Objective To explore the risk factors for urinary tract infections in type 2 diabetes mellitus(T2DM )by analyzing the pathogens distribution characteristics and drug sensitivity. Methods A total of 3 ,065 T2DM patients were retrospectively analyzed for the clinical and etiological characteristics of urinary tract infections in T2DM. All patients enrolled in the study were divided into two groups :T2DM with urinary tract infection group (n= 210) and simple T2DM group (n= 132). Data harvested were analyzed by SPSS 19.0. Results Of 3 ,065 patients with T2DM the incidence of urinary tract infection was 6.85%. Compared with T2DM group ,patients in T2DM+ urinary tract infection group were older , with longer duration of diabetes ,higher levels of FPG ,urea nitrogen ,creatinine and 24 hUAlb ,and lower levels of serum albumin ( P< 0.05 ). Logistic regression analysis showed that age ,gender ,duration of diabetes and 24 hUAlb were risk factors and albumin was protective factor for urinary tract infection in T2DM. 84.2% was gram-negative bacteria among the 210 isolated strains of pathogenic bacteria. The top three pathogens were Escherichia coli (65.2% ) ,Enterococcus faecalis (6.2% ) and Klebsiella pneumoniae (5.2% ). Gram-negative bacteria had high sensitivity to Imipenem (100% ) ,Cefoperazone sulbactam (99.2% ) and Amikacin (99.2% ) but low sensitivity to cephalothin (38.1% ) ,piperacillin (35.7% ) and amoxicillin (28.2% ). Conclusion T2DM patients who have the factors including female ,advanced age , long course of diabetes and renal insufficiency are prone to suffer from urinary tract infection. Gram-negative bacteria are the most common pathogens in T 2DM patients with urinary tract infection but bacteria spectrum has changed with percentage of multiple resistant bacteria rising dramatically.

Based on HRP (Hospital Resource Planning) system's device management module, A new online information management system is proposed and realized to meet the new challenge of medical devices' repairing and maintenance. the traditional telephone report or online report can all be deal. the repair progress can be visualized in real time PM planning and it's early warning are added.
Equipment and Supplies, Hospital ; Maintenance and Engineering, Hospital

OBJECTIVETo evaluate the therapeutic effects of transcatheter arterial chemoembolization (TACE) with or without radiofrequency ablation (RFA) in hepatocellular carcinoma (HCC) patients based on propensity score matching.

METHODSA logistic regression model was established with the treatment assignment as the dependent variable and the covariates as the independent variables. For each HCC patient, the propensity score was calculated from the model for caliper matching, and a survival analysis of the matched data were carried out.

RESULTSThe covariates between the groups were balanced after caliper matching based on the propensity scores. Before matching, the one-, two-, and three-year survival rates of TACE and TACE+RFA were 52.07% and 59.08%, 32.24% and 36.43%, and 316.54% and 19.39%, with the median survival time of 1.20 and 1.40 years, respectively, showing no significant differences in the overall survival rate between the two groups. After matching, the 1-year, 2-year, and 3-year survival rates of TACE and TACE+RFA groups were 54.39% and 62.28%, 23.15% and 40.08%, and 10.20% and 18.52%, with the median survival time of 1.10 years and 1.50 years, respectively, showing significant differences in the overall survival between the two groups. The survival rate in TACE+RFA group was higher than that of TACE only group.

CONCLUSIONPropensity score matching can effectively reduce the confounding bias of non-randomized clinical observational data for a more accurate evaluation of the therapeutic effect in HCC patients.

Adult ; Aged ; Carcinoma, Hepatocellular ; epidemiology ; mortality ; therapy ; Catheter Ablation ; Chemoembolization, Therapeutic ; Female ; Humans ; Liver Neoplasms ; epidemiology ; mortality ; therapy ; Logistic Models ; Male ; Middle Aged ; Propensity Score ; Survival Analysis ; Survival Rate

OBJECTIVETo evaluate the predictive power of risk model by combining traditional epidemiological factors and genetic factors.

METHODSOur previous GWAS data of lung cancer in Chinese were used in training set (Nanjing and Shanghai: 1473 cases vs. 1962 control) and testing set (Beijing and Wuhan: 858 cases vs. 1 115 control). All the single nucleotide polymorphisms (SNPs) associated with lung cancer risk were systematically selected and stepwise logistic regression analysis was used to select independent factors in the training set. The wGRS (weighted genetic score) was further used to calculate genetic risk score. To evaluate the contribution of the genetic factors, 3 risk models were established by using the training set, i.e. smoking model (based on smoking status) , genetic risk model (based on genetic risk score) and combined model (based on smoke and genetic risk score). The predictability of the models were evaluated by the areas under the receiver operating characteristic (ROC) curves, area under curve (AUC), net reclassification improvement (NRI) and integrated discrimination index (IDI). Besides, the results were further verified in the testing set.

RESULTSIn the training set, it was found that the AUC of the smoking, genetic risk and combined models were 0.65 (0.63-0.66), 0.60 (0.59-0.62) and 0.69 (0.67-0.71), respectively. Compared with combined model, the predictive power of other two models significantly declined, the difference was statistically significant (P<0.001). Furthermore, compared with the smoking model, the NRI of the combined model increased by 4.57% (2.23%-6.91%) and IDI increased by 3.11% (2.52%-3.69%) in the training set, the difference was statistically significant (P<0.001). Similarly, in the testing set NRI increased by 2.77%, the difference was not statistically significant (P=0.069) , and IDI increased by 3.16%, the difference was statistically significant (P<0.001).

CONCLUSIONThis study showed that combining 14 genetic variants with traditional epidemiological factors could improve the predictive power of risk model for lung cancer. The model could be used in the screening of high-risk population of lung cancer in Chinese and provide evidence for the early diagnosis and treatment of lung cancer.

Area Under Curve ; Asian Continental Ancestry Group ; Beijing ; Case-Control Studies ; China ; Genetic Predisposition to Disease ; Genetic Variation ; Genome-Wide Association Study ; Humans ; Lung Neoplasms ; epidemiology ; genetics ; Polymorphism, Single Nucleotide ; ROC Curve ; Risk Factors