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.

Caenorhabditis elegans,as an emerging model organism,has been widely used in multiple disciplines such as medicine,life science,and environmental science in recent years,due to its charac-teristics of short life cycle,clear genetic background,highly conserved evolution,complete genome analysis and excellent fitting between experimental data and human results.It also shows unique advan-tages in the field of toxicology.This paper summarizes its advantages in toxicological research starting from the biological characteristics of C.elegans,introduces the toxicological research methods and progress based on the C.elegans model,focuses on demonstrating its applications in environmental fo-rensic medicine and forensic toxicology,and looks forward to the application of the relevant results in the field of forensic science.

Objective:To investigate the application value of cardiac magnetic resonance imaging(CMR) in evaluating the perioperative risk of surgical treatment in patients of coronary artery disease and heart failure with reduced ejection fraction.Methods:A total of 78 patients diagnosed with CAD and HFrEF who underwent CABG in Beijing Anzhen Hospital from January 2018 to October 2021 were retrospectively enrolled. All patients underwent CMR examination before CABG. The perioperative data of the two groups were compared, and the risk factors that may lead to perioperative MACCE were analyzed by LASSO regression. Then, logistic regression was used to establish a prediction model and internal validation was performed to evaluate the diagnostic efficiency and accuracy of the model by Bootstrap method. Results:There were 24 patients(30.8%) with perioperative MACCE and 54 patients(69.2%) without perioperative MACCE. LASSO regression was used to screen out three factors related to the outcome. Multivariate logistic regression analysis showed that LGE in the third and eighth segment of left ventricle and diastolic radial strain rate were independent risk factors for perioperative MACCE. The area under the curve of the prediction model constructed with CMR was 0.799(95% CI: 0.696-0.901), so the discrimination was good. The calibration curve showed that the prediction curve was basically fit to the standard curve, and the Hosmer- Lemeshow test P=0.797, indicating high prediction accuracy. Conclusion:CMR is a valuable test for evaluating perioperative risk in patients with coronary heart disease complicated with HFrEF. To establish the risk prediction model combined with CMR can provide some reference for the assessment of perioperative risk in these patients undergoing surgical treatment.

Objective To investigate the awareness of radon exposure hazards among non-uranium miners in Chongqing, China. Methods A survey was conducted among 177 male miners from eight non-uranium metal mines in Chongqing to collect data on basic information, personal habits, and the rate of radon awareness. Factors affecting radon awareness were analyzed using chi-square test and logistic regression model. Results The awareness rate of radon among miners was 23.73%. The chi-square test indicated significant difference in the radon awareness rate among miners with different levels of education (χ² = 10.28, P < 0.05), while there was no significant difference across different ages, years of work, labor relations, job categories, and types of miners (P > 0.05). Binary logistic regression analysis showed that a college (junior college) or higher level of education, a high school level of education, and working in mines were factors affecting the radon awareness among miners (χ² = 4.030, 9.150, 11.776, P < 0.05). Conclusion Miners lack awareness of radon, and there is an urgent need to strengthen education and propaganda regarding the hazards of radon.