Chinese Medical Sciences Journal最新文献

{"title":"scPANDA: PAN-Blood Data Annotator with a 10-Million Single-Cell Atlas","authors":"Chang-Xiao Li,&nbsp;Can Huang,&nbsp;Dong-Sheng Chen","doi":"10.24920/004472","DOIUrl":"10.24920/004472","url":null,"abstract":"<div><h3>Objective</h3><div>Recent advancements in single-cell RNA sequencing (scRNA-seq) have revolutionized the study of cellular heterogeneity, particularly within the hematological system. However, accurately annotating cell types remains challenging due to the complexity of immune cells. To address this challenge, we develop a PAN-blood single-cell Data Annotator (scPANDA), which leverages a comprehensive 10-million-cell atlas to provide precise cell type annotation.</div></div><div><h3>Methods</h3><div>The atlas, constructed from data collected in 16 studies, incorporated rigorous quality control, preprocessing, and integration steps to ensure a high-quality reference for annotation. scPANDA utilizes a three-layer inference approach, progressively refining cell types from broad compartments to specific clusters. Iterative clustering and harmonization processes were employed to maintain cell type purity throughout the analysis. Furthermore, the performance of scPANDA was evaluated in three external datasets.</div></div><div><h3>Results</h3><div>The atlas was structured hierarchically, consisting of 16 compartments, 54 classes, 4,460 low-level clusters (<em>pd_cc_cl_tfs</em>), and 611 high-level clusters (<em>pmid_cts</em>). Robust performance of the tool was demonstrated in annotating diverse immune scRNA-seq datasets, analyzing immune-tumor coexisting clusters in renal cell carcinoma, and identifying conserved cell clusters across species.</div></div><div><h3>Conclusion</h3><div>scPANDA exemplifies effective reference mapping with a large-scale atlas, enhancing the accuracy and reliability of blood cell type identification.</div></div>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":"40 1","pages":"Pages 68-87"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enrichment Analysis and Deep Learning in Biomedical Ontology: Applications and Advancements","authors":"Hong-Yu Fu,&nbsp;Yang-Yang Liu,&nbsp;Mei-Yi Zhang,&nbsp;Hai-Xiu Yang","doi":"10.24920/004464","DOIUrl":"10.24920/004464","url":null,"abstract":"<div><div>Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.</div></div>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":"40 1","pages":"Pages 45-56"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diversity, Complexity, and Challenges of Viral Infectious Disease Data in the Big Data Era: A Comprehensive Review","authors":"Yun Ma ,&nbsp;Lu-Yao Qin ,&nbsp;Xiao Ding ,&nbsp;Ai-Ping Wu","doi":"10.24920/004461","DOIUrl":"10.24920/004461","url":null,"abstract":"<div><div>Viral infectious diseases, characterized by their intricate nature and wide-ranging diversity, pose substantial challenges in the domain of data management. The vast volume of data generated by these diseases, spanning from the molecular mechanisms within cells to large-scale epidemiological patterns, has surpassed the capabilities of traditional analytical methods. In the era of artificial intelligence (AI) and big data, there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information. Despite the rapid accumulation of data associated with viral infections, the lack of a comprehensive framework for integrating, selecting, and analyzing these datasets has left numerous researchers uncertain about which data to select, how to access it, and how to utilize it most effectively in their research. This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels, from the molecular details of pathogens to broad epidemiological trends. The scope extends from the micro-scale to the macro-scale, encompassing pathogens, hosts, and vectors. In addition to data summarization, this review thoroughly investigates various dataset sources. It also traces the historical evolution of data collection in the field of viral infectious diseases, highlighting the progress achieved over time. Simultaneously, it evaluates the current limitations that impede data utilization. Furthermore, we propose strategies to surmount these challenges, focusing on the development and application of advanced computational techniques, AI-driven models, and enhanced data integration practices. By providing a comprehensive synthesis of existing knowledge, this review is designed to guide future research and contribute to more informed approaches in the surveillance, prevention, and control of viral infectious diseases, particularly within the context of the expanding big-data landscape.