Osteoporosis （OP） is a metabolic disorder characterized by microstructural deterioration of bone and increased bone fragility due to reduced bone mass， which can cause the development of bone-related diseases. This condition imposes significant economic and psychological burdens on patients. While modern medicine has extensively researched the pathogenesis of OP， it remains incompletely understood. Current clinical management primarily relies on anti-resorptive drugs and synthetic metabolic agents. However， long-term use of some medications may yield suboptimal therapeutic outcomes and lead to severe adverse reactions. Given the necessity for prolonged or lifelong treatment for OP， there is a critical need to identify highly effective， safe， and cost-effective pharmaceutical interventions. In light of evolving disease management paradigms and recent advancements in OP research， traditional Chinese medicine has demonstrated emerging advantages in addressing this condition. Through literature review， this study delves into the pathogenesis of OP from five perspectives： hormonal dysregulation， autophagy， ferroptosis， oxidative stress， and intestinal flora alteration. Furthermore， it summarizes the therapeutic efficacy and specific mechanisms of traditional Chinese medicine monomers and compound formulas against OP through regulating hormone levels， interfering with autophagy， inhibiting ferroptosis， counteract oxidative stress，and maintain intestinal flora balance. These multifaceted insights are expected to provide theoretical reference and guide future clinical traditional Chinese medicine approaches for preventing and managing OP.

BACKGROUND:Mitochondrial autophagy is closely related to the occurrence and development of steroid-induced osteonecrosis of the femoral head(SONFH),but specific biomarkers and regulatory mechanisms remain unclear. OBJECTIVE:To identify the key biomarkers of mitochondrial autophagy in steroid-induced osteonecrosis of the femoral head using machine learning algorithms and to conduct an immune infiltration analysis. METHODS:The SONFH datasets GSE123568 and GSE74089 were downloaded from the GEO database,serving as the training and validation sets,respectively.Differentially expressed genes between SONFH and control groups were selected,and weighted gene co-expression network analysis was performed.Mitochondrial autophagy-related genes were obtained from MitoCarta3.0 and intersected with differentially expressed genes and module genes.Two machine learning algorithms were utilized to identify key genes of SONFH mitochondrial autophagy,and validated using an external validation set.CIBERSORT and immune infiltration analysis were employed to assess the proportion of immune cells,and ssGSEA was used to analyze the correlation between mitochondrial autophagy genes and immune cells. RESULTS AND CONCLUSION:Differential analysis identified a total of 1 163 differentially expressed genes,including 663 upregulated genes and 500 downregulated genes.Weighted gene co-expression network analysis identified 4 key modules,comprising 1 412 module genes.Intersection with mitochondrial autophagy genes yielded 39 intersecting genes as disease-related mitochondrial autophagy genes.Gene ontology enrichment analysis showed that the biological processes were mainly related to heme metabolism,mitochondrial transport,nucleotide bisphosphate metabolism and thioester metabolism,and the cellular components were mainly related to mitochondrial matrix,mitochondrial outer membrane,organelle outer membrane and mitochondrial inner membrane,and the molecular functions were mainly related to fatty acid ligase activity,iron-sulfur cluster binding,and cofactor A ligase activity.Kyoto Encyclopedia of Genes and Genomes enrichment analysis mapped out a total of six pathways,which were mainly related to fatty acid degradation,mitochondrial autophagy,butyric acid metabolism,fatty acid biosynthesis and cofactor biosynthesis.Through LASSO regression and RFE-SVM algorithm analysis,four intersecting genes(ALDH5A1,FBXL4,MCL1,and STOM)were identified.The receiver operating characteristic curves of the four core genes and the diagnostic column chart validation set were all greater than 0.9.The occurrence and development of SONFH were related to immune cells such as dendritic cells,bone marrow-derived suppressor cells,regulatory T cells,and central memory CD8 T cells.To conclude,the four key mitochondrial autophagy genes ALDH5A1,FBXL4,MCL1,and STOM play a crucial role in the progression of SONFH through osteoclast differentiation and immune mechanisms.Additionally,all four genes have good disease prediction efficacy and can serve as biomarkers for the diagnosis and treatment of SONFH.

