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.

OBJECTIVE To evaluate the clinical comprehensive value of four Chinese patent medicines （Xuezhikang， Zhibitai， Zhibituo， Jiangzhiling） in the treatment of hyperlipidemia， and provide a reference for rational clinical drug use. METHODS A clinical comprehensive evaluation index system was established in accordance with the Evidence and Value： Impact on Decision-Making （EVIDEM） framework and Technical Guideline for Clinical Comprehensive Evaluation of Cardiovascular Drugs （2022 edition， trial implementation）. CNKI， Wanfang data， VIP， PubMed， ScienceDirect， Embase and official websites were retrieved to collect the literature such as drug instructions， guidelines and consensus statements， and systematic reviews/meta-analyses for the four Chinese patent medicines. A comprehensive evaluation was conducted from seven dimensions： effectiveness， safety， economy， suitability， accessibility， innovation and characteristics of traditional Chinese medicine. RESULTS This evaluation index system included 7 first-level indicators， 15 second-level indicators and 30 third-level indicators. Xuezhikang achieved the highest comprehensive evaluation score of 81.4 points， and was classified as class Ⅰ recommendation. Zhibitai with 76.0 points and Zhibituo with 60.9 points were both classified as class Ⅱ recommendation. Jiangzhiling with 48.8 points was classified as class Ⅳ recommendation. CONCLUSIONS Xuezhikang demonstrates the optimal clinical comprehensive value for treating hyperlipidemia. Zhibitai exhibits certain advantages in terms of safety and characteristics of traditional Chinese medicine； Zhibituo shows a moderate performance in all aspects； Jiangzhiling has a relatively low score. Appropriate medicines can be selected clinically according to actual conditions and patients’ characteristics.

Objective: To evaluate the incremental vaccine effectiveness (VE) of two dose of the mumps containing vaccine (MuCV) in chidren, so as to provide a basis for optimizing mumps immunization strategies. Methods: A 1∶2 frequency matched case-control study was conducted by using reported mumps cases in childcare centers or schools from Lu an, Hefei, Ma anshan and Huainan cities of Anhui Province from September 1, 2023 to June 30, 2024, as a case group(383 cases). And healthy children in the same classroom were selected as a control group(766 cases). The MuCV immunization histories of participants were collected to estimate the incremental VE of the second dose of MuCV against mumps. Group comparisons were performed using the Chi square test or t-test. For matched case-control pairs, the Cox regression model was employed to calculate the odds ratio (OR) with 95% confidence interval (CI) for two dose MuCV vaccination and to estimate the incremental vaccine effectiveness (VE). Results: There were no statistically significant differences between the case and control groups regarding gender, age, dosage of MuCV vaccination and the time interval since the last dose vaccination( χ 2/t=0.05, 0.20, 0.94, -0.02, P >0.05). The proportions of the case and control groups vaccinated with two doses of MuCV were 26.63% and 29.37%, respectively, and the overall incremental VE of the second dose of MuCV was 40.73% (95% CI=3.03%-63.77%, P <0.05). Subgroup analyses revealed that the incremental VE for children with a period of ≥1 year between the two doses of MuCV was 54.13% (95% CI=1.90%-78.56%, P <0.05), while for children with a period of <1 year, it was 30.63% (95% CI=-28.59%-62.58%, P >0.05). The incremental VE of the second dose of MuCV was 30.36% (95% CI=-25.95%-61.50%, P >0.05) in kindergarten children and 66.73% (95% CI=14.92%-86.99%, P <0.05) in elementary and secondary school students. The incremental VE was 28.78% (95% CI=-27.46%-60.21%, P >0.05) within five years of the last dose of MuCV vaccination and 66.07% (95% CI=-41.56%-91.87%, P >0.05) for vaccinations administered beyond five years. Conclusions The second dose of MuCV may offer additional protection for children; however, extending the interval between two dose of MuCV (<1 year) has shown limited incremental protective effects. Therefore, it is crucial to consider optimizing current immunization strategies for mumps.