</div></div>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":"40 1","pages":"Pages 29-44"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strengthening Biomedical Big Data Management and Unleashing the Value of Data Elements","authors":"Wei Zhou,&nbsp;Jing-Chen Zhang,&nbsp;De-Pei Liu","doi":"10.24920/004471","DOIUrl":"10.24920/004471","url":null,"abstract":"","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":"40 1","pages":"Pages 1-2"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Spaces in Medicine and Health: Technologies, Applications, and Challenges","authors":"Wan-Fei Hu,&nbsp;Si-Zhu Wu,&nbsp;Qing Qian","doi":"10.24920/004466","DOIUrl":"10.24920/004466","url":null,"abstract":"<div><div>Data space, as an innovative data management and sharing model, is emerging in the medical and health sectors. This study expounds on the conceptual connotation of data space and delineates its key technologies, including distributed data storage, standardization and interoperability of data sharing, data security and privacy protection, data analysis and mining, and data space assessment. By analyzing the real-world cases of data spaces within medicine and health, this study compares the similarities and differences across various dimensions such as purpose, architecture, data interoperability, and privacy protection. Meanwhile, data spaces in these fields are challenged by the limited computing resources, the complexities of data integration, and the need for optimized algorithms. Additionally, legal and ethical issues such as unclear data ownership, undefined usage rights, risks associated with privacy protection need to be addressed. The study notes organizational and management difficulties, calling for enhancements in governance framework, data sharing mechanisms, and value assessment systems. In the future, technological innovation, sound regulations, and optimized management will help the development of the medical and health data space. These developments will enable the secure and efficient utilization of data, propelling the medical industry into an era characterized by precision, intelligence, and personalization.</div></div>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":"40 1","pages":"Pages 18-28"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formula-S: Situated Visualization for Traditional Chinese Medicine Formula Learning","authors":"Zhi-Yue Wu ,&nbsp;Su-Yuan Peng ,&nbsp;Yan Zhu ,&nbsp;Liang Zhou","doi":"10.24920/004462","DOIUrl":"10.24920/004462","url":null,"abstract":"<div><h3>Objective</h3><div>The study of medicine formulas is a core component of traditional Chinese medicine (TCM), yet traditional learning methods often lack interactivity and contextual understanding, making it challenging for beginners to grasp the intricate composition rules of formulas. To address this gap, we introduce Formula-S, a situated visualization method for TCM formula learning in augmented reality (AR) and evaluate its performance. This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.</div></div><div><h3>Methods</h3><div>Formula-S is an interactive AR tool designed for TCM formula learning, featuring three modes (3D, Web, and Table). The dataset included TCM formulas and herb properties extracted from authoritative references, including textbook and the SymMap database. In Formula-S, the hierarchical visualization of the formulas as herbal medicine compositions, is linked to the multidimensional herb attribute visualization and embedded in the real world, where real herb samples are presented. To evaluate its effectiveness, a controlled study (<em>n</em>=30) was conducted. Participants who had no formal TCM knowledge were tasked with herbal medicine identification, formula composition, and recognition. In the study, participants interacted with the AR tool through HoloLens 2. Data were collected on both task performance (accuracy and response time) and user experience, with a focus on task efficiency, accuracy, and user preference across the different learning modes.</div></div><div><h3>Results</h3><div>The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks. Regarding user experience, our new approach demonstrated the highest system usability and lowest task load, effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency. Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment, suggesting this approach offers usability advantages for TCM education.</div></div><div><h3>Conclusions</h3><div>The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods. Additionally, it provides superior contextual understanding of TCM formulas, making it a promising new solution for TCM learning.