Aim To study the change of mutant pre- vention concentration (MPC) in carbapenem-resistant Acinetobacter baumannii (CRAB) treated with moxi- floxacin ( MFX ) and/ or cefoperazone/ sulbactam (CFS) in vitro, and provide a theoretical support for preventing the bacterial resistance. Methods To cal- culate the fractional inhibitory concentration (FIC) in- dex, the minimum inhibitory concentration (MIC) of 20 clinical isolates of CRAB treated with MFX and/ or CFS was determined by checkerboard microdilution as- say. In addition, to calculate the selection index (SI), the MPC of 20 clinical isolates of CRAB treated with MFX and/ or CFS was determined by agar plate di- lution assay. Results Our study showed that there was synergistic/ addictive action, rather than antago- nism action against clinical isolates of CRAB when treated with MFX + CFS. The SI of the 20 isolates trea- ted with MFX or CFS alone was 4 ~128 and 8 ~64 re- spectively, but reduced to 1 ~8 and 4 ~16 when trea- ted with MFX + CFS, which decreased by 2 ~16 and 2 ~4 times respectively compared with the single treat- ment. Conclusion These results suggest that the combination treatment of MFX + CFS against clinical isolates of CRAB might lower the MPC of the isolates treated with MFX/ CFS alone, narrow the mutant selec- tion window, and prevent the generation of drug - re- sistant mutants.

Objective To compare the predictive value of 5 scoring systems for hemorrhagic transformation risk after intravenous thrombolysis in patients with acute ischemic stroke (AIS) in different therapeutic windows.Methods A single-center and retrospective study was performed for 243 AIS patients who underwent intravenous thrombolysis using recombinant tissue plasminogen activator (rt-PA) in different therapeutic windows in our department during January 2014 and December 2016.Five scoring systems,including HAT model (hemorrhage after thrombolysis),MSS model (multicenter stoker survey),GRASPS model (glucose at presentation,race,age,sex,systolic blood pressure at presentation,severity of stroke at presentation),SEDAN model (baseline blood sugar,early infarct signs,hyperdense cerebral artery sign on admission CT,age,NIHSS on admission),and SITS model (safe implementation of thrombolysis in strokemonitoring study) were used to evaluate the risks for hemorrhagic transformation.The relationships between the 5 scoring systems and incidence rate of hemorrhagic transformation were analyzed among the patients in different therapeutic windows.The predictive values of the 5 scoring systems were compared using the areas (AUC) under the receiver operating characteristic (ROC) curve.Results When the AIS patients were treated by intravenous thrombolysis within 3 h,the AUC of GRASPS and HAT models were 0.698 and 0.619,respectively,higher than those of the other 3 systems.When the therapeutic window was between 3 to 4.5 h,HAT model and SEDAN model had highest AUC (0.719,0.744) than the other 3 systems (P ＜0.05).When the windows were ＞4.5 ～6 h,the HAT model had the highest AUC (0.676).Conclusion The 5 scoring systems show better predictive value for hemorrhagic transformation after intravenous thrombolysis.For the therapeutic window within 4.5 h,HAT model presents best predictive value than the other 4 scoring systems.

Objective To investigate the imaging changeof cerebral ischemireperfusion injury (CIRI) afteinterventional therapy in acute middle cerebral artery occlusion .Method32 patientwith acute middle cerebral artery occlusion in ouhospital from January 2013 to Novembe2014 were selected .16 casewere performed the recanalization therapy aftearterial thrombolysiand/omechanical thrombectomy(recanalization group) and 16 casewere notreated by thrombolytitherapy (non-recanalization group) .The differenceof brain imaging changes(onse,on 3 ,7 d afteonset) were analyzed and compared between the two group. ResultThe proportion of lateral ventricle compression degree and the shifdegree of brain midline on 3 d afteonsein the reca-nalization group were greatethan those in the non-recanalization group ,the differencebetween the two groupwere statistically significant[0 .50 ± 0 .11 v.0 .58 ± 0 .10 ,0 .57(0 .18 ,0 .83)cm v.0 .22(0 ,0 .57)cm ,P＜0 .05] ,while which on 7 d of onsein the recanalization group were lesthan those in the non-recanalization group[0 .80 ± 0 .11 v.0 .55 ± 0 .12 ,0(0 ,0 .13) v.0 .46(0 , 0 .88)cm ,P＜0 .055] .Conclusion Although the interventional therapy ian importanmeasure foearly treatmenof ischemistroke ,buiaggravatethe early brain edem,therefore CIRI induced by the interventional therapy should be paid more attention to.

Objective:To comprehensively evaluate the input and output efficiency of scientific research in hospital by bootstrap data envelopment analysis, to provide useful information for optimization of scientific performance appraisal and hospital discipline development strategy.Methods:37 disciplines were included as decision making unit, input variables include research expenditure and number of research personnel, and output variables include number of science and technology awards, research projects, patent transfer, paper, composition, and academic influence. The bootstrap-DEA method was used to evaluate the efficiency of all DMUs.Results:The main of overall efficiency and pure technical efficiency in basic DEA model are 0.858 and 0.909, but are 0.804 and 0.853 in Bootstrap DEA model, the differences between two models have statistically significant ( P<0.001). There are 11 DMUs with an overall efficiency in 0.9~1.0, 14 DMUs in 0.8~0.9, 7 DMUs in 0.6~0.8, 5 DMUs lower than 0.6. There are 3 DMUs are increasing return to scale, 16 DMUs are constant return to scale, 18 DMUs are decreasing return to scale. No statistically significance was observed between different types of DMUs( P>0.05). There are 4 DMUs reveal input slacks in number of research staffs and 10 DMUs reveal output slacks. Conclusions:The results of Bootstrap-DEA are more accurate than the basic methods for the evaluation of the input-output efficiency of hospital scientific research, so that it is worth popularizing and applying. According to the evaluation results, the hospital management department and disciplines could optimize their discipline development strategies and put forward targeted improvement measurements.