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 We aimed to investigate the TCM syndrome characteristics and risk factors of early death in higher-risk patients with myelodysplastic syndromes with excess blasts (HR-MDS-EB). Methods The data of 57 HR-MDS-EB patients in the hematology ward of Xiyuan Hospital, China Academy of Chinese Medical Sciences admitted between January 2013 and August 2020 were analyzed. Patients were divided into an early death group and a non-early death group according to the survival time. The basic information, clinical characteristics, specialized examination, complete blood cell counts and laboratory examination result for first diagnosis, TCM syndrome characteristics, and other data of patients were collected and analyzed to screen the risk factors of death.Results There were statistically significant differences in Charlson comorbidity index (CCI) score, Barthel index score, platelet count at first diagnosis, blast ratio in peripheral blood, lactate dehydrogenase levels, blazing pathogenic heat pattern, and constipation between the early death group and the non-early death group (P<0.05). According to the result of the above univariate analysis, logistic multivariate regression analysis was performed for significant factors P<0.05. CCI score had statistically significant effects on the risk of death (P<0.05).Conclusion Higher CCI score is an independent risk factor for early death in HR-MDS-EB 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 investigate the clinical efficacy and safety of tirofiban and rt-PA alteplase in the treatment of minor stroke patients.Methods A total of 171 patients with minor stroke ad-mitted to Department of Neurology of Zhengzhou People's Hospital from January 2021 to January 2024 were prospectively and continuously enrolled,and were randomly divided into tirofiban group(84 cases)and rt-PA group(87 cases).The tirofiban group received intravenous infusion of tirofiban but not intravenous thrombolysis therapy.The rt-PA group was treated with intravenous thrombolysis.The 90-day mRS score was observed in all patients.NIHSS was used to score the improvement of neurological function.3-month all-cause death was observed during follow-up.Results The tirofiban group had a significant larger proportion of mRS score of 0-1 at 90 d(92.86％vs 82.76％,P＜0.05),lower NIHSS scores at 24 h and 7 d after treatment and smaller proportion of early neurological deterioration than the rt-PA group(P＜0.05).Larger proportion of mRS score of 0-2 in the 90 day and slightly smaller proportion of the score of 4-6 at 90 d were observed in the tirofiban group than the rt-PA group(P＞0.05).There were no statistical difference in the incidence of 3-month all-cause death between the two groups(P＞0.05).Conclusion Compared with rt-PA,tirofiban can significantly improve the clinical prognosis of pa-tients with minor stroke,reduce the risk of early neurological deterioration,and has higher safety.

Objective:To analyze the association between the triglyceride-glucose(TyG)index and the risk of nephrolithiasis across various demographic and clinical subgroups,aiming to enhance early di-agnosis and treatment of nephrolithiasis and promote personalized care in diverse populations.Methods:This cross-sectional study analyzed the medical records of 84 968 adults,stratified into three categories(low,middle,high)according to their TyG index scores.To evaluate the association between the TyG index and nephrolithiasis risk,multivariable Logistic regression models were employed,adjusting for po-tential confounders.Additionally,piecewise linear regression models were used to investigate the non-linear dynamics of the TyG index's relationship with nephrolithiasis risk.Subgroup analyses were per-formed to explore variations in the effects of the TyG index across different demographic and clinical populations.Results:Increasing TyG index was associated with a higher risk of nephrolithiasis,rising from 4.36％in the low group to 8.96％in the high group(P＜0.001).In adjusted models,males in the middle and high TyG index categories demonstrated significantly elevated risks of nephrolithiasis,with odds ratios of 1.18(95％CI:1.07-1.31,P=0.002)and 1.29(95％CI:1.15-1.45,P＜0.001),respectively.Conversely,in females,the association was not statistically significant post-adjustment(OR=0.98,95％CI:0.82-1.16,P=0.778).Among males,for each unit increment in the TyG index be-low the critical threshold of 8.98,there was a notable 40％escalation in the risk of developing nephroli-thiasis(OR=1.40,95％CI:1.24-1.58,P＜0.001).Surpassing this threshold,the TyG index no longer conferred a significant increase in risk(OR=0.91,95％CI:0.78-1.06,P=0.24).Subgroup analyses indicated that this association remained stable regardless of age,BMI,or hypertension status.Conclusion:The TyG index is positively associated with the risk of nephrolithiasis in males,demonstra-ting a nonlinear dose-response relationship that becomes especially pronounced at certain index levels.This biomarker could potentially serve as a valuable clinical tool for identifying males who are at a high risk of developing nephrolithiasis,thereby enabling targeted preventive strategies.Further research is urgently needed to explore the underlying mechanisms and to verify the applicability of these results across different populations.

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.