Background While the repair and regeneration of cartilage injuries remain a significant research challenge, the relationship between subchondral bone and stem cells has emerged as a new focus. However, a bibliometric analysis of the research trends in this specific area is currently lacking. Methods We searched Web of Science Core Collection (WOSCC), Scopus, and PubMed for all relevant literature on the subchondral bone-stem cell relationship from database inception to September 3, 2025. CiteSpace was used to visualize annual publication counts, authors, institutions, countries, co-citations (authors, journals, references), and keywords. Results A total of 1,267 publications were included. Research on the subchondral bone-stem cell relationship is an emerging focus. The most prolific author was Zhang Wei from China (15 publications). The institution with the most publications was the Chinese Academy of Sciences (52 publications), which also had the highest centrality (0.19). The most highly-cited author was ZHEN GH. AM J Sport Med was the most highly-cited journal and had the highest centrality (0.31). Keyword analysis showed “mesenchymal stem cells” as the most frequent term, while “cartilage” had the highest keyword centrality (0.27). The top 10 largest keyword clusters were #0 monosodium iodoacetate, #1 chitosan, #2 osteochondral defects, #3 scatter factor, #4 silk fibroin, #5 tissue engineering, #6 edetic acid, #7 microgels, #8 knee, and #9 cartilage repair, which were emerging research fronts. Conclusion Research on the subchondral bone-stem cell relationship is an emerging focus. Consistent hotspots include cartilage and bone repair, cell therapy and tissue engineering, animal models and experiments, as well as transplantation and in vitro - studies. Simultaneously, advances in technology and deeper research are establishing in vivo studies, real-time monitoring techniques, and the investigation of specific factors and signaling pathways as new research hotspots.

The 2025 6th International Conference on Material Chemistry and Composite Materials (MCCM 2025) was successfully held in Guangzhou, China, from December 12 to 14, 2025. The conference attracted researchers, scholars, and industry professionals from around the world, reflecting the growing global interest in material chemistry, composite technologies, and their expanding applications across energy, electronics, manufacturing, and environmental engineering. As material science continues to evolve rapidly through advances in chemical synthesis, structural design, and functional innovation, MCCM 2025 provided a timely platform for interdisciplinary exchange and scientific collaboration. MCCM 2025 focused on frontier developments in polymer chemistry, semiconductor materials, high-performance composites, advanced packaging materials, catalysis, separation media, and energy-related materials. The conference served as a forum for presenting cutting-edge research results, discussing emerging challenges, and exploring future directions in material chemistry and composite materials. The conference featured a distinguished series of keynote speeches delivered by internationally recognized experts. Prof. Shiyong Yang (Institute of Chemistry, Chinese Academy of Sciences, China) presented advances in polymer materials for high-density IC manufacturing and advanced packaging, highlighting progress in photosensitive polyimides, epoxy molding compounds, and bonding materials. Prof. Qinghua Lu (Shanghai Jiao Tong University, China) discussed the localization progress and technological development trends of photopolyimide photoresists, emphasizing molecular design principles and recent breakthroughs in domestic production. Prof. Wenhua Sun (Institute of Chemistry, Chinese Academy of Sciences, China) introduced new horizons in polyethylene research through late-transition-metal complex catalysts, showcasing industrially impactful innovations in α-olefin production. Prof. Wei Li (Zhejiang University of Technology, China) provided an overview of cutting-edge technologies and material innovation trends in the global semiconductor industry, highlighting the critical role of IC development in AI, HPC, and next-generation electronics. Prof. Wenbing Kang (Shandong University, China) examined key issues and sustainable innovation strategies in the industrialization of photoresist materials. Prof. Yi Zhang (Sun Yat-sen University, China) presented research on high-performance photosensitive polyimides and their applications in advanced membrane encapsulation. Mr. Yongjie Du (Dynamic Technology, China) discussed development trends of IC substrates in the AI era and the application of high-density interconnect technologies. Prof. Jianbo Qu (China University of Petroleum, East China, China) introduced construction strategies for high-speed biomacromolecule separation media based on mass-transfer enhancement. Prof. Saidur Rahman (Sunway University, Malaysia) shared insights into low-cost advanced materials for thermal and energy storage applications. Assoc. Prof. Qaiser Mahmood (Guangdong Laboratory of Chemistry and Chemical Engineering, China) presented emerging trends in nickel-catalyzed polyethylene elastomers. List of Committee Member is available in this PDF.

Rising Star: Rewriting the Code of Life for the Future of Food.

A decade of progress and paradigm shifts in osteoporosis therapeutics: A bibliometric analysis.

