ObjectiveTo compare and analyze the differences of bacterial resistance of 3 different strains of bacteria isolated from Mohnarin cerebrospinal fluid (CSF), blood and bile of literature published in China, to provide the basis for clinical rational drug use. MethodsWe searched databases including CNKI and WanFang Data for reports about bacterial resistance from Mohnarin CSF, blood and bile from 2006 to 2014. Two reviewers independently screened literature, extracted data, and analyzed the differences of bacterial resistance from CSF, blood and bile by SPSS 19.0 software. ResultsComposition ratio of the gram positive bacteria from CSF specimens was much higher than those of the blood and the bile (χ2=383.118, P<0.001). The separation of E. coli, K. pneumoniae, E. cloacae, P. aeruginosa, A. baumannii, E. faecium and E. faecalis from CSF exhibited multi-drug resistance, and their resistance rates to commonly used antimicrobial agents were significantly higher than those from blood and bile (P<0.001), especially the A. baumannii, K. pneumoniae, E. cloacae and E. faeciu, and their overall resistance rates to commonly used antimicrobial drugs were 68.1%, 60.5%, 59.8%, and 59.4%, respectively. The top three antibiotics with higher resistance rate were piperacillin, sulfamethoxazole/trimethoprim and cefotaxime in A. baumannii, piperacillin, ceftriaxone and cefotaxime in K. pneumoniae, cefoxitin, Ampicillin/sulbactam and cefuroxime in E. cloacae, penicillin G, ampicillin and erythromycin in E. faecium. The resistant rates of quinolone in E. coli, E. cloacae, A. baumannii and E. faecium from CSF specimens were high, but low in K. pneumoniae, P. aeruginosa and E. faecalis. ConclusionThere are differences for drug resistance of the bacteria from different specimens from Mohnarin, the bacteria from CSF specimens exhibits multi-drug resistance, the resistances are significantly higher than those from blood and bile.
Non-invasive brain stimulation is a technology that uses magnetic field or electric field to act on the brain to adjust the activity of cerebral cortex neurons. It mainly includes transcranial magnetic stimulation and transcranial direct current stimulation. The principle is to accelerate the induction of neuroplasticity by changing the excitability of the cerebral cortex. The characteristics are noninvasive, safe and that the patient can tolerate it. This article mainly introduces the theoretical foundation and mechanisms of non-invasive brain stimulation, and its application and safety in stroke complications, neuropathic pain and epilepsy, and discusses the commonly used treatment regimens of non-invasive brain stimulation in different neurological diseases, in order to provide possible treatment reference for the rehabilitation of neurological diseases.
Objective To explore the effect of low-load resistance training on physical fitness in aged adults. Methods Select the aged adults who will go to the outpatient Department of Rehabilitation Medicine of Peking Union Medical College Hospital between June 1, 2020 and May 31, 2021. The aged adults were randomly divided into three groups by using the method of random number table: medium intensity aerobic training group (aerobic training group), standard-load resistance training group (standard-load group) and low-load resistance training group (low-load group). The basic information, exercise endurance (peak power, peak oxygen uptake), exercise cardiopulmonary function [peak heart rate, predicted peak heart rate, peak minute ventilation (VE), ventilatory equivalent for carbon dioxide at anaerobic threshold (EqCO2 during AT)], muscle strength, and muscle oxygen related indexes were collected blindly before the first exercise and after 12 weeks of training, respectively. To compare the differences of the indexes before and after training. Results A total of 90 patients were enrolled, 30 in each group. There was no significant difference in age, sex, height, weight and body mass index among the three groups (P>0.05). There was no significant difference in cardiopulmonary endurance, cardiopulmonary function, muscle strength, muscle oxygen related indexes between the groups before and after training (P>0.05). Except for the indexes related to cardiac function (peak heart rate, predicted peak heart rate) and resting muscle oxygen level (P>0.05), other indexes related to pulmonary function, cardiopulmonary endurance, muscle strength, and time of muscle oxygen falling to the valley in the three groups were statistically significant compared with those before training (P<0.05). Except for peak power, peak oxygen uptake and time of muscle oxygen falling to the valley (P>0.05), the difference of muscle strength before and after training in the three groups was statistically significant (P<0.05), including grip strength, chest push, sitting rowing, leg extension, hip abduction, body bending and horizontal push and push, and the low-load group was better than the aerobic training group (P<0.05), but the improvement of body bending and horizontal push and push in the standard-load group was better than the low-load group (P<0.05). Conclusions Low-load resistance training, standard-load resistance training and aerobic training have almost the same effect on improving the physical fitness of the elderly. Low-load resistance training is superior to medium intensity aerobic training in improving muscle strength, which is an effective method to improve the physical fitness of the aged adults.
Meta-analysis of survival data is becoming more and more popular. The data could be extracted from the original literature, such as hazard ratio (HR) and its 95% confidence interval, the difference of actual frequency and theoretical frequency (O - E) and its standard deviation. The data can be used to calculate the combined HR using Review Manager (RevMan), Stata and R softwares. RevMan software is easy to learn, but there are some limitations. Stata and R software are powerful and flexible, and they are able to draw a variety of graphics, however, they need to be programmed to achieve.
ObjectiveA simulation study was used to generate the multivariate normal distribution data with a residual effect based on series of N-of-1 trials. The statistical performance of paired t-test, mixed effect model and Bayesian mixed effect model were compared.MethodsThree-cycles N-of-1 trials were set, and the participants were randomly assigned to 2 different treatments in each cycle. The simulation study included the following procedures: producing six-dimensional normal distribution data, randomly allocating intervention methods and patients, adding residual effects, constructing and evaluating 3 models, and setting the parameters. The sample sizes were set as 3, 5, 8 and 10, and the correlation coefficients among different times were set as 0.0, 0.5 and 0.8. Different proportions of residual effects for the 2 groups were set. Type I error, power, mean error (ME), and mean square error (MSE) were used to compare the 3 models.ResultsWhen there was no residual effect in the 2 groups, type I errors of 3 models were approximately 0.05, and their MEs were approximately 0. Paired t-test had the highest power and the lowest MSE. When the residual effect existed in the 2 groups, the type I error of paired t-test increased, and its estimated value deviated from the true value (ME≠0). Type I errors of the mixed effect model and Bayesian mixed-effect model were approximately 0.05, and they had the same power. The estimated values of the two models were close to the true value (ME was approximately 0).ConclusionsWhen there is no residual effect (0% vs. 0%), paired t-test is suitable for data analysis of N-of-1 trials. When there is a residual effect, the mixed effect model and Bayesian mixed-effect model are suitable for data analysis of N-of-1 trials.