OBJECTIVE: To provide the experience and demonstration for the transformation of acupuncture-moxibustion techniques standards from Chinese national standards to international standards. METHODS: Questionnaire research, literature research, semi-structured interviews and expert consultation were used. RESULTS: The safety of acupuncture-moxibustion techniques was evaluated through literature research, and based on the results of the questionnaire survey, expert interviews, and expert consultation, 11 main bodies and structure of the former Chinese national standard, Technical Benchmark of Acupuncture and Moxibustion: General Rules for Drafting, were adjusted and optimized in accordance with the requirements of international standard (including the language, normative references, purpose, scope, applicable environment, target population, work team, terms and definitions, general principles and basic requirements, structural elements and text structure, and compilation process); and the first international standard, World Federation of Acupuncture-Moxibustion Societis (WFAS) Technical Benchmark of Acupuncture and Moxibustion: General Rules for Drafting was formulated to specify the general rules for drafting. CONCLUSION The 3 key questions, "international compatibility", "technical operability" and "safety" should be solved technically on the basis of explicit international requirements. It is the core technical issue during transforming the national standards of technical benchmark of acupuncture and moxibustion into international standards.
Moxibustion/methods* ; Acupuncture Therapy/methods* ; Humans ; Translational Research, Biomedical/standards* ; Surveys and Questionnaires ; China ; Benchmarking/standards*

The high failure rates in clinical drug development based on animal models highlight the urgent need for more representative human models in biomedical research. In response to this demand, organoids and organ chips were integrated for greater physiological relevance and dynamic, controlled experimental conditions. This innovative platform-the organoids-on-a-chip technology-shows great promise in disease modeling, drug discovery, and personalized medicine, attracting interest from researchers, clinicians, regulatory authorities, and industry stakeholders. This review traces the evolution from organoids to organoids-on-a-chip, driven by the necessity for advanced biological models. We summarize the applications of organoids-on-a-chip in simulating physiological and pathological phenotypes and therapeutic evaluation of this technology. This section highlights how integrating technologies from organ chips, such as microfluidic systems, mechanical stimulation, and sensor integration, optimizes organoid cell types, spatial structure, and physiological functions, thereby expanding their biomedical applications. We conclude by addressing the current challenges in the development of organoids-on-a-chip and offering insights into the prospects. The advancement of organoids-on-a-chip is poised to enhance fidelity, standardization, and scalability. Furthermore, the integration of cutting-edge technologies and interdisciplinary collaborations will be crucial for the progression of organoids-on-a-chip technology.
Organoids/physiology* ; Humans ; Biomedical Research/methods* ; Lab-On-A-Chip Devices ; Animals ; Microphysiological Systems

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

INTRODUCTION

For female nursing students in Vietnam, juggling work, school, and personal obligations can be especially difficult. Research on how these students develop resilience while juggling their multiple roles is still lacking, despite the fact that their numbers in nursing school are increasing. This study explores how the work-life-study balance (WLSB) of female students pursuing an Associate Degree in Nursing (ADN) to a Bachelor of Science in Nursing (BSN) program is shaped by their real-life experiences and sociocultural influences.

A qualitative research design informed by interpretative phenomenological analysis (IPA) was used in this study. In September 2024, ten carefully selected female nursing students participated in semi-structured interviews at a private university in Ho Chi Minh City. From October 2024 to February 2025, each 45–60 minute interview was subjected to a thematic analysis using Delve software.

The challenges faced by the participants, along with their support systems, coping strategies, and aspirations, were captured in four key themes, each with its own set of sub-themes. The first theme, Navigating Life's Crossroads: The Struggle for Balance, highlighted the students' struggles to manage competing demands, featuring subthemes, Pulled in All Directions, Time as a Scarce Commodity, and Compromises and Sacrifices. The second theme, Anchors in the Storm: Finding Strength in Support, emphasized the vital role of relational support, showcasing subthemes, Peer Solidarity and Shared Struggles, and Family as a Pillar of Strength. The third theme, Pathways to Resilience: Strategies for Survival, focused on coping strategies and adaptive techniques, incorporating subtheme, Faith and Inner Strength, Embracing the Role of a Working Learner, and Prioritizing and Organizing. Finally, the last theme, Purpose, Aspiration, and Future Orientation, brought attention to the participants' sources of motivation and their optimistic outlook, with subthemes, Motivation Rooted in Family and Self and Hope and Optimism as Sustaining Forces. These findings, grounded in the Transformative Resilience Model, illustrate how students harness their inner drive, familial and social responsibilities, and cultural values to adapt and thrive in the face of challenges. To maintain their dedication to education and uplift their families, participants leaned on hope, spiritual insights, and a sense of agency, viewing their struggles as meaningful experiences.

The experiences of Vietnamese female nursing students reveal a remarkable resilience shaped by both heavy social expectations and personal challenges. Drawing from the Transformative Resilience Model, this study highlights how facing and overcoming adversity can lead to significant identity development and personal growth. Institutional support plays a crucial role in enhancing a student's well-being, which can include flexible academic policies, accessible mental health services, and adaptable work-study options. Financial pressures, job-related stress, and academic demands often contribute to burnout. These findings underscore the urgent need for systemic, collaborative efforts to foster inclusive and sustainable learning environments for nursing students who are balancing work and study.

Human ; Education, Continuing ; Nursing Research

This reflective paper explored the philosophical foundations of lifelong learning and impactful research in the field of nursing. Anchored in personal experience and supported by scholarly literature, it illustrated the transformative power of continuous learning, the cultivation of research competence, and the moral responsibility of contributing meaningfully to society. A nurse researcher's journey is not defined by awards or accomplishment but by an unwavering dedication to knowledge creation, community involvement, and evidence-based practice. The "emptying one's cup" metaphor embodies intellectual humility, a mindset that keeps the mind open to learning, self-improvement, and meaningful service throughout one's career.