BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

AIM:To investigate the compositional and structural differences in the ocular surface microbiota of patients with primary pterygium during the progressive and stationary stages, and to identify key microbial biomarkers associated with pterygium progression.METHODS: A cross-sectional study design was adopted. Patients with progressive pterygium(PPG group), stationary pterygium(SPG group), and age- and sex-matched healthy volunteers(HCG group)were enrolled. Conjunctival swabs were collected from the affected eye(or the healthy eye in controls)of each participant. 16S rRNA gene amplicon sequencing of the V3V4 region was performed. The analytical workflow encompassed rarefaction curve analysis, Alpha/Beta diversity analyses, ASV analysis. LEfSe analysis and the Kruskal-Wallis H test were used to screen differential microbiota.RESULTS: Final enrollment included 28 patients(28 eyes)in each group: PPG(11 males, 17 females; mean age 60.54±8.44 y), SPG(10 males, 18 females; mean age 61.82±10.21 y), and HCG(12 males, 16 females; mean age 64.07±7.27 y). No significant differences were found among the three groups in baseline characteristics(all P>0.05). The number of valid sequences and the rarefaction curves confirmed sufficient sequencing depth. Alpha/Beta diversity analyses showed no statistically significant differences among the three groups(all P>0.05), indicating that the overall structure of the ocular surface microbiota was relatively stable. LEfSe analysis(LDA score>3.0)revealed that the Propionibacteriales, the Propionibacteriaceae, the Cutibacterium and the Comamonadaceae were significantly enriched in the PPG group. Following Kruskal-Wallis H tests with Bonferroni correction for multiple comparisons, the relative abundances of Propionibacteriales, Propionibacteriaceae, and Cutibacterium were significantly higher in PPG group than in the HCG group(all PSymbol|@@0.05), but did not differ significantly between the PPG and SPG groups(all P>0.05). The abundance of Comamonadaceae was significantly higher in SPG group than in HCG group(PSymbol|@@0.05), whereas PPG group not differ from either group(all P>0.05).CONCLUSION: Cutibacterium, the representative genus of the propionibacterial group, tends to be enriched in eyes with progressive pterygium, indicating a potential association between this genus and pterygium progression.

Network analysis serves as a critical methodological approach for enhancing the efficiency of health management in elderly patients with chronic diseases,demonstrating unique advantages in precisely identifying intervention targets.This article comprehensively reviews recent advancements in the application of network analysis for managing chronic conditions in older adults,systematically organizing research findings across 3 analytical dimensions:cross-sectional networks,dynamic networks,and individualized networks.The review aims to provide healthcare professionals with a robust theoretical foundation and methodological support for developing personalized health management strategies.

Head and neck squamous cell carcinoma(HNSCC)progression is closely associated with chronic inflammation mediated by the NLRP3 inflammasome within the tumor microenvironment(TME).The NLRP3 inflammasome integrates pathogen-associated and stress-in-duced signals to dynamically regulate IL-1β/IL-18 secretion and pyroptosis.Through its action,NLRP3 inflammasome exhibits a functional du-ality,exerting tumor-suppressive effects by activating the Caspase-1/GSDMD pathway to induce tumor cell pyroptosis while also driving ma-lignant progression through remodeling of immunosuppressive TMEs.This remodeling includes driving M2-type macrophage polarization and suppressing NETs-associated pyroptosis,thereby promoting cancer stem cell maintenance and contributing to chemotherapy resistance.Preclinical studies have confirmed that NLRP3-targeted strategies,including small-molecule inhibitors(MCC950 and BAY11-7082),Bacopa monnieri,and IL-1 signaling biologics,significantly suppress tumor growth and metastasis in HNSCC and other cancers.In this review,we systematically decipher the NLRP3 regulatory network,evaluate the translational potential of targeted interventions,and offer novel thera-peutic avenues to overcome treatment barriers in HNSCC.

