OBJECTIVES: To explore the association between Chinese visceral adiposity index (CVAI) and the risk of nephrolithiasis. METHODS: This cross-sectional study analyzed data from 78 438 Chinese adults who underwent ultrasound examinations during health screening at the Health Examination Center of Affiliated Hospital of Yangzhou University. Participants were divided into quartiles (Q1-Q4 groups) based on CVAI. Multivariate logistic regression models were utilized to evaluate the association between CVAI and nephrolithiasis risk, followed by subgroup analyses to further explore potential relationships. The performance of CVAI in predicting the risk of nephrolithiasis was evaluated using receiver operating characteristic (ROC) curves. RESULTS: Increased CVAI was significantly associated with a higher risk of nephrolithiasis, with prevalence rising from 3.36% in the Q1 group to 10.67% in the Q4 group (P<0.01). In adjusted models, CVAI was positively correlated with the prevalence rate of nephrolithiasis (OR=1.002, 95%CI: 1.001-1.004, P<0.01). The risks of nephrolithiasis in the Q2, Q3, and Q4 groups were 1.196-fold (95%CI: 1.069-1.338, P<0.01), 1.260-fold (95%CI: 1.109-1.433, P<0.01), and 1.316-fold (95%CI: 1.125-1.539, P<0.01) higher than in the Q1 group, respectively. Subgroup analysis revealed that CVAI was positively associated with the risk of nephrolithiasis in male participants, individuals aged <60 years, the hypertension group, populations with or without diabetes mellitus, and the normal body mass index subgroup. Genders and age had an interaction effect on the correlation between CVAI and the risk of nephrolithiasis development (both P<0.05). The ROC curve analysis demonstrated that CVAI exhibited superior predictive efficacy compared to waist circumference, body mass index, visceral adiposity index, weight-adjusted waist index, cardiometabolic index and body shape index, with an area under the curve of 0.622. CONCLUSIONS In Chinese adults, CVAI is positively associated with the risk of nephrolithiasis development, which may serve as a potential predictive marker for nephrolithiasis.
Humans ; Nephrolithiasis/etiology* ; Male ; Female ; Middle Aged ; Cross-Sectional Studies ; Adult ; Intra-Abdominal Fat ; Risk Factors ; China/epidemiology* ; Adiposity ; Aged ; Logistic Models ; Obesity, Abdominal/epidemiology* ; East Asian People

Background and purpose:In breast cancer surgery,margin status assessment significantly impacts patient prognosis,with positive margins indicating higher recurrence and metastasis risks.Ensuring complete tumor resection is thus critical for surgical success.Indocyanine green(ICG)has garnered attention for its potential real-time imaging of breast cancer lesions under near-infrared light.This study employed ICG for intraoperative assessment of breast cancer lesion margin status and further explored the possibility of optimizing the safe margin distance surround the lesion in normal breast tissue.Methods:Clinical data of patients admitted to the Department of Thyroid and Breast Surgery,the Fourth Affiliated Hospital of Anhui Medical University(Affiliated Chaohu Hospital),from December 2021 to September 2022 were collected.A retrospective clinical study was conducted on breast cancer patients who were randomly assigned to either the ICG group or the conventional surgery group.Two to three hours before surgery,patients in the ICG group received a peripheral intravenous injection of 0.5 mg/kg ICG.Intraoperative fluorescence imaging was performed on the specimen before and after resection,as well as on the residual cavity.Near-infrared fluorescence imaging equipment was used to quantitatively measure fluorescence intensity of resected lesions at 4 directions(12,3,6,and 9 o'clock)and detect fluorescence in the residual cavity after lesion removal.Specimens were promptly sent to the pathology department for pathological examination,and safety margins of normal breast tissue in the 4 directions were recorded.The Strengthening the Reporting of Observational Studies in Epidemiology(STROBE)checklist was followed for this study.This study was approved by the Ethics Committee of the Fourth Affiliated Hospital of Anhui Medical University(Affiliated Chaohu Hospital)(No.KYXM-202310-46).Results:This study included 50 breast cancer patients,with 24 in the ICG group and 26 in the traditional surgery group.In the ICG group,fluorescence signals were detected at all lesion sites.Specifically,fluorescence density values at the lesion center,margin,and surrounding normal breast tissue were measured as 251.08±10.73,208.08±19.74,and 156.76±16.47,respectively,showing a gradual decrease from center outward with statistically significant differences(P＜0.05).Additionally,fluorescence ratios between the lesion center and margin,and center and surrounding normal tissue,were 1.22±0.13 and 1.62±0.19,respectively.After resection,abnormal fluorescence was observed in 2 of 24 cases in the residual cavity,with 1 case being invasive carcinoma with ductal carcinoma in situ and the other normal breast tissue.Ultimately,this study demonstrated that ICG achieved a sensitivity of 95.9%and a specificity of 97.9%in margin assessment.After specimen resection,the safety margins of normal glandular tissue surrounding the lesion were measured.The safety widths for the ICG group and the concurrent breast cancer surgery group were(8.36±6.42)mm and(15.08±4.75)mm,respectively.This difference was statistically significant(P＜0.05).Conclusion:ICG is a real-time,efficient,and cost-effective tracer that can be used to determine breast cancer margins,with excellent sensitivity and specificity.For early-stage breast cancer patients who are eligible for breast-conserving surgery,this tracer helps to reduce the amount of healthy breast tissue that is removed around the lesion.