This paper presents a bibliometric analysis of the fast-growing area of deep learning in neuroimaging. Using data from the Scopus database, we analyzed 12564 peer-reviewed publications originating from 102 countries, published in 2259 sources over the period from 2014 to 2024. The field demonstrated a compound average annual growth rate of 51.7%. We found that China emerged as the most productive contributor, accounting for 22.9% of the total publications and 18% of total citations. The Chinese Academy of Sciences was identified as the most productive research institution with 149 publications and 1557 citations, while Lecture Notes in Computer Science was noted as the most highly cited source in this domain. High usage of deep learning, brain, and magnetic resonance imaging identified the most prominent research themes. Also, our analysis noted strong research emphasis on the application of various deep learning architectures for the diagnosis and study of important neurological disorders like Parkinson's Disease, Alzheimer's Disease, and Mild Cognitive Impairment. The article would be useful in understanding the current state-of-the-art deep learning for neuroimaging by identifying key research trends, influential institutions, and prominent research themes. In this way, it will contribute to helping future researchers go further in this fast-growing field.

Against the dual backdrop of educational digital transformation and the inheritance of excellent traditional Chinese culture, the integration of digital martial arts into campuses faces core challenges: insufficient teaching standardization, superficial cultural dissemination, and a lack of psychological empowerment. Based on a wearable-free somatosensory technology platform certified by the Chinese Academy of Sciences (CAS) and supported by multiple national invention patents, this study systematically constructs a trinity model for digital martial arts campus integration—"digital teaching-immersive cultural inheritance-psychological empowerment"—integrating multi-modal AI and a dual-core digital coach system. Technically, it highlights the core advantages of wearable-free somatosensory technology, the precise adaptability of multi-modal AI, and the digital coach’s dual system ("time-travel learning + intelligent scoring"), with dual adherence to national martial arts teaching standards and mental health assessment criteria to enhance technical authority. In practice, through three dimensions—standardized teaching, immersive cultural inheritance, and psychological-emotional sub-services—it realizes the digital evolution of martial arts teaching skills and methods, while integrating psychological-emotional detection and intervention as an integral sub-item throughout the teaching process. This research provides technical support and practical paradigms for the integration of digital martial arts into campuses, helping teenagers improve skills, inherit culture, and develop sound personalities through martial arts learning.

A comprehensive checklist of type specimens preserved in the fish collection of the Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, is presented. The collection dates back to the late 1950s. As of early 2025, this repository comprises approximately 200,000 specimens, including 2660 type specimens spanning 6 orders, 21 families, 86 genera, and 245 species. The type-specimens collection is notably rich in Cypriniformes, with 198 species from 12 families and 65 genera, and Siluriformes, with 38 species from 4 families and 12 genera. Additionally, it includes 1 species each of Anabantiformes and Beloniformes, 5 species of Gobiiformes from a single genus, and 2 species of Synbranchiformes from a solitary genus. All original description references of the 245 species and their current valid taxonomy have been verified. Geographically, the specimens are predominantly sourced from China and neighboring countries, particularly the southwestern region of China.

Activity-Based Protein Profiling (ABPP) has been widely applied in the field of drug target identification by virtue of its unique technical advantages. To gain insights into the global research status of this field, this study outlines the research content and progress and predicts future development trends through bibliometric analysis. This study adopted a dual-platform data retrieval strategy and conducted visual analysis by combining multidimensional metrics with the use of CiteSpace and VOSviewer. This study included a total of 5,326 relevant pieces of literature published between 2006 and 2025. The global number of publications showed an overall upward trend, particularly since 2019. The United States and China were the major contributing countries, while the Chinese Academy of Sciences and Benjamin Cravatt were the most active institutions and authors in this field, respectively. Analysis indicated that the Journal of Biological Chemistry made significant contributions to this field, and the keywords "protein" and "expression" had the highest frequency of occurrence. Research in this field focuses on technological innovation and clinical translation and exhibits a trend of interdisciplinary integration with artificial intelligence, spatial omics, and other disciplines. Future priorities will focus on the optimization of probe design, deepening international cooperation, and promoting the application of clinical translation. This study identifies that the current research focus lies in the development of key pathogenic protein inhibitors, the exploration of drug repurposing strategies, and the target validation of Chinese medicine monomers and natural products. This study provides valuable references for scholars in this field.

