ObjectiveTo analyze the research hotspots and development trends of brain-computer interface (BCI) in the treatment for spinal cord injury (SCI). MethodsRelevant literatures on BCI applied in SCI treatment, published from the inception of the Web of Science Core Collection to July, 2025, were retrieved. Visualization analysis was performed using CiteSpace, VOSviewer and Tableau Desktop. ResultsA total of 437 literatures were included, and the annual number of publications showed an overall increasing trend. The United States ranked first in the number of publications; Graz University of Technology was the institution with the highest number of publication; Gernot R Mueller-Putz was the most productive author, while Jonathan R Wolpaw was the most cited author. Brain-computer interface and artificial intelligence were identified as the high-frequency and bursting keywords in this field. The researches were characterized by the cross-integration of five core disciplines: neuroscience and rehabilitation medicine, biomedical engineering, computer science and artificial intelligence, neurophysiology, and materials science. ConclusionResearches on BCI in SCI treatment are accelerating continuously, and technological integration is becoming the core trend.

ObjectiveTo analyze the research hotspots and development trends of brain-computer interface (BCI) in the treatment for spinal cord injury (SCI). MethodsRelevant literatures on BCI applied in SCI treatment, published from the inception of the Web of Science Core Collection to July, 2025, were retrieved. Visualization analysis was performed using CiteSpace, VOSviewer and Tableau Desktop. ResultsA total of 437 literatures were included, and the annual number of publications showed an overall increasing trend. The United States ranked first in the number of publications; Graz University of Technology was the institution with the highest number of publication; Gernot R Mueller-Putz was the most productive author, while Jonathan R Wolpaw was the most cited author. Brain-computer interface and artificial intelligence were identified as the high-frequency and bursting keywords in this field. The researches were characterized by the cross-integration of five core disciplines: neuroscience and rehabilitation medicine, biomedical engineering, computer science and artificial intelligence, neurophysiology, and materials science. ConclusionResearches on BCI in SCI treatment are accelerating continuously, and technological integration is becoming the core trend.

ObjectiveTo analyze the research hotspots and development trends of brain-computer interface (BCI) in the treatment for spinal cord injury (SCI). MethodsRelevant literatures on BCI applied in SCI treatment, published from the inception of the Web of Science Core Collection to July, 2025, were retrieved. Visualization analysis was performed using CiteSpace, VOSviewer and Tableau Desktop. ResultsA total of 437 literatures were included, and the annual number of publications showed an overall increasing trend. The United States ranked first in the number of publications; Graz University of Technology was the institution with the highest number of publication; Gernot R Mueller-Putz was the most productive author, while Jonathan R Wolpaw was the most cited author. Brain-computer interface and artificial intelligence were identified as the high-frequency and bursting keywords in this field. The researches were characterized by the cross-integration of five core disciplines: neuroscience and rehabilitation medicine, biomedical engineering, computer science and artificial intelligence, neurophysiology, and materials science. ConclusionResearches on BCI in SCI treatment are accelerating continuously, and technological integration is becoming the core trend.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Background: Epidermal growth factor (EGF) is widely applied in dermatology for its regenerative effects; however, its clinical utility is limited by low stability and high production costs.To overcome these limitations, we developed a novel probiotic-derived epidermal growth factor (pEGF) by engineering Pediococcus pentosaceus CBT SL4 to express and secrete EGF. Objective: To investigate the anti-photoaging effects of topical pEGF in a ultraviolet B (UVB)-irradiated mice by assessing histological and molecular changes related to collagen synthesis, extracellular matrix (ECM) remodeling, oxidative stress, and inflammation. Methods: SKH1 hairless mice were exposed to UVB for twelve weeks to induce photoaging and treated with topical 10% pEGF cream from week 9 to 12. Skin samples were analyzed using dermoscopy, histology, immunohistochemistry, quantitative real-time polymerase chain reaction, western blotting, and proteomics to evaluate morphological and molecular alterations in ECM components and inflammatory markers. Results: Topical pEGF treatment improved skin texture and reduced wrinkles. Histological evaluation revealed that EGF significantly attenuated UVB-induced epidermal thickening and restored collagen density, with the strongest effects observed in the pEGF group. Molecular analysis showed downregulation of matrix metalloproteinases (MMPs; MMP-1, MMP-3), upregulation of collagen (type I collagen, type III collagen) and tissue inhibitors of metalloproteinases (TIMPs; TIMP-1, TIMP-2). Additionally, pEGF reduced pro-inflammatory cytokines (tumor necrosis factor-α, interleukin-1β) and oxidative stress markers. Conclusion pEGF demonstrated potent anti-photoaging effects through ECM restoration, inflammation modulation, and barrier reinforcement. These findings support the therapeutic potential of pEGF as a stable, cost-effective alternative to conventional EGF in dermatological applications.

Purpose: This study aimed to project cancer incidence and mortality for 2026 to estimate Korea’s current cancer burden. Materials and Methods: Cancer incidence data from 1999 to 2023 were obtained from the Korea National Cancer Incidence Database, while cancer mortality data from 1993 to 2024 were acquired from the Ministry of Data and Statistics. Cancer incidence and mortality were projected by fitting a linear regression model to observed age-specific cancer rates against their respective years and then by the projected age-specific rates by the anticipated age-specific population for 2026. A joinpoint regression model was applied to identify significant changes in trends, using only the most recent trend data for predictions. Results: A total of 308,876 new cancer cases and 86,317 cancer deaths are expected in Korea in 2026. The most commonly diagnosed cancer is projected to be thyroid cancer, followed by the colorectal, lung, breast, prostate and stomach cancers. These six cancers are expected to account for 63.5% of all newly diagnosed cancers. Lung cancer is expected to be the leading cause of cancer-related deaths, followed by liver, colorectal, pancreatic, gallbladder, and stomach cancers, together comprising 65.9% of all cancer deaths. Conclusion Korea’s cancer burden continues to shift toward malignancies prevalent in older populations. The sustained increase in prostate cancer among men and the rising mortality impact of pancreatic cancer reflect structural changes in the national cancer profile amid rapid population aging.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Purpose: This study aimed to report the overall national trends in the rates of cancer screening based on recommendations and provide insights into the changing trends of these rates across different demographics. Materials and Methods: This study used data from the Korean National Cancer Screening Survey (KNCSS), which surveys nationwide cancer-screening rates and includes 4,500 individuals meeting the Korean National Cancer Screening Program (NCSP) protocol age criteria. Cancer-screening rates were assessed using structured questionnaires; yearly trends were analyzed for both lifetime cancer-screening rates and rates of screening based on recommendations, and subgroup analyses were performed based on age and sex. Results: The rates of cancer screening based on recommendations showed significant increments: the stomach cancer-screening rate increased from 39.2% in 2004 to 77.5% in 2023 (3.50% per year), the liver cancer-screening rate increased from 20.0% to 48.8% (4.30% per year), and the colorectal cancer, increased from 19.9% to 70.7% (5.15% per year). The breast cancer-screening rate increased from 33.2% to 72.7% (2.88% per year), and the cervical cancer, increased from 58.3% to 70.2% (1.08% per year). Despite some differences, particularly in relation to sociodemographic factors, screening rates increased significantly for all cancer types. Conclusion Cancer-screening rates in Korea increased consistently from 2004 to 2023, demonstrating the effectiveness of the national cancer-screening program. However, the increments in breast, cervical and lung cancer-screening rates were relatively lower, indicating the need for additional efforts and strategies.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.