Objective To investigate the effects of playing virtual reality games on the recovery of hemiplegic upper extremities after stroke.Methods Thirty stroke patients with hemiplegic upper extremities were randomly assigned to a treatment group (n=15) or a control group (n=15).Both groups received routine medication and conventional physical therapy,while the treatment group was additionally given (Nintendo) gaming-based virtual reality therapy.Before and after 2 weeks of treatment,the patients in both groups were evaluated using the Fugl-Meyer Assessment for the Upper Extremities (FMA-UE),Brunnstrom staging and co-contraction ratios (CRs).Surface electromyogram signals from the biceps brachii and triceps brachii were also recorded during maximum isometric voluntary flexion and extension of the affected elbow.Results No significant differences in any of the measurements were observed between the 2 groups before or after the intervention.Both groups demonstrated significant increases in their average FMA-UE score,Brunnstrom staging and CRs.Conclusions Virtual reality gaming using a Wii controller is as effective as conventional therapy in enhancing upper extremity motor function and elbow flexion and extension after stroke.

Objective To introduce the construction and application of clinical microbiology laboratory data management expert system.Methods Firstly, the process management was introduced to clinical microbiology laboratory. Then the characteristics of data on each node of work process were analyzed, and SQL Server data table was created as the knowledge base of the expert system.Finally, VB6.0 was used to compile the knowledge acquisition module, reasoning desktop module and input/output interface procedures to finally construct the expert system.Rates of defect report, errors in bacterial identification and drug sensitivity test, delay in culture results reporting and average delayed days were compared before and after the application of the expert system.Results The expert system could be used for data management in process nodes like sample reception, information collection and input, bacteria culture medium selection, bacterial identification and drug sensitive test, interpretation of drug sensitivity results, comprehensive evaluation in bacterial identification and drug sensitivity results, report of negative result, report of positive result, blood culture, Mycoplasma culture, time limit of detection, and nosocomial infection indicators.No defect report was found after the application of expert system; rate of errors in selection of drug sensitivity test medium was reduced from 0.81% ( 31/3 836 ) in 2012 to 0.02%(1/5 433) in 2014;rate of delay in culture results reporting was reduced from 1.78% (320/17 983) to 1.18%(232/19 692), and the average delayed days was also reduced (3.8 d vs.3.2 d).Conclusion Clinical microbiology laboratory data management expert system can improve work efficiency and reduce errors, which can enhance the overall management of laboratory and the quality of clinical service.

Objective To investigate the clinical isolation situation and drug resistance features of enterococcal bacteria in order to provide reference for the clinical rational use of antibacterial agents and infection control. Methods A total of 1220 strains of enterococcal bacteria that induced hospital infection were analyzed retrospectively. Walk Away 96 automated microbial analyzer was used for strain identification and drug sensitive test. MIC was used for screening high-level aminoglycoside resistant strains. WHONET 5.6 was used for data analysis. Results A total of 1220 strains of enterococci were detected, including 675 strains of enterococci faecalis, accounting for 55.3%, and 445 strains of ente-rococci faecium, accounting for 36.5%. Enterococcal bacteria mainly distributed in clinical urine specimens, accounting for 57.5%. The total drug resistance rate of enterococci faecalis was high and the drug resistance rates to penicillin, ampicillin, ciprofloxacin and levofloxacin were all higher than 90%, which were significantly higher than those of the enterococci faecium (<17%). The drug resistance rate of enterococci faecalis to quinupristin/dalfopristin was 100.0%and that of enterococci faecium was 12.6%. For both types of bacteria, no strain resistant to vancomycin was found, but 3 strains of enterococci faecalis were resistant to linezolid. The screening rates of enterococci faecalis for high-level gentamicin drug resistant strains and high-level streptomycin resistant strains were 54.1% and 27.3% respectively while those of enterococci faecium were 58.2% and 56.9% respectively. Conclusion The drug resistance situation of enterococcal bacteria to common antibacterial drugs is not optimistic, and the monitoring of clinical distribution and drug resistance situation of enterococcal bacteria is of important guiding significance to the clinical treatment of entero-coccal bacterial infection.

Objective To analyze the possible risk factors of IVIG non-response of Kawasaki disease (KD),Shanghai Children's Hospital and Shanghai Junze Software develop an research platform,which is based on E-Science model.Through the mathematical model by integrating the risk factors to explore the method of effective prediction for IVIG non-response,and to provide the clinical basis for timely and effective treatment and prognosis of the disease.Methods The data of KD children who were hospitalized in Shanghai Children's Hospital from January 2013 to November 2016 were included.The indexes included gender,age,time of IVIG treatment,and laboratory examinations.The multivariate logistic regression was used to analyze the influencing factors of IVIG non-response.The indexes in the model were deduced according to the independent variables of the logistic regression equation.The ROC curve and the area under the curve were calculated for the new prediction model.The sensitivity and specificity of the new prediction model were calculated according to the cutoff value.Finally,the new model was compared with the Kobayashi and Egami scoring model.Results The levels of CRP,NLR,LDH,ALB and FDP in children with KD were influencing factors for IVIG nonresponse (P ＜ 0.05).According to the logistic regression equation,the sensitivity and specificity of the model used to predict IVIG non-response were 69.7％ and 80.4％,respectively,and the area under the ROC curve was 0.825 (95％ CI:0.769-0.882).Kobayashi and Egami scoring models were tested,the sensitivity and specificity of the new scoring system were better than previous ones.Condusion The scoring model established in this study has a good effect in predicting IVIG non-response in KD patients and could be used in clinical practice,and it is worthy to be validated and adjusted by large-scale data.