Background and purpose:In breast cancer surgery,margin status assessment significantly impacts patient prognosis,with positive margins indicating higher recurrence and metastasis risks.Ensuring complete tumor resection is thus critical for surgical success.Indocyanine green(ICG)has garnered attention for its potential real-time imaging of breast cancer lesions under near-infrared light.This study employed ICG for intraoperative assessment of breast cancer lesion margin status and further explored the possibility of optimizing the safe margin distance surround the lesion in normal breast tissue.Methods:Clinical data of patients admitted to the Department of Thyroid and Breast Surgery,the Fourth Affiliated Hospital of Anhui Medical University(Affiliated Chaohu Hospital),from December 2021 to September 2022 were collected.A retrospective clinical study was conducted on breast cancer patients who were randomly assigned to either the ICG group or the conventional surgery group.Two to three hours before surgery,patients in the ICG group received a peripheral intravenous injection of 0.5 mg/kg ICG.Intraoperative fluorescence imaging was performed on the specimen before and after resection,as well as on the residual cavity.Near-infrared fluorescence imaging equipment was used to quantitatively measure fluorescence intensity of resected lesions at 4 directions(12,3,6,and 9 o'clock)and detect fluorescence in the residual cavity after lesion removal.Specimens were promptly sent to the pathology department for pathological examination,and safety margins of normal breast tissue in the 4 directions were recorded.The Strengthening the Reporting of Observational Studies in Epidemiology(STROBE)checklist was followed for this study.This study was approved by the Ethics Committee of the Fourth Affiliated Hospital of Anhui Medical University(Affiliated Chaohu Hospital)(No.KYXM-202310-46).Results:This study included 50 breast cancer patients,with 24 in the ICG group and 26 in the traditional surgery group.In the ICG group,fluorescence signals were detected at all lesion sites.Specifically,fluorescence density values at the lesion center,margin,and surrounding normal breast tissue were measured as 251.08±10.73,208.08±19.74,and 156.76±16.47,respectively,showing a gradual decrease from center outward with statistically significant differences(P＜0.05).Additionally,fluorescence ratios between the lesion center and margin,and center and surrounding normal tissue,were 1.22±0.13 and 1.62±0.19,respectively.After resection,abnormal fluorescence was observed in 2 of 24 cases in the residual cavity,with 1 case being invasive carcinoma with ductal carcinoma in situ and the other normal breast tissue.Ultimately,this study demonstrated that ICG achieved a sensitivity of 95.9%and a specificity of 97.9%in margin assessment.After specimen resection,the safety margins of normal glandular tissue surrounding the lesion were measured.The safety widths for the ICG group and the concurrent breast cancer surgery group were(8.36±6.42)mm and(15.08±4.75)mm,respectively.This difference was statistically significant(P＜0.05).Conclusion:ICG is a real-time,efficient,and cost-effective tracer that can be used to determine breast cancer margins,with excellent sensitivity and specificity.For early-stage breast cancer patients who are eligible for breast-conserving surgery,this tracer helps to reduce the amount of healthy breast tissue that is removed around the lesion.

Objective To investigate the investigation of co-morbidity etiology and prognosis analysis of chronic diseases in the elderly population. Methods The data of 1 475 elderly patients who were seen and treated in Chengdu Fifth People's Hospital from January 2019 to December 2021 were screened to analyze their disease status, co-morbidity combinations and patterns, co-morbidity influencing factors, and prognosis. Results The top four prevalence rates among 1 475 elderly patients with chronic diseases were hypertension 555 (37.63%), gastric or gastrointestinal diseases 445 (30.17%), arthritis or rheumatism 427 (28.95%), and diabetes 329 (26.58%). 1034 co-morbidities were found in 1475 elderly patients with chronic diseases, with a co-morbidity rate of 70.10%. The binary disease combination accounted for 58.41% and the ternary disease combination accounted for 41.59%. Female, age >70 years, family history of chronic diseases, overweight/obesity, daily physical inactivity, history of alcohol/smoking, poor sleep quality, and poor dietary habits were the independent influencing factors for co-morbidity in elderly patients with chronic diseases (OR_female=2.413, ORage ≥ 70=1.670, OR_{history of alcohol consumption}family history of chronic diseases=2.846, OR_{history of alcohol consumption}overweight/obesity=2.570, ORdaily inactivity=1.802, OR_{history of alcohol consumption}=3.543, ORhistory of smoking=1.784, OR_{poor sleep quality}=2.128, OR_{unhealthy dietary habits}=2.085, all P<0.05). Compared with elderly patients with chronic diseases without co-morbidity, patients with co-morbidity had higher odds of exacerbation of the original disease/acute readmission and lower odds of new chronic disease (χ²_{primary exacerbation/emergency readmission}=10.726, χ²_{new chronic disease}=5.873 , all P<0.05). Conclusion Gender, age, chronic disease history, BMI, and lifestyle habits are important factors influencing co-morbidity in elderly patients with chronic diseases, and patients with co-morbidity have a relatively poor prognosis.

OBJECTIVES: To classify bladder cancer based on immune cell infiltration score and to construct a risk assessment model for prognosis of patients. METHODS: The transcriptome data and data of breast cancer patients were obtained from the TCGA database. The single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was realized by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were extracted. A risk scoring model and a nomogram for risk assessment of prognosis for bladder cancer patients were constructed and verified. RESULTS: The immune cell infiltration scores of normal tissues and tumor tissues were calculated, and B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. Breast cancer patients were clustered into two groups (Cluster 1 and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). WGCNA screened out 35 genes related to key immune cells, and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients. CONCLUSIONS According to the immune cell infiltration score, bladder cancer patients can be classified. And the bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.