</div></div>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":"40 1","pages":"Pages 57-67"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biomedical Data in China: Policy, Accumulation, Platform Construction, and Applications","authors":"Jing-Chen Zhang ,&nbsp;Jing-Wen Sun ,&nbsp;Xiao-Meng Liu ,&nbsp;Jin-Yan Liu ,&nbsp;Wei Luo ,&nbsp;Sheng-Fa Zhang ,&nbsp;Wei Zhou","doi":"10.24920/004445","DOIUrl":"10.24920/004445","url":null,"abstract":"<div><div>Biomedical data is surging due to technological innovations and integration of multidisciplinary data, posing challenges to data management. This article summarizes the policies, data collection efforts, platform construction, and applications of biomedical data in China, aiming to identify key issues and needs, enhance the capacity-building of platform construction, unleash the value of data, and leverage the advantages of China's vast amount of data.</div></div>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":"40 1","pages":"Pages 9-17"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Value of Myocardial Strain in Monitoring Fluorouracil-Based Chemotherapy-Related Cardiac Dysfunction in Gastrointestinal Cancer Patients","authors":"Wei Yang ,&nbsp;Jian-Xia Yang ,&nbsp;Jing-Yuan Guan ,&nbsp;Wu-Yun Bao ,&nbsp;Mei Zhang","doi":"10.24920/004387","DOIUrl":"10.24920/004387","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the predictive value of myocardial strain for cardiotoxicity associated with fluorouracil-based chemotherapies in gastrointestinal cancer patients.</div></div><div><h3>Methods</h3><div>Patients with diagnosis of gastrointestinal cancers, who were hospitalized for chemotherapy involving antimetabolic drugs, were eligible in this prospective study. Echocardiography was performed before and after each chemotherapy cycle during hospitalization until the completion of chemotherapy. Cancer therapy-related cardiac dysfunction (CTRCD) was identified if there was a decrease in left ventricular ejection fraction (LVEF) by at least 5% to an absolute value of &lt; 53% from the baseline, accompanied by symptoms or signs of heart failure; or a decrease in LVEF of at least 10% to an absolute value of &lt; 53% from the baseline, without symptoms or signs of heart failure. Subclinical cardiac impairment is defined as a decrease in the left ventricular global longitudinal strain (GLS) of at least 15% from baseline. Clinical data and myocardial strain variables were collected. Changes of echocardiographic indexes after chemotherapy at each cycle were observed and compared to those of pre-chemotherapy. Cox regression analysis was used to determine the associated indexes to CTRCD, and receiver operating characteristic (ROC) curves were plotted for evaluation of their predicting efficacy.</div></div><div><h3>Results</h3><div>Fifty-one patients completed 4 cycles of chemotherapy and were enrolled in the study analysis. LVEF, GLS, GLS epicardium (GLS-epi), and GLS endocardium (GLS-endo) were decreased after the 4 cycles of chemotherapy. Throughout the chemotherapy period, 6 patients (11.8%) progressed to CTRCD. The Cox regression analysis revealed that the change in left atrial ejection fraction (LAEF) and LAS during the reservoir (LASr) phase after the first cycle of chemotherapy (C1v-LAEF and C1v-LASr, respectively) were significantly associated with the development of CTRCD [C1v-LAEF (<em>HR</em>=1.040; 95%<em>CI</em>: 1.000-1.082; <em>P</em>=0.047); C1v- LASr (<em>HR</em>=1.024; 95%<em>CI</em>: 1.000-1.048; <em>P</em>=0.048)]. The sensitivity and specificity were 50.0% and 93.3%, respectively, for C1v-LAEF predicting CTRCD when C1v-LAEF &gt; 19.68% was used as the cut-off value, and were 66.7% and 75.6%, respectively, for C1v-LASr predicting CTRCD when C1v-LASr &gt; 14.73% was used as the cut-off value. The areas under the ROC curve (AUC) for C1v-LAEF and C1v-LASr predicting CTRCD were 0.694 and 0.707, respectively.</div></div><div><h3>Conclusion</h3><div>GLS changes among patients with subclinical impairment of cardiac function who were treated with fluorouracil-based chemotherapies, and C1v-LAEF and C1v-LASr of the left atrium are early predictors of cardiac function deterioration.</div></div>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":"39 4","pages":"Pages 273-281"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Signature Combing Cuproptosis- and Ferroptosis-Related Genes in Nonalcoholic Fatty Liver Disease","authors":"Rou-Rou Fang ,&nbsp;Qi-Fan Yang ,&nbsp;Jing Zhao ,&nbsp;Shou-Zhu Xu","doi":"10.24920/004403","DOIUrl":"10.24920/004403","url":null,"abstract":"<div><h3>Objective</h3><div>To identify cuproptosis- and ferroptosis-related genes involved in nonalcoholic fatty liver disease and to determine the diagnostic value of hub genes.