indicated that word embedding could efficiently capture semantic similarities and regularities across diverse *** indicated that our proposed framework could accurately recover the missing label information of medical images through the automatic extraction of electronic medical record ***,it could serve as a powerful tool for exploring useful training data in various medical AI applications.

关键词： MinBERT convolutional neural network electronic medical record medical image labeling word embedding

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Air pollution and hypertension in rural versus urban children: Lipidomic insights into PM2.5 impacts

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Environmental research 2025年 278卷 121715页

作者： Liu, Qin Zhang, Di Liang, Fengchao Liu, Fangchao Xiao, Leyuan An, Xizhou Chen, Xin Liang, Xiaohua Department of Epidemiology and Biostatistics Children's Hospital of Chongqing Medical University National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity Chongqing China School of Public Health and Emergency Management Southern University of Science and Technology Shenzhen China Department of Epidemiology Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China

Fine particulate matter and its impact on blood pressure (BP) in children remain a concern, with the role of lipid metabolism as a potential mediator not fully elucidated. We conducted a cohort study of 2239 urban subjects and 2194 rural subjects at baseline in China from 2014 to 2024 and a nested case-control study with lipidomics analyses. Analysis results showed that higher fine particulate matter smaller than 2.5 μm (PM2.5) exposure associated with high density lipoprotein cholesterol (HDL-C), non-HDL-C, and higher systolic blood pressure (SBP), partially mediated by HDL-C/non-HDL-C changes. Mediation analysis indicated a significant mediating effect of HDL-C on the PM2.5-DBP and PM2.5-MAP (DBP, diastolic blood pressure;MAP, mean arterial pressure) association in urban subjects, while no mediation effect was found in rural subjects. For non-HDL-C, significant mediating effects were observed in both urban and rural subjects. Further analyses revealed distinct urban-rural lipidomic patterns, with specific phosphatidylethanolamine (PEs) mediating PM2.5-related hypertension in rural subjects, while lactosylceramides (LacCer) played this role in urban youth. These patterns extended to other BP indices as well. In the urban area, PG(44:11), LacCer(d45:1), were identified as playing significant mediating roles in the association between PM2.5 exposure and hypertension while for rural subjects, PEs including PE(16:0/16:0) and PE(18:0/18:2) showed significant mediating effects. Our findings underscore the impact of PM2.5 exposure on lipid profiles and BP risk in children, suggesting area-specific mechanisms and the potential for lipidomic-based interventions to mitigate environmental health risks. © 2025 Elsevier Inc.

关键词： health risks

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Multimorbidity and mortality among older patients with coronary heart disease in shenzhen,China

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Journal of Geriatric Cardiology 2024年第1期21卷 81-89页

作者： Fu-Rong LI Shuang WANG Xia LI Zhi-Yuan CHENG Cheng JIN Chun-Bao MO Jing ZHENG Feng-Chao LIANG Dong-Feng GU Shenzhen Key Laboratory of Cardiovascular Health and Precision Medicine Southern University of Science and TechnologyShenzhenGuangdongChina School of Public Health and Emergency Management Southern University of Science and TechnologyShenzhenGuangdongChina Shenzhen Health Development Research and Data Management Center ShenzhenChina School of Medicine Southern University of Science and TechnologyShenzhenGuangdongChina

BACKGROUND The current understanding of the magnitude and consequences of multimorbidity in Chinese older adults with coronary heart disease(CHD)is *** aimed to assess the association and population-attributable fractions(PAFs)between multimorbidity and mortality among hospitalized older patients who were diagnosed with CHD in shenzhen,*** We conducted a retrospective cohort study of older Chinese patients(aged≥65 years)who were diagnosed with *** proportional hazards models were used to estimate the associations between multimorbidity and all-cause and cardiovascular disease(CVD)*** also calculated the *** The study comprised 76,455 older hospitalized patients who were diagnosed with CHD between January 1,2016,and August 31,*** them,70,217(91.9%)had multimorbidity,defined as the presence of at least one of the predefined 14 chronic *** with cancer,hemorrhagic stroke and chronic liver disease had the worst overall death risk,with adjusted HRs(95%CIs)of 4.05(3.77,4.38),2.22(1.94,2.53),and 1.85(1.63,2.11),*** CVD mortality,the highest risk was observed for hemorrhagic stroke,ischemic stroke,and chronic kidney disease;the corresponding adjusted HRs(95%CIs)were 3.24(2.77,3.79),1.91(1.79,2.04),and 1.81(1.64,1.99),***-cause mortality was mostly attributable to cancer,heart failure and ischemic stroke,with PAFs of 11.8,10.2,and 9.1,*** for CVD mortality,the leading PAFs were heart failure,ischemic stroke and diabetes;the corresponding PAFs were 18.0,15.7,and 6.1,*** Multimorbidity was common and had a significant impact on mortality among older patients with CHD in shenzhen,***,heart failure,ischemic stroke and diabetes are the primary contributors to ***,prioritizing improved treatment and management of these comorbidities is essential for the survival prognosis of CHD patients from a holistic public health perspective.

关键词： patients morbidity mortality

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