Gastric cancer (GC) remains a leading cause of cancer-related morbidity and mortality worldwide, particularly in East Asia. Nanomedicine has emerged as a promising strategy to enhance the precision and efficacy of GC diagnosis and therapy. This study aimed to systematically evaluate global research trends and knowledge structures in GC nanomedicine during the early 21st century using bibliometric and visualization analyses. Relevant publications during the early 21st century were retrieved from the Web of Science Core Collection (WoSCC) and analyzed using R, VOSviewer, CiteSpace, Scimago Graphica, and Pajek. The included records spanned 2003-2026, with the two items labeled "2026" reflecting early access articles already indexed in WoSCC at the time of data retrieval. A total of 1,717 publications authored by 9,857 researchers across 2,192 institutions in 83 countries were included. China, Iran, and the United States were identified as leading contributors, with major research hubs at the Chinese Academy of Sciences, Shanghai Jiao Tong University, and Islamic Azad University. Research output exhibited a sustained upward trend, with citation frequency and H-index steadily increasing. Five primary research hotspots were identified: nano-drug delivery and chemotherapy optimization, targeted therapy and signaling pathway modulation, photothermal/photodynamic synergistic therapies, functionalized nanomaterials for diagnostics, and immunotherapeutic/antibacterial nanomedicine. Over time, the field has evolved from early nanocarrier design (2003-2015) to targeted therapy and diagnostic applications (2015-2020), and more recently toward immunotherapy and green synthesis strategies (2020-2026). Importantly, most studies in this field remain at the preclinical or early translational stage, underscoring the need for further validation using standardized preclinical models and well-designed clinical trials. This bibliometric study provides a comprehensive overview of the developmental trajectory, key contributors, and emerging trends in GC nanomedicine, offering valuable insights to guide future interdisciplinary research, clinical translation, and strategic resource allocation.

Reductionism has underpinned modern science since the 17th-century Scientific Revolution. This methodology decomposes systems into minimal units and deduces wholes from parts, succeeding across physical, life and social sciences. However, reductionism reveals limits as the scientific frontier shifts from identifying building blocks to understanding how components generate collective behavior. We possess massive data and precise local equations but fail to predict cell fates, financial crises or cognitive emergence in neural networks. Complex systems science-a mid-20th-century discipline exploring the structures, behaviors, evolution and laws of complex systems-integrates with diverse fields to transform scientific paradigms. Rapid AI development triggers this paradigm shift. AI algorithms handle massive data and complex systems, while AI systems themselves constitute complex systems whose progress depends on complex systems science. In this NSR Forum, we convene five researchers to discuss the essence of complex systems science and its integration with other disciplines to support a pivotal scientific paradigm shift in the AI era. Tingting Gao Postdoc Researcher, Network Science Institute, Northeastern University, USA Wei Lin Professor, School of Mathematical Sciences and Research Institute of Intelligent Complex Systems, Fudan University, China Yu Liu Associate Professor, Department of Systems Science, Beijing Normal University at Zhuhai, China Jiang Zhang Professor, School of Systems Science, Beijing Normal University, China Lei Guo (Chair) Professor, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China.

Global research landscape of natural nanogels in biomedical applications: A two-decade bibliometric analysis of development, research hotspots and future trends.

Soil science research in Research Center for Eco-Environmental Sciences: Review and outlook.

To reveal the knowledge structure, research hotspots, and development trends in the field of nutrition through visual analysis of core literature on the application of artificial intelligence(AI), providing a reference for nutritional research. Using bibliometric method, we retrieved relevant literature from the Web of Science database between 2016 and 2024, limiting the literature types to original articles or reviews. CiteSpace 6.4. R1 software was used for visual analysis, including keyword co-occurrence, clustering, timeline, and emergence analysis, to construct a knowledge graph and analyze author, institutional collaboration networks, and research themes. A total of 1896 core papers were included, showing an accelerating growth trend in publication volume, entering a rapid growth phase after 2021, with a total growth rate of 485.7% and an average annual growth rate of 19.8%. Core authors included 78 individuals, accounting for 25.74% of all researchers, publishing 433 papers, which accounted for 22.84% of the total literature. Research institutions were mainly concentrated in China and the United States, with the University of California and Harvard University in the U. S. , and the Chinese Academy of Sciences and the Chinese Academy of Medical Sciences/Union Medical College in China, occupying core positions in the collaboration network. Keyword analysis revealed that keywords such as "machine learning""metabolic syndrome""gut microbiota" were high-frequency terms, accounting for 45.31% of the total word frequency. Keyword clustering analysis formed 11 thematic clusters, covering key application scenarios such as chronic disease risk assessment and personalized dietary interventions. The keyword burst analysis reveals evolving research priorities, shifting from disease-diet association studies to nutrition interventions, mental health, and technical standardization. The findings indicate that AI applications in nutrition are advancing toward diversified and refined development. Future efforts should emphasize interdisciplinary collaboration to promote standardized and policy-driven implementation of these technologies.