Objective Focusing on gynecological surgery,we constructed a prediction model for surgical duration by extracting features from unstructured surgical planning texts and integrating multimodal data via artificial intelligence technology.Methods The clinical data of 34 614 patients who underwent gynecologic surgeries at West China Second University Hospital,Sichuan University between January 2022 and October 2024 were collected.An embedding-transformer model was constructed to convert surgical planning texts into a one-dimensional numerical feature,referred to as the step feature.The predictive value of the step feature was assessed by comparing the performance improvements of linear regression,random forest,eXtreme Gradient Boosting(XGBoost),support vector regression,K-nearest neighbor regression,and artificial neural network algorithms in two scenarios—with and without the step feature as an input.The out-of-sample prediction accuracy of the models was assessed using mean absolute error(MAE),root mean squared error(RMSE),and R-squared(R2).Furthermore,the model interpretability was examined using SHapley Additive exPlanations(SHAP)values.Results SHAP results showed that the step feature had the highest predictive contribution.Temporal factors in surgical scheduling also influenced gynecological surgery duration.The XGBoost model demonstrated optimal performance on the test set,significantly improving prediction accuracy with a 40.43%increase in R2,while reducing MAE and RMSE by 21.27%and 20.13%,respectively,compared to the baseline model without the step feature.Conclusion The embedding-transformer model developed in this study effectively extracts features from surgical planning texts and enhances the predictive performance of machine learning models.The XGBoost prediction model can assist hospital administrators in implementing more refined management of gynecological surgeries and improving the utilization efficiency of surgical resources.

In recent years, immunotherapy represented by immune checkpoint inhibitors has made important breakthroughs in the treatment of various malignant tumors. At present, immune checkpoint inhibitors have been widely used in patients with metastatic and recurrent advanced head and neck squamous cell carcinoma (HNSCC). Biomarkers play an important role in predicting the efficacy of immunotherapy for HNSCC. The biomarkers that can predict immunotherapy effect of patients with HNSCC mainly include programmed-death receptor ligand 1, tumor mutation load, human papillomavirus, and immune cell related indicators. This article reviews the predictive markers of immunotherapy for HNSCC and the current research situation. The further understanding of the predictive markers can help to accurately screen out the patients receiving the immunotherapy and to improve the prognosis of patients.

Objective:To investigate the efficacy and safety of daratumumab-based regimens for the treatment of relapsed/refractory multiple myeloma (RRMM).Methods:A retrospective case series study was conducted. Thirty-seven RRMM patients treated with daratumumab-based regimens at Shanxi Bethune Hospital from January 2017 to November 2023 were selected, and their efficacy and adverse reactions were analyzed.Results:The median age [ M ( Q1, Q3)] of 37 RRMM patients was 62 (56, 68) years, the median number of previous treatment lines was 2 (1, 3.5) lines, 12 cases (32.4%) had extramedullary lesions, 12 cases (32.4%) had lactate dehydrogenase (LDH) ≥ 245 U/L, and 11 cases (29.7%) had previously received the third-line or more treatment. Among 27 patients who completed fluorescence in situ hybridization testing, 8 cases (29.6%) had high-risk cytogenetical changes. The median time from diagnosis to use of daratumumab was 23.1 (5.9, 52.0) months. The overall response rate (ORR) of 37 RRMM patients after treatment was 75.7% (28/37), with ORR of 88.0% (22/25) and 50.0% (6/12) for patients without and with extramedullary lesions, respectively, and the difference was statistically significant ( P = 0.036). The median follow-up time was 12.3 (4.6, 22.7) months, the median progression-free survival (PFS) time was 7.8 months (95% CI: 2.0- 13.7 months), and the median overall survival (OS) time was 22.4 months (95% CI: 17.5-29.5 months). The median PFS time for patients without and with extramedullary lesions was 11.8 and 4.2 months, and the median OS time was 23.5 and 8.3 months, respectively, and the differences in PFS and OS between the two were statistically significant (both P < 0.05); the median PFS time for patients with LDH ≥ 245 U/L and < 245 U/L was 6.5 and 11.9 months, and the median OS time was 30.2 and 12.1 months, respectively, and the differences in PFS and OS between the two were statistically significant (both P < 0.05). The incidence of non-hematological adverse reactions was 32.4% (12/37), with the most common being infusion-related adverse reactions (29.7%, 11/37), all of which were grade 1-2; the incidence of ≥ grade 3 hematological adverse reactions was 13.5% (5/37), with thrombocytopenia being the most common (8.1%, 3/37). Conclusions:The ORR of RRMM patients treated with daratumumab-based regimens is high, and the adverse reactions are tolerable.

Objective To analyze the clinical characteristics and interventional treatment effects of pa-tients with renal arteriovenous fistula in this hospital over the past 10 years.Methods A retrospective analy-sis was conducted on the clinical and imaging data of 40 patients with renal arteriovenous fistula who were treated in this hospital from October 2012 to October 2022.After classifying the fistula type using digital sub-traction angiography(DSA),an individualized embolization plan was developed by a vascular surgery team.Postoperatively,the embolization materials,embolization success rate,clinical success rate,perioperative renal function changes and complications were analyzed.Results A total of 29 cases(72.5%)had acquired renal arteriovenous fistula,while 11 cases(27.5%)had non-acquired renal arteriovenous fistula.Forty-three super-selective transcatheter arterial embolization(TAE)procedures were performed,achieving a technical success rate of 100.0%(40/40)and a clinical success rate of 95.0%(38/40).The mean serum creatinine levels after TAE were(91.39±23.72)mmol/L in the non-acquired group and(105.94±35.51)mmol/L in the acquired group.No statistically significant difference was observed in perioperative serum creatinine changes between the two groups(P=0.095).No severe complications such as renal function deterioration occurred postopera-tively in any patient.Conclusion Gross hematuria serves as the primary clinical manifestation of renal arterio-venous fistula,with abdominal pain being more prevalent in non-acquired renal arteriovenous fistula.The em-bolization approach combining coils with polyvinyl alcohol particles demonstrates favorable safety and efficacy in treating renal arteriovenous fistula.

BACKGROUND:In the research and application of neural stem cells,cell culture and passage are key links,which directly affect the quality of cells and experimental results.It is of great significance to find the most suitable digestive enzymes that can maintain the biological characteristics of embryonic mouse spinal cord neural stem cells and enhance their passage efficiency.OBJECTIVE:To explore the most suitable digestive enzyme for passage of neural stem cells from the spinal cord of embryonic mice.METHODS:Microscopic dissection was used to isolate and extract spinal cord tissue from E14 d embryonic mice,which was cultured in DMEM/F12 serum-free medium containing epidermal growth factor,basic fibroblast growth factor,and B27.After spherulation,Nestin and Sox2 immunofluorescence identification was performed.During neural stem cell passage and culture,single-cell suspensions were prepared using trypsin,papain,and TrypLETM Express enzyme digestion combined with blow molding.The cell dispersion and spheroidization were observed,and passage 3 cells were stained with propidium iodide to detect cell death.Cell proliferation was detected by counting the total number of cells.Immunofluorescence staining,western blot assay and RT-PCR were used to detect the expression of Olig2,Tuj1,GFAP,and NeuN at the protein and mRNA levels and to identify cell differentiation.RESULTS AND CONCLUSION:After 72 hours of culture,E14 d embryonic mouse spinal cord tissue cells could form suspended neurospheres,which could be passaged after 5-7 days.Compared with trypsin and papain,TrypLETM Express enzyme combined with blow beating method was used for passage.The cell dispersion rate was high,the activity was good,and more NeuN-and Tuj1-positive neurons differentiated.This study optimized the culture and passaging process of neural stem cells,laying a foundation for further research on stem cell transplantation therapy for spinal cord diseases.