The guidelines for clinical research on new drugs provide unified standards for drug developers, researchers, and regulatory authorities, playing a crucial role in new drug development. This article systematically reviews the evolution of guidelines for clinical research on new traditional Chinese medicine(TCM) drugs in China, with a focus on analyzing the current status of these guidelines and the problems that exist. It also provides interpretations of three important guidelines. The article points out that with the continuous emergence of new clinical trial design methods, development concepts, and tools, and under the background of the "three combinations" evidence evaluation system for new TCM drugs, it is imperative to revise existing guidelines, formulate new ones, and develop new tools for clinical efficacy evaluation. It is hoped that relevant departments will adopt an open attitude and work together to build a technical system of clinical research guidelines for new TCM drugs that aligns with the characteristics of TCM.
Humans ; Biomedical Research/trends* ; China ; Clinical Trials as Topic ; Drugs, Chinese Herbal/therapeutic use* ; Guidelines as Topic ; Medicine, Chinese Traditional/standards*

The objective of this study is to map the global scientific competitive landscape in the field of artificial intelligence (AI) medical devices using scientific data. A bibliometric analysis was conducted using the Web of Science Core Collection to examine global research trends in AI-based medical devices. As of the end of 2023, a total of 55 147 relevant publications were identified worldwide, with 76.6% published between 2018 and 2024. Research in this field has primarily focused on AI-assisted medical image and physiological signal analysis. At the national level, China (17 991 publications) and the United States (14 032 publications) lead in output. China has shown a rapid increase in publication volume, with its 2023 output exceeding twice that of the U.S.; however, the U.S. maintains a higher average citation per paper (China: 16.29; U.S.: 35.99). At the institutional level, seven Chinese institutions and three U.S. institutions rank among the global top ten in terms of publication volume. At the researcher level, prominent contributors include Acharya U Rajendra, Rueckert Daniel and Tian Jie, who have extensively explored AI-assisted medical imaging. Some researchers have specialized in specific imaging applications, such as Yang Xiaofeng (AI-assisted precision radiotherapy for tumors) and Shen Dinggang (brain imaging analysis). Others, including Gao Xiaorong and Ming Dong, focus on AI-assisted physiological signal analysis. The results confirm the rapid global development of AI in the medical device field, with "AI + imaging" emerging as the most mature direction. China and the U.S. maintain absolute leadership in this area-China slightly leads in publication volume, while the U.S., having started earlier, demonstrates higher research quality. Both countries host a large number of active research teams in this domain.
Artificial Intelligence ; Bibliometrics ; Humans ; China ; Equipment and Supplies ; United States ; Biomedical Research

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.
China ; Humans ; Biomedical Research ; Data Management ; Data Collection

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.
Deep Learning ; Biological Ontologies ; Humans ; Big Data ; Biomedical Research

With the rise of data-intensive research, data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence (AI) readiness. In the biomedical domain, data are characterized by high complexity and privacy sensitivity, calling for robust and systematic data management skills. This paper reviews current trends in scientific data governance and the evolving policy landscape, highlighting persistent challenges such as inconsistent standards, semantic misalignment, and limited awareness of compliance. These issues are largely rooted in the lack of structured training and practical support for researchers. In response, this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive, lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness. Furthermore, it outlines a tiered training strategy tailored to different research stages-undergraduate, graduate, and professional, offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.
Artificial Intelligence ; Humans ; Biomedical Research

BACKGROUND: Supporting the mental health of researchers is essential to maintaining human resources and advancing science. This study investigated the association between Japanese medical researchers' perceptions of research evaluation processes and their psychological well-being. METHODS: We performed a web-based self-administered questionnaire survey. The questionnaires were distributed to each academic society through the Japanese Association of Medical Sciences from December 2022 to January 2023. These questionnaires targeted medical researchers. Exposure was the medical researchers' perceptions of quantitative indicators for evaluating medical research and researchers. The outcome was psychological well-being, measured using the Japanese version of the World Health Organization-Five Well-Being Index (WHO-5). Multivariable-adjusted logistic regressions were conducted to investigate the association between individual attitudes toward research evaluation and psychological well-being. Stratified analyses by research fields, i.e., clinical, basic, and social medicine, were also performed. RESULTS: A total of 3,139 valid responses were collected. After excluding 176 responses from research fields of other than clinical, basic, or social medicine, 2,963 researchers (2,185 male, 737 female, and 41 other) were analyzed. Prevalence of poor well-being (WHO-5 score <13) was 28.3% in the researchers. The highest number of medical researchers was in clinical medicine (n = 500) followed by basic medicine (n = 217) and social medicine (n = 121). Medical researchers who considered research funding slightly important/not important for researcher evaluation had poorer psychological well-being than those who considered it especially important (slightly important: adjusted odds ratio (aOR) 1.33, 95% confidence interval (CI) 1.03-1.71; not important: aOR 1.53, 95%CI 1.10-2.12). This tendency was stronger among basic medical researchers than clinical or social medical researchers. The research field significantly modified the relationship between research funding received and interaction with poor psychological well-being both additively (P = 0.030) and multiplicatively (P = 0.024). CONCLUSIONS The discrepancy between medical researchers' attitudes toward research evaluation and the current state of research evaluation in their research community may worsen their psychological well-being. The influence of this discrepancy differs among clinical, basic, and social medicine. Appropriate evaluation of medical research and researchers in each field can facilitate improving their psychological well-being via the resolution of this discrepancy.
Humans ; Japan ; Female ; Male ; Cross-Sectional Studies ; Adult ; Research Personnel/statistics & numerical data* ; Middle Aged ; Biomedical Research ; Surveys and Questionnaires ; Mental Health ; Psychological Well-Being ; East Asian People