</div></div><div><h3>Methods</h3><div>The gene expression dataset GSE89632 was retrieved from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between the non-alcoholic steatohepatitis (NASH) group and the healthy group using the ‘limma’ package in R software and weighted gene co-expression network analysis. Gene ontology, kyoto encyclopedia of genes and genomes pathway, and single-sample gene set enrichment analyses were performed to identify functional enrichment of DEGs. Ferroptosis- and cuproptosis-related genes were obtained from the FerrDb V2 database and available literatures, respectively. A combined signature for cuproptosis- and ferroptosis-related genes, called CRF, was constructed using the STRING database. Hub genes were identified by overlapping DEGs, WGCNA-derived key genes, and combined signature CRF genes, and validated using the GSE109836 and GSE227714 datasets and real-time quantitative polymerase chain reaction. A nomogram of NASH diagnostic model was established utilizing the ‘rms’ package in R software based on the hub genes, and the diagnostic value of hub genes was assessed using receiver operating characteristic curve analysis. In addition, immune cell infiltration in NASH <em>versus</em> healthy controls was examined using the CIBERSORT algorithm. The relationships among various infiltrated immune cells were explored with Spearman's correlation analysis.</div></div><div><h3>Results</h3><div>Analysis of GSE89632 identified 236 DEGs between the NASH group and the healthy group. WGCNA highlighted 8 significant modules and 11,095 pivotal genes, of which 330 genes constituted CRF. Intersection analysis identified <em>IL</em>6, <em>IL</em>1<em>B</em>, <em>JUN</em>, <em>NR</em>4<em>A</em>1, and <em>PTGS</em>2 as hub genes. The hub genes were all downregulated in the NASH group, and this result was further verified by the NASH validation dataset and real-time quantitative polymerase chain reaction. Receiver operating characteristic curve analysis confirmed the diagnostic efficacy of these hub genes with areas under the curve of 0.985, 0.941, 1.000, 0.967, and 0.985, respectively. Immune infiltration assessment revealed that gamma delta T cells, M1 macrophages, M2 macrophages, and resting mast cells were predominantly implicated.</div></div><div><h3>Conclusion</h3><div>Our investigation underscores the significant association of cuproptosis- and ferroptosis-related genes, specifically <em>IL</em>6, <em>IL</em>1<em>B</em>, <em>JUN</em>, <em>NR</em>4<em>A</em>1, and <em>PTGS</em>2, with NASH. These findings offer novel insights into the pathogenesis of NASH, potentially guiding future diagnostic and therapeutic strategies.</div></div>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":"39 4","pages":"Pages 261-272"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current Status and Future Suggestions for Innovative Drug Research and Development in China","authors":"Shu-Xia Liu,&nbsp;Yan Wang,&nbsp;Shuang Yang,&nbsp;Ya-Qing Wang,&nbsp;Mei-Mei Xu","doi":"10.24920/004363","DOIUrl":"10.24920/004363","url":null,"abstract":"<div><div>In recent years, with the policy reform of new drug review and approval, China has ushered in a surge of innovative drug development. Based on the Pharnexcloud database and the Pharmcube database, we analyzed the innovative drugs approved for marketing and entered clinical trials in China, sorted out the major research and development (R&amp;D) institutions and enterprises, the distribution of innovative drug target types, and the primary therapeutic areas of the approved innovative drugs, and compared with the global innovative drug R&amp;D landscape. Since 2020, China's innovative drug development has shown a rapid growth trend, with intense competition among biopharmaceutical companies, and the emergence of a number of leading biopharmaceutical enterprises. Popular targets for clinical-stage and approved drugs include protein tyrosine kinase, epidermal growth factor receptor, vascular endothelial growth factor receptor, and others. Oncological diseases are the most active domain for new drug R&amp;D in China, followed by infectious diseases and neurological diseases. Suggestions for future development are proposed to increase policy support, enhance R&amp;D innovation investment, optimize pipeline layout, strengthen international cooperation, and focus on innovative targets. This review provides a reference for pharmaceutical R&amp;D enterprises, scientific researchers, and government administrators.</div></div>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":"39 4","pages":"Pages 288-296"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}