BACKGROUND:Osteosarcoma has a complex pathogenesis and a poor prognosis.While advancements in medical technology have led to some improvements in the 5-year survival rate,substantial progress in its treatment has not yet been achieved. OBJECTIVE:To screen key molecular markers in osteosarcoma,analyze their relationship with osteosarcoma treatment drugs,and explore the potential disease mechanisms of osteosarcoma at the molecular level. METHODS:GSE99671 and GSE284259(miRNA)datasets were obtained from the Gene Expression Omnibus database.Differential gene expression analysis and Weighted Gene Co-expression Network Analysis(WGCNA)on GSE99671 were performed.Functional enrichment analysis was conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes separately for the differentially expressed genes and the module genes with the highest positive correlation to the disease.The intersection of these module genes and differentially expressed genes was taken as key genes.A Protein-Protein Interaction network was constructed,and correlation analysis on the key genes was performed using CytoScape software,and hub genes were identified.Hub genes were externally validated using the GSE28425 dataset and text validation was conducted.The drug sensitivity of hub genes was analyzed using the CellMiner database,with a threshold of absolute value of correlation coefficient|R|>0.3 and P<0.05. RESULTS AND CONCLUSION:(1)Differential gene expression analysis identified 529 differentially expressed genes,comprising 177 upregulated and 352 downregulated genes.WGCNA analysis yielded a total of 592 genes with the highest correlation to osteosarcoma.(2)Gene Ontology enrichment results indicated that the development of osteosarcoma may be associated with extracellular matrix,bone cell differentiation and development,human immune regulation,and collagen synthesis and degradation.Kyoto Encyclopedia of Genes and Genomes enrichment results showed the involvement of pathways such as PI3K-Akt signaling pathway,focal adhesion signaling pathway,and immune response in the onset of osteosarcoma.(3)The intersection analysis revealed a total of 59 key genes.Through Protein-Protein Interaction network analysis,8 hub genes were selected,which were LUM,PLOD1,PLOD2,MMP14,COL11A1,THBS2,LEPRE1,and TGFB1,all of which were upregulated.(4)External validation revealed significantly downregulated miRNAs that regulate the hub genes,with hsa-miR-144-3p and hsa-miR-150-5p showing the most significant downregulation.Text validation results demonstrated that the expression of hub genes was consistent with previous research.(5)Drug sensitivity analysis indicated a negative correlation between the activity of methotrexate,6-mercaptopurine,and pazopanib with the mRNA expression of PLOD1,PLOD2,and MMP14.Moreover,zoledronic acid and lapatinib showed a positive correlation with the mRNA expression of PLOD1,LUM,MMP14,PLOD2,and TGFB1.This suggests that zoledronic acid and lapatinib may be potential therapeutic drugs for osteosarcoma,but further validation is required through additional basic experiments and clinical studies.

Objective To investigate the association between body roundness index (BRI) and urge urinary incontinence (UUI) in a nationally representative U.S.population, so as to provide a new indicator for the prevention and management of UUI. Methods A total of 17226 participants from the 2015—2023 National Health and Nutrition Examination Survey (NHANES) were included in the study. The association between BRI and UUI was assessed with weighted multivariable logistic regression, and the nonlinear relationship was analyzed with weighted restricted cubic spline (RCS). Subgroup analyses were conducted based on demographic and clinical characteristics to explore potential heterogeneity in the association. Receiver operating characteristic (ROC) curves were plotted to compare the predictive performance of BRI with body mass index (BMI) and waist circumference for UUI, and the area under the curve (AUC) was calculated. Results A total of 4879 UUI patients (28.32%, UUI group) and 12347 non UUI participants (71.68%, NUUI group) were included in the 17226 participants. Significant differences were observed between the UUI and NUUI groups in terms of age, sex, race, marital status, BIM, height, waist circumference, poverty income ratio (PIR), diabetes, alcohol consumption and smoking (P<0.001). The participants in the UUI group had significantly higher BRI than that in the NUUI group [ (6.53±2.63) vs. (5.47±2.34), P<0.001]. As BRI increased from Q2 to Q4, the incidence of UUI also rose (P_(trend)<0.0001). After the confounding factors were fully adjusted, participants in the Q4 group had a 104% increased risk of UUI compared to the Q1 group (OR=2.04, 95% CI:1.81-2.30, P<0.0001). There was a significant positive nonlinear trend in the dose-response relationship between UUI and BRI (P_(overall trend)<0.001, P_(nonlinear association)=0.886). Subgroup analysis showed that the association between UUI and BRI was more significant in diabetic patients, different racial and BMI stratifications (P<0.05).ROC curve analysis showed that, compared with BMI (AUC=0.59) and waist circumference (AUC=0.59), BRI demonstrated superior predictive accuracy (AUC=0.63, P<0.001). Conclusion Based on US 2015—2023 NHANES, the study shows that increased BRI is independently associated with an increased risk of UUI, and its predictive performance is superior to traditional obesity metrics.BRI has the potential to serve as a risk stratification tool for UUI.