As representatives of the third generation of biomedical materials, hydrogels exhibit revolutionary potential in tissue engineering, precision drug delivery, and smart medical devices due to their ability to construct bionic microenvironments. However, the clinical translation of hydrogels is still limited by multidimensional challenges, including biocompatibility, scalable production, and regulatory complexity. This paper systematically reviews the design innovations, functionalization strategies, and translational bottlenecks of hydrogel materials, integrates the latest technological trends, such as 4D printing and AI-driven design, and proposes a collaborative optimization pathway encompassing materials, technology, clinical applications, and policy. By introducing local Chinese innovation cases and monitoring scientific advancements, this study offers solutions that possess both academic significance and practical guidance for the clinical translation of hydrogels.
Hydrogels ; Tissue Engineering ; Translational Research, Biomedical ; Biocompatible Materials ; Humans ; Drug Delivery Systems

OBJECTIVE: To explore the key target molecules and potential mechanisms of oridonin against non-small cell lung cancer (NSCLC). METHODS: The target molecules of oridonin were retrieved from SEA, STITCH, SuperPred and TargetPred databases; target genes associated with the treatment of NSCLC were retrieved from GeneCards, DisGeNET and TTD databases. Then, the overlapping target molecules between the drug and the disease were identified. The protein-protein interaction (PPI) was constructed using the STRING database according to overlapping targets, and Cytoscape was used to screen for key targets. Molecular docking verification were performed using AutoDockTools and PyMOL software. Using the DAVID database, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted. The impact of oridonin on the proliferation and apoptosis of NSCLC cells was assessed using cell counting kit-8, cell proliferation EdU image kit, and Annexin V-FITC/PI apoptosis kit respectively. Moreover, real-time quantitative PCR and Western blot were used to verify the potential mechanisms. RESULTS: Fifty-six target molecules and 12 key target molecules of oridonin involved in NSCLC treatment were identified, including tumor protein 53 (TP53), Caspase-3, signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase kinase 8 (MAPK8), and mammalian target of rapamycin (mTOR). Molecular docking showed that oridonin and its key target molecules bind spontaneously. GO and KEGG enrichment analyses revealed cancer, apoptosis, phosphoinositide-3 kinase/protein kinase B (PI3K/Akt), and other signaling pathways. In vitro experiments showed that oridonin inhibited the proliferation, induced apoptosis, downregulated the expression of Bcl-2 and Akt, and upregulated the expression of Caspase-3. CONCLUSION Oridonin can act on multiple targets and pathways to exert its inhibitory effects on NSCLC, and its mechanism may be related to upregulating the expression of Caspase-3 and downregulating the expressions of Akt and Bcl-2.
Diterpenes, Kaurane/chemistry* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Humans ; Network Pharmacology ; Lung Neoplasms/pathology* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Cell Line, Tumor ; Signal Transduction/drug effects* ; Gene Expression Regulation, Neoplastic/drug effects* ; Reproducibility of Results ; Gene Ontology

