Objective To analyze the clinically isolated bacterial distribution and drug resistance characteristics in the our hospital during 2016 to provide the pathogenic drug resistance monitoring data for rational bacterial drugs use in clinic.Methods The clinicaly submitted samples were performed the pathogenic bacterial isolation according to the routine method.The isolated pathogens were identified by the Vitek2-Compact system and the drug susceptibility test was performed by adopting the MIC and KB methods.The results were statistically analyzed by adopting the WHONET5.6 software.Results A total of 2 214 non-repeat strains of bacteria were isolated in 2016,including 1 614 strains of Gram-negative bacilli,accounting for 72.9％,600 strains of Gram-positive bacteria,accounting for 27.1 ％.The top five isolated bacteria were Klebsiella pneumoniae,Escherichia coli,Pseudomonas aeruginosa,Acinetobacter baumannii and Staphylococcus aureus.The detection rates of ESBLs producing Escherichia coli and Klebsiella pneumoniae were 51.8％ and 27.6％ respectively.The detection rates of methicillin-resistant Staphylococcus aureus (MRSA) was 26.5％.No vancomycin or linezolid resistant staphylococcal strains were found.Conclusion The main isolated pathogens in our hospital are dominated by Gram-negative bacteria.Hospital should strengthen reasonable and standardized use of antibacterial drugs to reduce the generation of drug resistant bacterial strains.

Background::While type 2 diabetes mellitus (T2DM) is considered a putative causal risk factor for coronary artery disease (CAD), the intrinsic link underlying T2DM and CAD is not fully understood. We aimed to highlight the importance of integrated care targeting both diseases by investigating the phenotypic and genetic relationships between T2DM and CAD.Methods::We evaluated phenotypic associations using data from the United Kingdom Biobank ( N = 472,050). We investigated genetic relationships by leveraging genomic data conducted in European ancestry for T2DM, with and without adjustment for body mass index (BMI) (T2DM: Ncase/ Ncontrol = 74,124/824,006; T2DM adjusted for BMI [T2DM adjBMI]: Ncase/ Ncontrol = 50,409/523,897) and for CAD ( Ncase/ Ncontrol = 181,522/984,168). We performed additional analyses using genomic data conducted in multiancestry individuals for T2DM ( Ncase/ Ncontrol = 180,834/1,159,055). Results::Observational analysis suggested a bidirectional relationship between T2DM and CAD (T2DM→CAD: hazard ratio [HR] = 2.12, 95% confidence interval [CI]: 2.01–2.24; CAD→T2DM: HR = 1.72, 95% CI: 1.63–1.81). A positive overall genetic correlation between T2DM and CAD was observed ( rg = 0.39, P = 1.43 × 10 -75), which was largely independent of BMI (T2DM adjBMI–CAD: rg = 0.31, P = 1.20 × 10 –36). This was corroborated by six local signals, among which 9p21.3 showed the strongest genetic correlation. Cross-trait meta-analysis replicated 101 previously reported loci and discovered six novel pleiotropic loci. Mendelian randomization analysis supported a bidirectional causal relationship (T2DM→CAD: odds ratio [OR] = 1.13, 95% CI: 1.11-1.16; CAD→T2DM: OR = 1.12, 95% CI: 1.07-1.18), which was confirmed in multiancestry individuals (T2DM→CAD: OR = 1.13, 95% CI: 1.10-1.16; CAD→T2DM: OR = 1.08, 95% CI: 1.04-1.13). This bidirectional relationship was significantly mediated by systolic blood pressure and intake of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, with mediation proportions of 54.1% (95% CI: 24.9-83.4%) and 90.4% (95% CI: 29.3-151.5%), respectively. Conclusion::Our observational and genetic analyses demonstrated an intrinsic bidirectional relationship between T2DM and CAD and clarified the biological mechanisms underlying this relationship.

Objective: The aim of the present study was to investigate the survival rate and its prognostic factors for patients with biliary tract cancer, and then a prognostic risk prediction model was constructed to predict the survival probability of patients. Methods: A total of 14 005 patients with biliary tract cancer (including gallbladder cancer, extrahepatic bile duct cancer, and ampulla of Vater cancer), who were diagnosed between 2010 and 2015 in the US National Cancer Institute Surveillance, Epidemiology, and End Results Program (SEER) were included in the development cohort. The prognostic risk factors of biliary tract cancer were investigated using multivariate Cox regression models. The predictive nomograms were then constructed to predict the overall survival probability of 1, 3, and 5 years, and the predictive discrimination and calibration ability of the nomograms were further evaluated. Meanwhile, 11 953 patients who were diagnosed during 2004 to 2009 from SEER Program were then selected to validate the external predictive accuracy of the prediction models. Results: The 1, 3 and 5-year cumulative survival rates of patients with biliary tract cancer were 41.9%, 20.4% and 15.3%, respectively, in the development cohort. Age greater than 50 years, African Americans and Native Americans and Alaska Natives, higher T, N and M stage and poor histological differentiation grade were risk factors for death, while married status, Asia-Pacific Islanders, insured status and surgery on primary site were protective factors. Gender was not significantly associated with the overall survival. The C statistic of the prediction model was 0.73 (95%CI: 0.72-0.74), and the calibration curve showed that the interaction curves of predictive and actual survival rates of 1, 3 and 5 years were close to the 45 degree diagonal. Results in the validation cohort were similar with those in the construction cohort, with a C statistic of 0.70 (95%CI: 0.69-0.72), indicating high external applicability of the prediction model. Findings from gallbladder cancer, extrahepatic bile duct cancer, and ampulla of Vater cancer are in consistent with the overall biliary tract cancer. Conclusions The survival rate of patients with biliary tract cancer is relatively poor, and the survival prediction model based on prognostic factors has high prediction accuracy. In the future, this prognostic prediction model could be applied to clinical practice to guide individualized treatment for patients with biliary tract cancer.