Ovulatory dysfunction (OD) is one of the main causes of infertility in women of childbearing age, which not only affects their reproductive ability, but also physical and mental health. Traditional treatment strategies have limited efficacies, and the emergence of biomedicines provides a promising alternative solution via the strategies of combining engineered design with modern advanced technology. This review explores the pathophysiological characteristics and related induction mechanisms of OD, and evaluates the current cutting-edge advances in its treatments. It emphasizes the potentials of biomedicines strategies such as hydrogels, nanoparticles and extracellular vesicles in improving therapeutic precision and efficacy. By mimicking natural physiological processes, and achieving controlled drug release, these advanced drug carriers are expected to address the challenges in ovarian microenvironment reprogramming, tissue repair, and metabolic and immune regulation. Despite the promising progress, there are still challenges in terms of biomedical complexity, differences between animal models and human physiology, and the demand for intelligent drug carriers in the therapy of OD. Future researches are mainly dedicated to developing precise personalized biomedicines in OD therapy through interdisciplinary collaboration, promoting the development of reproductive regenerative medicine.

The aim of this paper is to present the basic principles of the mesh basket-like six-electrode renal denervation (RDN) system, research progress in anti-hypertensive therapy and modulation of autonomic nerves for the treatment of diseases. It discusses the advances in system design, operational optimization and expansion of clinical applications, as well as the challenges faced. The future prospects of personalized autonomic nerves modulation for the treatment of diseases are also discussed.

Radiation therapy (RT) is a well-established and widely used treatment modality for prostate cancer. As prostate-rectum proximity contributes to rectal toxicity, there is growing interest in rectal protection. Technical advances have enabled the reduction of rectal injury. To improve the safety of prostate cancer radiotherapy and minimize the rectal toxicity of irradiation, the advances in rectal protection technique during prostate cancer radiotherapy, including technical advances of radiation therapy, image-guided radiotherapy, application of endorectal balloons, and use of rectum spacers, were reviewed, aiming to provide reference for improving the safety of prostate cancer radiotherapy and alleviating rectal radiation injury.