OBJECTIVES: Hepatocellular carcinoma is one of the most common primary malignant tumors with the third highest mortality rate worldwide. This study aims to identify key genes associated with hepatocellular carcinoma prognosis using the Gene Expression Omnibus (GEO) database and provide a theoretical basis for discovering novel prognostic biomarkers for hepatocellular carcinoma. METHODS: Hepatocellular carcinoma-related datasets were retrieved from the GEO database. Differentially expressed genes (DEGs) were identified using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and key genes were identified using Cytoscape software. The University of Alabama at Birmingham Cancer Data Analysis Resource (UALCAN) was used to analyze the expression levels of key genes in normal and hepatocellular carcinoma tissues, as well as their associations with pathological grade, clinical stage, and patient survival. The Human Protein Atlas (THPA) was used to further validate the impact of key genes on overall survival. Expression levels of key genes in the blood of hepatocellular carcinoma patients were evaluated using the expression atlas of blood-based biomarkers in the early diagnosis of cancers (BBCancer). RESULTS: A total of 78 DEGs were identified from the GEO database. GO and KEGG analyses indicated that these genes may contribute to hepatocellular carcinoma progression by promoting cell division and regulating protein kinase activity. Sixteen key genes were screened via Cytoscape and validated using UALCAN and THPA. These genes were overexpressed in hepatocellular carcinoma tissues and were associated with disease progression and poor prognosis. Finally, BBCancer analysis showed that ASPM and NCAPG were also elevated in the blood of hepatocellular carcinoma patients. CONCLUSIONS This study identified 16 key genes as potential prognostic biomarkers for hepatocellular carcinoma, among which ASPM and NCAPG may serve as promising blood-based markers for hepatocellular carcinoma.
Humans ; Carcinoma, Hepatocellular/mortality* ; Liver Neoplasms/pathology* ; Prognosis ; Computational Biology/methods* ; Protein Interaction Maps/genetics* ; Biomarkers, Tumor/genetics* ; Gene Expression Regulation, Neoplastic ; Gene Expression Profiling ; Gene Ontology ; Databases, Genetic

OBJECTIVES: Obtaining informed consent from research participants is an ethical and legal obligation for medical researchers in clinical studies. Electronic informed consent (eIC) is increasingly being adopted in clinical research worldwide. However, there is limited data on Chinese medical researchers' knowledge and attitudes toward eIC. This study aims to investigate their knowledge, attitudes, and influencing factors regarding eIC use in clinical research. METHODS: This cross-sectional study was conducted using stratified random sampling. From June to August 2022, medical researchers from 8 tertiary hospitals were surveyed via an online platform (Wenjuanxing). A self-developed eIC knowledge questionnaire and attitude scale were used to assess participants' understanding and perceptions of eIC. Univariate analysis was employed to explore factors influencing attitude scores and the correlation between knowledge and attitudes. A generalized linear model was used to analyze associations between demographic characteristics and attitude scores, including the frequency of difficulties in using smartphones or computers, preferred device for using eIC, and their interaction effects. Stratified analysis was further performed for significant interactions. RESULTS: A total of 399 valid questionnaires were collected. The mean accuracy rate on the eIC knowledge questionnaire was (94.88±15.50)%. Of the respondents, 74.9% had heard of eIC, and 84.5% preferred using mobile devices over computers to access eIC. The median attitude score was 3.41 (3.18, 3.76), indicating generally positive attitudes. Specifically, 81.7% found eIC more convenient than paper-based consent, 79.7% considered it more efficient, and 51.1% believed it could fully replace paper forms. However, 60.7% expressed concerns about data security and privacy, and 89.7% believed that relevant laws and regulations need improvement. Spearman correlation analysis showed a weak positive correlation between knowledge and attitude scores (r=0.171, P=0.001). Univariate analysis indicated that the frequency of difficulty using devices and preferred device for eIC were significantly associated with attitude scores (P<0.05). After adjusting for confounding factors, the generalized linear model demonstrated that participants who occasionally experienced had difficulty using devices had significantly lower attitude scores compared to those who never had difficulty (β=-0.040, 95% CI -0.071 to -0.009, P=0.012). Those who preferred using PCs had significantly lower attitude scores than those who preferred mobile devices (β=-0.066, 95% CI -0.108 to -0.023, P=0.002). Interaction analysis showed a significant interaction analysis showed a significant interaction between age and preferred device (P=0.011), particularly among participants aged ≥45-year (P<0.001). No other interactions were found to be significant (all P>0.05). CONCLUSIONS Medical researchers in China generally have a high level of knowledge and positive attitudes toward eIC, though concerns remain regarding data security and privacy. Future promotion of eIC in Chinese clinical research should be grounded in ethical considerations and address the specific needs of older users and mobile device users, while also enhancing researchers' competencies in using digital tools and eIC systems.
Humans ; Cross-Sectional Studies ; Informed Consent ; Surveys and Questionnaires ; Female ; Male ; Health Knowledge, Attitudes, Practice ; Adult ; Biomedical Research ; Research Personnel/psychology* ; Middle Aged ; China