Objective To analyze the risk factors of multi-drug resistant bacterial infection of urinary system in type 2 diabetes mellitus(T2DM)patients and establish a decision tree model.Methods A total of 371 patients with T2DM were selected and divided into infected(IF,n=47)group and non-infected(NIF,n=324)group.The influencing factors of multi-drug resistant urinary system infection were analyzed in patients with T2DM.IBM SPSS Modeler was used to establish a decision tree model for predicting multi-drug resistant urinary system infection.Results The infection rate of multidrug resistant bacteria in urinary system was 12.67%.Age,female proportion,DM duration,use of antibiotics before admission,length of hospital stay,and hemoglobin A1c(HbA1c)in IF group were higher than that in NIF group(P<0.05).Logistic regression analysis showed that age≥60 years old(OR 2.629,95%CI 1.349～5.125,P=0.005),woman(OR 3.044,95%CI 1.506～6.151,P=0.002),DM duration≥10 years(OR 3.320,95%CI 1.671～6.593,P=0.001),length of hospital stay>15 days(OR 2.406,95%CI 1.237～4.682,P=0.010)and HbA1c>8.02%(OR 2.363,95%CI 1.210～4.617,P=0.012)were influencing factors for multidrug-resistant bacteria infection of urinary system.Decision tree model showed that sex was the most important factor in urinary multidrug-resistant infections.Receiver operating characteristic curve results showed that the area under the curve value of Logistic regression model was slightly higher than that of decision tree(Z=1.465,P=0.143).Conclusions Age,sex,DM duration,length of hospital stay,and HbA1c are the influencing factors of T2DM patients complicated with urinary multi-drug resistant bacteria infection,the decision tree model established in this study has good risk prediction efficiency,which is helpful for early clinical identification of high-risk patients with multi-drug resistant bacteria infection of urinary system.

Objective To analyze the risk factors of multi-drug resistant bacterial infection of urinary system in type 2 diabetes mellitus(T2DM)patients and establish a decision tree model.Methods A total of 371 patients with T2DM were selected and divided into infected(IF,n=47)group and non-infected(NIF,n=324)group.The influencing factors of multi-drug resistant urinary system infection were analyzed in patients with T2DM.IBM SPSS Modeler was used to establish a decision tree model for predicting multi-drug resistant urinary system infection.Results The infection rate of multidrug resistant bacteria in urinary system was 12.67%.Age,female proportion,DM duration,use of antibiotics before admission,length of hospital stay,and hemoglobin A1c(HbA1c)in IF group were higher than that in NIF group(P<0.05).Logistic regression analysis showed that age≥60 years old(OR 2.629,95%CI 1.349～5.125,P=0.005),woman(OR 3.044,95%CI 1.506～6.151,P=0.002),DM duration≥10 years(OR 3.320,95%CI 1.671～6.593,P=0.001),length of hospital stay>15 days(OR 2.406,95%CI 1.237～4.682,P=0.010)and HbA1c>8.02%(OR 2.363,95%CI 1.210～4.617,P=0.012)were influencing factors for multidrug-resistant bacteria infection of urinary system.Decision tree model showed that sex was the most important factor in urinary multidrug-resistant infections.Receiver operating characteristic curve results showed that the area under the curve value of Logistic regression model was slightly higher than that of decision tree(Z=1.465,P=0.143).Conclusions Age,sex,DM duration,length of hospital stay,and HbA1c are the influencing factors of T2DM patients complicated with urinary multi-drug resistant bacteria infection,the decision tree model established in this study has good risk prediction efficiency,which is helpful for early clinical identification of high-risk patients with multi-drug resistant bacteria infection of urinary system.

Objective To explore the difference of gene expression profiling between normal basilar arteries and basilar arteries of cerebral vasospasm (CVS) after subarachnoid hemorrhage (SAH) in rabbits. Methods cDNA chip of normal basilar arteries and basilar arteries of CVS after SAH in rabbits were downloaded from GEO database. The chip was analyzed and screened by Bioconductor software, and function enrichment and pathway analysis of the differentially expressed genes were analyzed by Cytoscape software. Then 6 adult male Japanese rabbits were used, and randomly divided into normal control group (n=3) and SAH model group (n=3). Rabbit SAH models were established by cisterna secondary-blood-injection method. RNA data of normal basilar artery specimens on the 0 day and basilar artery specimens after SAH on the 5-day were used to validate the parts of differentially expressed genes by qRT-PCR. Results A total of 4356 differentially expressed genes were found in normal basilar arteries and basilar arteries of CVS after SAH in rabbits. Among them, 920 genes were considered to be significant with P-value<0.05, such as GRIK1, MYH13, ZNF45, SAA3, RLN1, MSR1 and others. Function enrichment analysis indicated that the differentially expressed genes were involved in regulation of Ca2+transmembrane transporter activity, negative regulation of ion transmembrane transport, regulation of potassium ion transport, positive regulation of JAK-STAT signaling cascades and other biological processes. Pathway analysis showed that calcium signaling pathway, cGMP-PKG signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway and other signaling pathways maybe related with the differentially expressed genes. qRT-PCR verification showed that the expression of MSR1 in SAH model group was consistent with that of the chip result. Conclusion The gene expressions of basilar arteries of CVS after SAH in rabbits are significantly different, and MSR1 gene can be used as a potential target for studying the pathological mechanism of CVS.