ObjectiveTo investigate the current status of latent tuberculosis infection (LTBI) among the elderly population in rural areas of Changxing County, Zhejiang Province, and to provide an evidence for the development of LTBI prevention and control measures. MethodsBetween January and May 2024, elderly individuals participating in urban and rural residents’ health checkups were screened for Mycobacterium tuberculosis infection using a domestically produced interferon-γ release assay (IGRA) kit. Individuals tested positive by IGRA but without active tuberculosis were classified as LTBI cases. The prevalence of LTBI among the participants was subsequently analyzed. ResultsAmong the 6 765 subjects, 637 tested positive by IGRA, including one identified active tuberculosis patient, resulting in a LTBI prevalence rate of 9.40%. There was a statistically significant difference in positivity rates across different IGRA methodologies (χ²=35.530, P<0.001). Higher LTBI rate was observed in males, individuals with a history of diabetes mellitus, and those with a history of pulmonary tuberculosis, exhibiting statistically significant differences (χ²=32.401, P<0.001; χ²=5.789, P=0.020; χ²=39.248, P<0.001, respectively.) No statistically significant difference in LTBI rate was found across different age groups (χ²=0.238, P=0.971). ConclusionThe prevalence of LTBI among the elderly rural residents in Changxing County is relatively low. Male, individuals with a history of diabetes mellitus, and those with a history of pulmonary tuberculosis have an increased risk of LTBI, warranting targeted risk monitoring and timely interventions.

Diabetic retinopathy(DR)is one of the most common and severe microvascular complications in diabetic patients and has become one of the leading causes of blindness worldwide. With the continuous rise in the prevalence of diabetes, in-depth exploration of the pathogenesis of DR and effective intervention measures is of great clinical significance. The mechanistic target of rapamycin(mTOR), as a protein kinase, is widely involved in cellular processes such as growth, metabolism, and autophagy. Research indicates that the mTOR signaling pathway plays a crucial regulatory role in the pathological progression of DR, and its abnormal activity can disrupt retinal cell autophagy function, thereby accelerating cellular damage and disease progression. Autophagy, as an important regulatory mechanism for cellular homeostasis, maintains cellular functional balance by clearing damaged organelles and protein aggregates. This article provides a systematic review of the structural and functional aspects of the mTOR signaling pathway, the molecular regulatory mechanisms of autophagy, and their roles in retinal pathological changes. By summarizing current research findings, the article aims to clarify the key regulatory role of the mTOR-autophagy axis in DR, providing theoretical support for elucidating the molecular pathogenesis of DR and offering potential targets and research directions for developing novel targeted therapeutic strategies, thereby holding significant scientific and clinical value.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Objective This study aimed to investigate the relationship between multiple inflammatory markers and poor outcome in patients with intracerebral hemorrhage,and to compare their predictive abilities.Methods We retrospectively analyzed the patients with intracerebral hemorrhage admitted to the Department of Neurology of the First Affiliated Hospital of Nanchang University from January 1,2015 to March 31,2023.According to the Modified Rankin Scale at 90 days after onset,the patients were divided into good outcome(mRS≤2 points)and poor outcome(mRS score≥3 points).Clinical information,laboratory examinations and follow-up data were compared between the two groups.Inflammatory markers include neutrophil to lymphocyte ratio(NLR),platelet to lymphocyte ratio(PLR),monocyte to high-density lipoprotein ratio(MHR),systemic inflammatory response index(SIRI),systemic immune-inflammation index(SII),white blood cell count to mean platelet volume ratio(WMR),lymphocyte to monocyte ratio(LMR),and systemic coagulation-inflammation index(SCI).Univariate and multivariate logistic regression analysis were used to analyze the predictors of poor prognosis after intracerebral hemorrhage,and receiver operating characteristic curve(ROC)was constructed to compare their predictive ability.Results A total of 510 patients with intracerebral hemorrhage were included.Of those,297(58.2%)had good outcome,and 213(41.8%)had poor outcome.Comparison of baseline characteristics demonstrated that patients with poor prognosis had higher levels of white blood cells,neutrophils,high-density lipoprotein,fibrinogen,NLR,PLR,SIRI,SII,WMR at admission,larger baseline hematoma volume and baseline perihematomal edema volume,a higher proportion of lobar hemorrhage,older age,and lower levels of platelets,lymphocytes,LMR,and SCI(P<0.05).Multivariate regression analysis showed that NLR(OR:1.081,95%CI:1.032～1.1131,P=0.001),SIRI(OR:1.089,95%CI:1.014～1.169,P=0.019),SII(OR:1.000,95%CI:1.000～1.001,P=0.011),WMR(OR:2.627,95%CI:1.267～5.445,P=0.009)were independently associated with poor prognosis in patients with ICH.In ROC analysis,the area under the curve of NLR(0.729,95%CI:0.685～0.774)was higher than SIRI(0.692,95%CI:0.645～0.738),SII(0.688,95%CI:0.641～0.735)and WMR(0.65,95%CI:0.602～0.698)for predicting poor outcomes.Conclusion NLR,SIRI,SII and WMR at admission are independently associated with poor outcomes in patients with intracerebral hemorrhage,and NLR has the strongest predictive ability.

Oropharyngeal squamous cell carcinoma(OPSCC)is a malignant tumor originating from the squamous epithelium of the oro-pharyngeal mucosa,accounting for more than 90％of oropharyngeal malignancies.In recent years,human papillomavirus(HPV)infec-tion has become one of the primary etiological factors of oropharyngeal squamous carcinoma.The incidence of HPV-associated oropharyn-geal squamous carcinoma has been rising annually,with a noticeable trend toward younger populations,posing a significant threat to hu-man health.Due to the distinct biological behavior and clinical characteristics of HPV-associated oropharyngeal squamous carcinoma com-pared to its non-HPV-related counterpart,the diagnostic and treatment strategies for oropharyngeal squamous carcinoma have undergone substantial changes.Prevention and screening for oropharyngeal squamous carcinoma are of critical importance.The diagnostic and treat-ment process involves multi-disciplinary collaboration,including oral and maxillofacial surgery,otolaryngology,head and neck surgery,oncology,radiology and pathology.Based on evidence from clinical practice,a comprehensive,integrated diagnostic and therapeutic ap-proach has been established,centered around the concept of"prevention,screening,diagnosis,treatment,and rehabilitation",covering the entire patient lifecycle and providing a valuable reference for clinical practice.

Objective:To analyze the effects of the Focused Solution Model Nursing intervention on quality of life,negative emotions of the patients with prostate cancer.Methods:A total of 82 prostate cancer patients who were diagnosed and treated at the General Hospital of Eastern Theater Command between September 2022 and September 2024 were included and randomly divided into study group and control group by the method of random number table,with 41 patients in each group.The patients in the study group were treated with Focused Solution Model Nursing intervention.And the routine care was used in the control group The quality of life and negative emotions were compared between the two groups by using the scales of World Health Organization Quality of Life-Brief(WHOQOL-BREF),HAMA and HAMD.Results:Compared to the control group,the patients in the study group exhibited signifi-cantly higher scores in the physiological,psychological,environmental,and social relationship domains of the WHOQOL-BREF scale(P＜0.05).The scores of HAMA and HAMD in study group were lower than those of the control group(P＜0.05).Additionally,all subscales of the Social Impact Scale including social exclusion,internalized shame,social isolation and economic discrimination were significantly lower than those of the study group(P＜0.05).Conclusion:Focused Solution Model Nursing intervention can effec-tively improve the quality of life and negative emotions of the prostate cancer patients in the clinical treatment.