Over the past 25years, Briefings in Bioinformatics (BIB) has been the leading journal in the bioinformatics field. To commemorate this milestone and trace the journal's academic trajectory, we conducted a comprehensive bibliometric analysis of 4185 BIB publications published between 2000 and 2024. The publications were retrieved from Elsevier's Scopus. Since 2021, the annual number of citations has increased prominently, peaking in 2024 (n = 30 729). These publications have accumulated a total of 183 911 citations, including 364 publications that have received more than 100 citations. The most cited article was published by Kumar etal. (2004) and it has received 10 750 citations. The 4185 publications were contributed by 4140 authors from 79 countries/regions, reflecting BIB's international characteristics. In terms of the total number of publications (TP) and the total number of citations (TC), China (TP = 2085; TC = 58 851), the United States (TP = 1120; TC = 67 000), and the United Kingdom (TP = 329; TC = 16 581) were the leading countries. The Chinese Academy of Sciences (TP = 216; TC = 6200), Central South University (TP = 112; TC = 3079), and Zhejiang University (TP = 105; TC = 3503) were the leading affiliations. Zou, Quan (TP = 64; TC = 3023), Song, Jiangning (TP = 58; TC = 2652), and Wu, Rongling (TP = 35; TC = 633) were the leading authors. Collaboration has intensified over time, with the median number of authors per publication rising from two in 2000-2004 to five in 2020-2024. The research focus of BIB has shifted from foundational bioinformatics and database development to integrative multi-omics analysis and AI-driven biomedical research. As BIB develops, its international impact will continue to grow, solidifying its pivotal role in shaping the future of bioinformatics research.

<p indent="0mm">Salt marsh ecosystems are a typical type of “blue carbon” ecosystem, and their carbon sequestration function plays an important role in mitigating global climate change. Long-term and continuous carbon flux observations are crucial for accurately assessing the carbon sink capacity of salt marshes and predicting their potential responses under climate change conditions. However, the current lack of long-term monitoring data for these ecosystems limits in-depth investigations into their carbon sink functions. Therefore, high-quality, long-term datasets are urgently needed to support related studies. Since 2011, the Yellow River Delta Ecological Research Station of Coastal Wetland, Chinese Academy of Sciences, has conducted continuous monitoring of carbon exchange processes in the salt marsh ecosystem of the Yellow River Delta using the eddy covariance technique. a substantial amount of multi-year high-quality carbon flux observation data has been accumulated. This dataset compiles carbon flux observations in the salt marsh ecosystem of the Yellow River Deltafrom 2012 to 2020 and provides data products at two high temporal resolutions: half-hourly and daily. It includes net ecosystem exchange of CO₂ (NEE), gross primary productivity (GPP), and ecosystem respiration (ER). The CO₂ flux data in this dataset have been fully processed through standardized procedures, including quality control, gap filling, and partitioning. The annual average retention rate of raw data reaches 53%, also demonstrating the high quality of this dataset. The dataset offers reliable support for related research and contributes to a deeper understanding of carbon exchange processes in salt marsh ecosystems.

Environmental aquatic chemistry in the Research Center for Eco-Environmental Sciences: Efforts for cleaner water.

Social-economic-natural complex ecosystem (SENCE) theory and its application: Historical contributions and future prospects.

Rising Star: Folding Pattern and Working Mechanism of Functional RNA Molecules.

Research progress in atmospheric haze chemistry: Formation mechanism of air pollution complex and control technologies.

In embarking on randomized clinical trials (RCTs), researchers can hypothesize that a more intensive treatment is better than a less intensive treatment (positive hypothesis) or that a less intensive treatment is similar or noninferior to a more intensive treatment (negative hypothesis). Researchers may design noninferiority RCTs (NI-RCTs) to support negative hypotheses and standard RCTs (S-RCTs) to support negative or positive hypotheses. Regardless of hypotheses, S-RCTs and NI-RCTs should produce consistent results when assessing similar participants, interventions, control, and outcomes. To compare effect estimates in S-RCTs with positive hypotheses versus NI-RCTs and in S-RCTs with negative hypotheses versus NI-RCTs. Meta-research. 98 meta-analyses. 468 RCTs, including 153 NI-RCTs and 315 S-RCTs (149 positive and 166 negative hypotheses). S-RCTs as the exposure and NI-RCTs as the control. The ratio of effect estimates between S-RCTs and NI-RCTs in each meta-analysis was combined across meta-analyses. Standard RCTs with positive hypotheses produced effect estimates 1.47 (95% CI, 1.27 to 1.70) times larger than NI-RCTs; among RCTs rated as having low risk of bias for blinding, the ratio was 1.01 (CI, 0.70 to 1.45), whereas among those rated as having high or unclear risk of bias for blinding, the ratio was 1.81 (CI, 1.41 to 2.33). Standard RCTs with negative hypotheses did not produce statistically different effect estimates from NI-RCTs (ratio, 0.93 [CI, 0.84 to 1.03]). Findings may be limited by residual differences between S-RCTs and NI-RCTs in the same meta-analysis. The researchers' hypotheses may bias the results of published RCTs, especially those with high or unclear risk of bias for blinding. The effect of researchers' hypotheses should be assessed in systematic reviews and clinical practice guidelines when RCTs addressing the same clinical question report conflicting hypotheses. The Shenzhen Municipal Government, Guangdong Province, China, and the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences.