Objective To investigate the heterogeneity and key molecular features of epithelial cells in triple-negative breast cancer (TNBC), identify prognostic biomarkers, and develop a robust survival prediction model. Methods Using TNBC single-cell transcriptomic data, epithelial cells were extracted, normalized, and subclustered to characterize their molecular signatures and functional differences. High-dimensional weighted gene co-expression network analysis (hdWGCNA) was applied to establish co-expression modules in epithelial cells. Multiple machine learning algorithms were integrated to select key prognostic genes and develop a risk-score model, whose performance was evaluated using receiver operating characteristic (ROC) curves and Kaplan-Meier (K-M) survival analysis. In addition, the immune microenvironment features and potential drug-response differences between the high- and low-risk groups were systematically assessed. Finally, PCR was performed to validate the expression differences of the key genes between tumor and normal tissues. Results We characterized the composition and molecular features of TNBC epithelial subpopulations and identified a TNBC-associated epithelial subset. By integrating hdWGCNA with machine learning approaches, 10 key genes were selected to construct a prognostic model, which effectively stratified patients into distinct survival-risk groups and demonstrated favorable predictive performance in ROC and K-M analyses. Immune profiling revealed the differences in the infiltration levels of seven immune cell types and immune function-related features between the high- and low-risk groups. Drug-sensitivity analysis suggested potential differential responses to eight agents across the risk groups. PCR validation further confirmed the differential expression of the ten signature genes between tumor and normal tissues. Conclusion This study reveals epithelial heterogeneity in TNBC at single-cell resolution and establishes a 10-gene prognostic model, which may facilitate the stratification of TNBC risk and the evaluation of immune characteristics and potential therapeutic strategies.

Flap surgery is a complex surgical procedure that has become an effective method for the treatment of many diseases and traumas.Flap survival is closely related to a variety of factors including cellular autophagy,oxidative stress,inflammatory response,mesenchymal stem cells(MSCs)function,and vascular regeneration.Cellular autophagy maintains intracellular homeostasis and plays a key role in reducing oxidative stress and inflammation and promoting injury repair.Excessive oxidative stress and inflammatory responses pose a threat to flaps,affecting their survival and successful transplantation.Endothelial cells are involved in vascu-lar regeneration through proliferation,migration,and production of angiogenic factors,and vascular endothelial growth factor directly promotes blood vessel formation and maintains endothelial cell function.MSCs play an important role in promoting flap survival and tissue repair due to their unique biological properties and multiple mechanisms of action.The multiple roles played by cellular autophagy,oxidative stress,inflammatory response,MSCs function,and vascular regeneration in influencing postoperative flap survival are hereby elaborated.The aim is to provide a basis for the clinical application of regulating the above factors to improve postoperative flap survival,improve the success rate of flap surgery,reduce complications,and bring more hope for the recovery and quality of life of patients.

Objective:To investigate the level of rehabilitation exercise adherence in patients undergoing anterior cruciate ligament (ACL) reconstruction within 6 months post-surgery, analyze its trajectory, and provide a reference for clinical nursing interventions.Methods:Using a convenience sampling method, 150 patients who underwent arthroscopic ACL reconstruction at the First Affiliated Hospital of Zhengzhou University from June 2022 to August 2023 were selected. Rehabilitation exercise adherence was assessed monthly from postoperative 1 month (M 1) to 6 months (M 6) using the Orthopedic Functional Exercise Adherence Scale. A latent class growth model was employed to analyze adherence trajectory categories, and Logistic regression was used to identify influencing factors for different adherence trajectories. Results:A total of 144 patients completed follow-up (follow-up rate was 96.00%). Exercise adherence scores gradually declined over the 6 months post-surgery, with scores of (50.30±5.39), (45.92±3.85), (39.48±5.27), (35.83±6.19), (31.85±7.59), and (29.88±7.88) from M 1 to M 6, respectively. The difference in scores across time points was statistically significant ( F=85.467, P<0.01). Three potential categories were identified through the latent class growth model: the moderate-high level rapid decline group ( n=65, 45.14%), the moderate-low level stable group ( n=32, 22.22%), and the moderate-low level slow decline group ( n=47, 32.64%). Logistic regression analysis revealed that gender and age were significant factors influencing adherence trajectory categories ( P<0.05) . Conclusions:Dynamic declines in rehabilitation exercise adherence are common among ACL reconstruction patients, with varying degrees of decline. Gender and age may influence adherence trajectories, suggesting that targeted management strategies could be implemented in clinical practice.

Objective:To investigate the level of rehabilitation exercise adherence in patients undergoing anterior cruciate ligament (ACL) reconstruction within 6 months post-surgery, analyze its trajectory, and provide a reference for clinical nursing interventions.Methods:Using a convenience sampling method, 150 patients who underwent arthroscopic ACL reconstruction at the First Affiliated Hospital of Zhengzhou University from June 2022 to August 2023 were selected. Rehabilitation exercise adherence was assessed monthly from postoperative 1 month (M 1) to 6 months (M 6) using the Orthopedic Functional Exercise Adherence Scale. A latent class growth model was employed to analyze adherence trajectory categories, and Logistic regression was used to identify influencing factors for different adherence trajectories. Results:A total of 144 patients completed follow-up (follow-up rate was 96.00%). Exercise adherence scores gradually declined over the 6 months post-surgery, with scores of (50.30±5.39), (45.92±3.85), (39.48±5.27), (35.83±6.19), (31.85±7.59), and (29.88±7.88) from M 1 to M 6, respectively. The difference in scores across time points was statistically significant ( F=85.467, P<0.01). Three potential categories were identified through the latent class growth model: the moderate-high level rapid decline group ( n=65, 45.14%), the moderate-low level stable group ( n=32, 22.22%), and the moderate-low level slow decline group ( n=47, 32.64%). Logistic regression analysis revealed that gender and age were significant factors influencing adherence trajectory categories ( P<0.05) . Conclusions:Dynamic declines in rehabilitation exercise adherence are common among ACL reconstruction patients, with varying degrees of decline. Gender and age may influence adherence trajectories, suggesting that targeted management strategies could be implemented in clinical practice.

Ankylosing spondylitis (AS) combined with lower cervical fracture is often categorized into unstable fracture, with a high incidence of neurological injury and a high rate of disability and morbidity. As factors such as shoulder occlusion may affect the accuracy of X-ray imaging diagnosis, it is often easily misdiagnosed at the primary diagnosis. Non-operative treatment has complications such as bone nonunion and the possibility of secondary neurological damage, while the timing, access and choice of surgical treatment are still controversial. Currently, there are no clinical practice guidelines for the treatment of AS combined with lower cervical fracture with or without dislocation. To this end, the Spinal Trauma Group of Orthopedics Branch of Chinese Medical Doctor Association organized experts to formulate Clinical guidelines for the treatment of ankylosing spondylitis combined with lower cervical fracture in adults ( version 2024) in accordance with the principles of evidence-based medicine, scientificity and practicality, in which 11 recommendations were put forward in terms of the diagnosis, imaging evaluation, typing and treatment, etc, to provide guidance for the diagnosis and treatment of AS combined with lower cervical fracture.

It has been proposed by Basic Questions On Proper Therapies for Different Diseases Geographically （《素问·异法方宜论篇》） that "wei （痿） diseases should be treated by Daoyin （导引）". Furthermore， it is clarified that the indications of Daoyin are those conditions related to spleen and dampness caused by dampness pathogen， excessive food intake and less exercise， and mainly manifested as heavy limbs， fatigue and flaccidity， which is similar to the metabolic imbalance in the early stage of glucose or lipid metabolism disorder in modern medicine. Based on modern clinical and basic research evidence， Daoyin can inhibit the response of inflammation， alleviate oxidative stress， regulate intestinal microbiota， and modulate gene expression to improve metabolic abnormalities， and this will provide ideas for researches on the indications of Daoyin.

In recent years, global outbreaks of infectious diseases, such as COVID-19, have triggered great concern about emerging infectious diseases. With the rapid development of next-generation sequencing technology and bioinformatic tools for viral metagenomics, there is now a widespread capability to detect and identify various known and unknown pathogenic microorganisms within both environmental and biological contexts. Furthermore, the continuous evolution of machine learning methods has led to the development and application of multiple rapid and highly accurate approaches for virus identification. Concurrently, owing to the continual progress in machine learning methods, several rapid and accurate virus identification techniques have been widely developed and applied. Therefore, this review aims to systematically summarize the key methodologies, frameworks, and the scope of applicability within the field of viral metagenomics, with a specific focus on virus identification and prediction. It could facilitate a deeper understanding of viral characteristics, identify potential novel pathogens, and provide technical support for the early prevention and control of infectious diseases.

Objective To analyze the research status and trend of scarlet fever literature in China, and to provide reference for subsequent research. Methods Three major Chinese databases, CNKI, Wanfang, and VIP, as well as Web of Science English database, were used to search for literature related to scarlet fever from 2000 to 2023. Citespace6.2.R2 software was used to statistically analyze the number of publications, authors, institutions and journals, co-cited literature, keyword clustering, and other literature characteristics of the literature. Results From 2000 to 2023, a total of 1 011 Chinese literature were included in the three major Chinese databases. Since 2011, the number of publications had gradually increased, but in recent years, the number of publications had decreased. The organization with the most publications was the Shenyang Center for Disease Control and Prevention. The cluster analysis of key words mainly formed 9 cluster tags, and the high-frequency keywords mainly included epidemic characteristics, epidemiology, incidence rate, etc. A total of 84 English literature were included in the WOS database, with an overall upward trend in publication volume. The institution with the most publications was the China Center for Disease Control and Prevention, and the most frequently cited journal was ^“LANCET INFECT DIS^”.《Resurgence of scarlet fever in China: a 13-year population-based surveillance study》 was the most cited journal. After keyword cluster analysis, 9 cluster labels were mainly formed, and the keywords were mainly outbreak，Hong Kong, and Group A streptococcus. Conclusion Compared with the English literature, which mainly focuses on spatiotemporal aggregation, etiology and strain resistance, Chinese literature focuses more on epidemic surveillance, clinical features and quality nursing.

In recent years, global outbreaks of infectious diseases, such as COVID-19, have triggered great concern about emerging infectious diseases. With the rapid development of next-generation sequencing technology and bioinformatic tools for viral metagenomics, there is now a widespread capability to detect and identify various known and unknown pathogenic microorganisms within both environmental and biological contexts. Furthermore, the continuous evolution of machine learning methods has led to the development and application of multiple rapid and highly accurate approaches for virus identification. Concurrently, owing to the continual progress in machine learning methods, several rapid and accurate virus identification techniques have been widely developed and applied. Therefore, this review aims to systematically summarize the key methodologies, frameworks, and the scope of applicability within the field of viral metagenomics, with a specific focus on virus identification and prediction. It could facilitate a deeper understanding of viral characteristics, identify potential novel pathogens, and provide technical support for the early prevention and control of infectious diseases.

Flap surgery is a complex surgical procedure that has become an effective method for the treatment of many diseases and traumas. Flap survival is closely related to a variety of factors including cellular autophagy, oxidative stress, inflammatory response, mesenchymal stem cells (MSCs) function, and vascular regeneration. Cellular autophagy maintains intracellular homeostasis and plays a key role in reducing oxidative stress and inflammation and promoting injury repair. Excessive oxidative stress and inflammatory responses pose a threat to flaps, affecting their survival and successful transplantation. Endothelial cells are involved in vascular regeneration through proliferation, migration, and production of angiogenic factors, and vascular endothelial growth factor directly promotes blood vessel formation and maintains endothelial cell function.MSCs play an important role in promoting flap survival and tissue repair due to their unique biological properties and multiple mechanisms of action. The multiple roles played by cellular autophagy, oxidative stress, inflammatory response, MSCs function, and vascular regeneration in influencing postoperative flap survival are hereby elaborated. The aim is to provide a basis for the clinical application of regulating the above factors to improve postoperative flap survival, improve the success rate of flap surgery, reduce complications, and bring more hope for the recovery and quality of life of patients.