OBJECTIVE: To explore the relationship between the geriatric nutritional risk index (GNRI) and cognitive function. METHODS: A cross-sectional study method was conducted. People aged ≥ 60 years from the National Health and Nutrition Examination Survey (NHANES) databases from 1999 to 2002 and 2011 to 2014 were included as study subjects. The participants were divided into three groups based on their GNRI scores: a medium-high risk group (82 ≤ GNRI < 92), a low risk group (92 ≤ GNRI < 98), and a no-risk group (GNRI ≥ 98). Demographic characteristics (gender, age, race, education), chronic diseases [chronic bronchitis, emphysema, thyroid problems, coronary heart disease, angina pectoris, stroke, hypertension, diabetes mellitus, and depression score on the patient health questionnaire (PHQ-9)], lifestyle habits (history of smoking, hours of sleep), etc., were collected. Cognitive function was assessed using the consortium to establish a registry for Alzheimer's disease word learning subtest (CERAD-WL), animal fluency test (AFT), and digit symbol substitution test (DSST) for the 2011-2014 data, while only the DSST was used for the 1999-2002 data. Differences in the above information among the GNRI cohorts were compared. Factors affecting cognitive function in the population were analyzed using multifactorial Logistic regression. RESULTS: 2 653 participants from 2011 to 2014 and 2 380 participants from 1999 to 2002 were enrolled, with a total of 5 033 participants in the study. There were statistically significant differences in age, stroke, diabetes mellitus, DSST score, AFT score, CERAD score test 1 recall (Cst1), and CERAD score test 2 recall (Cst2) among the GNRI groups. Multifactorial Logistic regression analysis of data from 2011 to 2014 showed that in model 3 (DSST score, age, gender, race, marriage, education, hours of sleep, history of smoking, emphysema, thyroid problems, chronic bronchitis, coronary heart disease, angina pectoris, hypertension, diabetes mellitus, depression score on the PHQ-9, and stroke) adjusted for all covariates, GNRI was a protective factor for DSST [odds ratio (OR) = 1.03, 95% confidence interval (95%CI) was 1.00 to 1.05, P = 0.03]; Logistic regression analyse for 1999 to 2002 and 2011 to 2014 showed a significant association even after adjustment for covariates (OR = 1.02, 95%CI was 1.00 to 1.03, P = 0.02). Subgroup Logistic regression analyses of the total population from 2011 to 2014 showed a significant association between GNRI and DSST scores (OR = 1.02, 95%CI was 1.01 to 1.03, P < 0.001), with significant associations in the age subgroups of 60 to 64 years old, across gender, non-Hispanic Whites and Blacks, by education, and by marital status associations were significant (all P < 0.05). Subgroup Logistic regression analyse of the total populations from 1999 to 2002 and 2011 to 2014 showed a significant association between the GNRI and DSST score (OR = 1.01, 95%CI was 1.01 to 1.02, P < 0.001), but did not show a significant year difference (interaction P = 0.503), and the newly found in the smoking population the association was also more significant (P < 0.01). CONCLUSION The GNRI correlates with the presence of cognitive functions related to processing speed, sustained attention, and executive function, and may be able to serve as an indicator for the assessment or prediction of related cognitive functions.
Humans ; Cross-Sectional Studies ; Aged ; Middle Aged ; Nutrition Surveys ; Cognition ; Female ; Male ; Nutritional Status ; Risk Factors ; Geriatric Assessment

Objective To develop a scientific,systematic,and operable healthcare quality evaluation system for Nation-al Regional Medical Centers(NRMCs),providing a theoretical basis and practical tool for objectively assessing their construction outcomes and guiding high-quality development.Methods Based on the classic"Structure-Process-Outcome"(SPO)quality management model,and aligned with national policy directives and the functional positioning of regional medical centers,a pre-liminary set of evaluation indicators was screened and an indicator system was constructed through literature review,policy analy-sis,and field investigations.Guangxi Hospital Division of The First Affiliated Hospital,Sun Yat-sen University was selected as the study subject,and cross-sectional data from March 2023 to June 2025 were collected for empirical application.Results A healthcare quality evaluation system for NRMCs was established,comprising 3 first-level dimensions(Structure Quality,Process Quality,Outcome Quality),10 second-level indicators,and 66 third-level indicators.This system covers multiple aspects,inclu-ding resource allocation,healthcare service efficiency,clinical practices,patient outcomes,and social benefits.Empirical results indicated that the center demonstrated a consistent upward trend in key indicators such as"Proportion of Discharged Patients Un-dergoing Level-4 Surgeries"(O1.2)and"DRG-CMI Value"(O2.1),while"Average Length of Hospital Stay"(P3.1)and"Cost Consumption Index"(O2.3)showed a steady decline.The indicator system effectively revealed the center's progress in en-hancing regional influence and operational efficiency.Conclusion The developed healthcare quality evaluation system is well-grounded in theory and practice,combining scientific rigor with policy relevance,and can serve as a decision-support tool for quality assessment and improvement in National Regional Medical Centers.

Objective To develop a scientific,systematic,and operable healthcare quality evaluation system for Nation-al Regional Medical Centers(NRMCs),providing a theoretical basis and practical tool for objectively assessing their construction outcomes and guiding high-quality development.Methods Based on the classic"Structure-Process-Outcome"(SPO)quality management model,and aligned with national policy directives and the functional positioning of regional medical centers,a pre-liminary set of evaluation indicators was screened and an indicator system was constructed through literature review,policy analy-sis,and field investigations.Guangxi Hospital Division of The First Affiliated Hospital,Sun Yat-sen University was selected as the study subject,and cross-sectional data from March 2023 to June 2025 were collected for empirical application.Results A healthcare quality evaluation system for NRMCs was established,comprising 3 first-level dimensions(Structure Quality,Process Quality,Outcome Quality),10 second-level indicators,and 66 third-level indicators.This system covers multiple aspects,inclu-ding resource allocation,healthcare service efficiency,clinical practices,patient outcomes,and social benefits.Empirical results indicated that the center demonstrated a consistent upward trend in key indicators such as"Proportion of Discharged Patients Un-dergoing Level-4 Surgeries"(O1.2)and"DRG-CMI Value"(O2.1),while"Average Length of Hospital Stay"(P3.1)and"Cost Consumption Index"(O2.3)showed a steady decline.The indicator system effectively revealed the center's progress in en-hancing regional influence and operational efficiency.Conclusion The developed healthcare quality evaluation system is well-grounded in theory and practice,combining scientific rigor with policy relevance,and can serve as a decision-support tool for quality assessment and improvement in National Regional Medical Centers.

Objective    To retrospectively analyze the surgical treatment of Stanford type A aortic dissection after coronary artery stenting, and to explore the surgical techniques and surgical indications. Methods    Clinical data of 1 246 consecutive patients who underwent operations on Stanford type A aortic dissection from April 2016 to July 2019 in Beijing Anzhen Hospital were retrospectively analyzed. Patients with Stanford type A aortic dissection after coronary artery stenting were enrolled. Results    Finally 19 patients were collected, including 16 males and 3 females with an average age of 54±7 years ranging from 35 to 66 years. There were 11 patients in acute phase, 15 patients with AC (DeBakey Ⅰ) type and 4 patients with AS (DeBakey Ⅱ) type. In AC type, there were 10 patients receiving Sun's surgery and 5 patients partial arch replacement. Meanwhile, coronary artery bypass grafting was performed in 7 patients and mitral valve replacement in 1 patient. Stents were removed from the right coronary artery in 4 patients. In this group, 1 patient died of multiple organ failure in hospital after operation combined with malperfusion of viscera. Eighteen patients recovered after treatment and were discharged from hospital. The patients were followed up for 30 (18-56) months. One patient underwent aortic pseudoaneurysm resection, one thoracic endovascular aortic repair, one emergency percutaneous coronary intervention due to left main artery stent occlusion, and one underwent femoral artery bypass due to iliac artery occlusion. Conclusion    Iatrogenic aortic dissection has a high probability of coronary artery bypass grafting at the same time in patients with Stanford type A aortic dissection after coronary artery stenting. Complicated type A aortic dissection after percutaneous coronary intervention should be treated with surgery aggressively.