BACKGROUND:Ferroptosis-related genes have been found to play an important role in the pathogenesis of rheumatoid arthritis.However,there is currently a lack of immune expression of ferroptosis-related signature genes in rheumatoid arthritis and the construction of competing endogenous RNA(CeRNA)interaction networks.Machine learning,as a powerful signature gene selection algorithm based on bioinformatics,can more accurately identify ferroptosis-related signature genes that dominate the pathogenesis of rheumatoid arthritis. OBJECTIVE:To screen ferroptosis-related signature genes in rheumatoid arthritis using bioinformatics and machine learning methods,and to analyze the correlation between ferroptosis-related signature genes and immune infiltration and the construction of CeRNA network of ferroptosis-related signature genes. METHODS:Rheumatoid arthritis-related microarrays were obtained from the GEO database,and ferroptosis-related genes and their differential gene expression were extracted using R language.The differentially expressed genes were screened using machine learning methods.The LASSO regression and SVM-RFE methods were used for signature gene screening,and the genes filtered by both were re-intersected to finally obtain the signature genes in rheumatoid arthritis.Receiver operating characteristic curves were used to assess the accuracy of the screened signature genes for disease diagnosis.Immune infiltration of rheumatoid arthritis and normal synovial tissues was analyzed using the CIBERSORT algorithm,and the correlation between the signature genes and immune cells was analyzed.Finally,the CeRNA network of ferroptosis-related signature genes for rheumatoid arthritis was constructed and the disease signature genes were validated. RESULTS AND CONCLUSION:A total of 150 ferroptosis-related genes in rheumatoid arthritis were obtained,including 55 up-regulated genes and 95 down-regulated genes.GO and KEGG enrichment analyses identified 18 GO significantly correlated entries and 30 KEGG entries respectively,mainly involving metal ion homeostasis,ferric ion homeostasis and oxidative stress response.Machine learning analysis finally identified disease signature genes GABARAPL1 and SAT1.GSEA analysis found that adipocytokine signaling pathway,drug metabolism cytochrome P450,fatty acid metabolism,PPAR signaling pathway,tyrosine metabolism were mainly concentrated when GABARAPL1 was highly expressed,and chemokine signaling pathway,intestinal immune network on IGA production were mainly concentrated when SAT1 was highly expressed.Immune infiltration analysis found that nine immune cells were significantly different in rheumatoid arthritis and normal synovial tissues,in which plasma cells,T-cell CD8,and T-cell follicular helper were highly expressed and the rest were lowly expressed in the disease group.Single gene and immune cell correlation analysis found that GABARAPL1 was positively correlated with dendritic resting cells,activated NK cells,and macrophage M1,with the most significant correlation with dendritic resting cells,while SAT1 was positively correlated with T cell CD4 and γδ T cells and negatively correlated with NK resting cells.GSVA analysis found that SAT1 was upregulated in ascorbic acid and aldehyde metabolism,while downregulated in B-cell receptor signaling pathway,Toll-like receptor signaling pathway,T-cell receptor signaling pathway,and natural killer cell-mediated cytotoxicity.GABARAPL1 showed a down-regulation trend in PPAR signaling pathway,metabolism of nicotinate and nicotinamide,tryptophan metabolism,fatty acid metabolism,and steroid biosynthesis.Sixty long non-code RNAs may play a key role in the development of rheumatoid arthritis.To conclude,the occurrence of rheumatoid arthritis is significantly correlated with the abnormal expression of rheumatoid arthritis-induced ferroptosis-related signature genes,and the signature genes induce disease development via relevant signaling pathways.By analyzing rheumatoid arthritis-related long non-code RNAs-mediated ceRNA networks,potential therapeutic targets and signaling pathways can be identified to further elucidate its pathogenesis and provide a reference basis for subsequent experimental studies.

Objective:To study the differential gene expression and immune cell infiltration of gout patients,to find the key genes and immune cells of gout pathogenesis,and to explore the relationship between immune cells and gout.Methods:The gout chip GSE160170 was downloaded from the GEO database,and the differential gene expression analysis was carried out with the help of R language.Then,the STRING database was used to analyze the differential gene,and the Cytoscape software was used to screen the key genes,and then carry out enrichment analysis.At the same time,the infiltration of immune cells were analyzed.Results:The study found that IL-6,IL-1β,TNF,CCL3,CXCL8 and CXCL1 were key genes in the pathogenesis of gout,which were mainly exerted by IL-17,Toll-like receptor,NOD-like receptor,NF-κB and other signaling pathways.Processes such as cellular responses to lipo-polysaccharides,bacteria-derived molecules,and biological stimuli lead to disease;immune infiltration results indicate that memory B cells,activated NK cells,activated dendritic cells,activated mast cells and eosinophils were involved in the disease.It was signifi-cantly expressed in gout patients;the correlation analysis between immune cells showed that the expression of follicular helper T cells were positively correlated with the expression of activated mast cells,and the expression of unactivated NK cells and monocyte were negatively correlated.Conclusion:Key genes and differentially expressed immune cells are closely related to the pathogenesis of gout,providing new ideas for the study of the molecular mechanism of gout.

Objective:To evaluate the effect of workshop-based incontinence associated dermatitis (IAD) training course for geriatric nurse specialists, and to provide references and evidence for the application of workshop teaching model in specialized nursing training.Methods:The convenience sampling method was used to select 40 participants in the 9th Chongqing Geriatric Nurse Specialist Training Program in May 2021 as the intervention group, and IAD training was conducted using the workshop method. The 42 participants in the 8th Chongqing Geriatric Nurse Specialist Training Program in May 2020 were selected as the control group, who received IAD training through traditional teaching methods. After the training, the achievement of teaching objectives and teaching satisfaction were compared between the two groups using questionnaire survey, and IAD nursing knowledge and attitudes were compared using scales. SPSS 27.0 was used for the t-test and chi-square test. Results:The questionnaire survey showed that the intervention group achieved higher teaching objectives in all dimensions compared to the control group ( P<0.05). Compared with the control group, the intervention group had higher scores in teaching satisfaction regarding teaching format, interest in learning, practical skills, theoretical understanding, integration of theory and practice, and problem-solving abilities ( P<0.05). Compared with the control group, the intervention group demonstrated significantly higher scores in IAD nursing knowledge [(11.32±1.56) vs. (9.02±2.26), P<0.05] and attitudes [(37.63±3.54) vs. (32.81±2.97), P<0.05]. Conclusions:The workshop training has been highly recognized by the trainees, contributing to improved implementation effectiveness of teaching, enhancing the scores of IAD nursing knowledge and attitudes. This approach offers a new perspective for training with a practical focus in the specialized nursing field.

Purpose/Significance To explore and implement the automated interpretation of pediatric medical exam answers,so as to enhance the efficiency and quality of answer explanation compilation.Method/Process The paper proposes a method that combines latent semantic indexing(LSI),MC-BERT,and the CoSENT model.Initially,multiple candidate answer explanations are extracted from ref-erence documents using the LSI method and the MC-BERT model.Subsequently,the CoSENT model is employed to calculate the simi-larity between the candidate explanations and the question stems as well as the answer options.The candidate explanation with the highest similarity is then selected as the final answer explanation.Result/Conclusion The experimental results show that the method presented in this paper achieves a precision rate of 72.6％.Compared to single methods or models,it significantly improves the recall and precision of answer parsing,effectively enhances the efficiency of compiling question answer explanations,reduces the burden on educators,and pro-vides significant data support for educational research.

Objectives:To assess the prognostic impact of the neoadjuvant rectal (NAR) score following neoadjuvant short-course radiotherapy and consolidation chemotherapy in locally advanced rectal cancer (LARC), as well as its value in guiding decisions for adjuvant chemotherapy.Methods:Between August 2015 and August 2018, patients were eligible from the STELLAR phase III trial (NCT02533271) who received short-course radiotherapy plus consolidation chemotherapy and for whom the NAR score could be calculated. Based on the NAR score, patients were categorized into low (＜8), intermediate (8-16), and high (＞16) groups. The Kaplan-Meier method, log rank tests, and multivariate Cox proportional hazard regression models were used to evaluate the impact of the NAR score on disease-free survival (DFS).Results:Out of the 232 patients, 24.1%, 48.7%, and 27.2% had low (56 cases), intermediate (113 cases), and high NAR scores (63 cases), respectively. The median follow-up period was 37 months, with 3-year DFS rates of 87.3%, 68.3%, and 53.4% ( P＜0.001) for the low, intermediate, and high NAR score groups. Multivariate analysis demonstrated that the NAR score (intermediate NAR score: HR, 3.10, 95% CI, 1.30-7.37, P=0.011; high NAR scores: HR=5.44, 95% CI, 2.26-13.09, P＜0.001), resection status ( HR, 3.00, 95% CI, 1.64-5.52, P＜0.001), and adjuvant chemotherapy ( HR, 3.25, 95% CI, 2.01-5.27, P＜0.001) were independent prognostic factors for DFS. In patients with R0 resection, the 3-year DFS rates were 97.8% and 78.0% for those with low and intermediate NAR scores who received adjuvant chemotherapy, significantly higher than the 43.2% and 50.6% for those who did not ( P＜0.001, P=0.002). There was no significant difference in the 3-year DFS rate (54.2% vs 53.3%, P=0.214) among high NAR score patients, regardless of adjuvant chemotherapy. Conclusions:The NAR score is a robust prognostic indicator in LARC following neoadjuvant short-course radiotherapy and consolidation chemotherapy, with potential implications for subsequent decisions regarding adjuvant chemotherapy. These findings warrant further validation in studies with larger sample sizes.