Objective To investigate the bacteriological features and the epidemiology of methicillin－ resistant Staphylococcus aureus (MRSA) in patients with impetigo, and provide evidence for selection of appropriate agents for treating impetigo and its complications. Methods Two hundred and forty－ six out- patients with impetigo were surveyed. Cultures were obtained from impetiginous lesions. Antibiotic susceptibility tests on 212 S.aureus isolates were performed by standard agar dilution testing (MIC). Results In a total of 246 isolates, 87.0％ yielded S.aureus alone, 2.0％ S.aureus and Group A beta－ hemolytic streptococcus (GABHS), 1.6％ GABHS alone, 4.1％ Staphylococcus epidermidis. Fifteen antimicrobial agents were used in the susceptibility tests. One hundred percent of S. aureus isolates were resistant to penicillin, 87.7％ were resistant to erythromycin, followed by clindamycin (75.5％ ), tetracycline (63.2％ ), chloramphenicol (54.2％ ), ampicillin－ sulbactam (44.8％ ), oxacillin (30.2％ ), ciprofloxacin (12.8％ ), cefotaxime (0.9％ ), fusidic acid (0.9％ ) and gentamicin (0.9％ ). None of the S.aureus isolates was resistant to vancomycin, cefazolin, cefuroxime and mupirocin. Sixty- four MRSA strains were isolated. No vancomycin resistant S.aureus strain was found. Conclusions Penicillin, erythromycin, clindamycin, tetracycline, chloramphenicol and ampicillin－ sulbactam prove to be no longer the appropriate therapeutic agents. Cephalosporin, mupirocin and fusidic acid might be the good choice for treatment of impetigo in our region.

Objective:To investigate the value of pulmonary perfusion defect index (PPDI), pulmonary artery obstruction index (PAOI) and right heart function parameters in the evaluation of severity of pulmonary embolism.Methods:The clinical data of 108 patients with pulmonary embolism who received treatment in The Second Hospital of Jiaxing from January 2019 to June 2021 were retrospectively analyzed. These patients were divided into high-risk ( n = 25), moderate-risk ( n = 32), and low-risk ( n = 51) groups according to the severity of pulmonary embolism. PAOI, PPDI, right ventricular short axis maximum diameter (RV), left ventricular short axis maximum diameter (LV), ratio of right/left right ventricular short axis maximum diameter (RV/LV) were determined in each group. PPDI, PAOI and right ventricular function parameters were correlated with the severity of pulmonary embolism. The area under the receiver operating characteristic curve, specificity and severity of PPDI, PAOI, RV, LV, RV/LV used alone and in combination to predict the severity of pulmonary embolism were analyzed. Results:PPDI, PAOI, RV, and RV/LV in the high-risk group were (32.52 ± 10.85)%, (45.01 ± 15.02)%, (50.32 ± 12.32) mm, (1.42 ± 0.45), respectively, which were significantly lower than (5.32 ± 1.85)%, (12.52 ± 3.25)%, (37.25 ± 8.52) mm, (0.96 ± 0.21) in the low-risk group, and LV was significantly lower in the high-risk group than that in the low-risk group [(35.14 ± 10.52) mm vs. (44.02 ± 15.21) mm, t = 13.95, 11.91, 2.62, 6.09, 5.44, all P < 0.05]. PPDI, PAOI, RV, and RV/LV in the moderate-risk group were (18.62 ± 6.02)%, (28.65 ± 8.65)%, (45.85 ± 10.02) mm, and (1.20 ± 0.32), respectively, which were significantly higher than those in the low-risk group ( t = 14.75, 12.06, 4.18, 4.13, all P < 0.05). There was no significant difference in LV between moderate-risk and low-risk groups ( t = 1.51, P > 0.05). Spearman correlation analysis showed that PPDI, PAOI, RV, RV/LV were positively correlated with the severity of pulmonary embolism ( r = 0.87, 0.84, 0.45, 0.41, all P < 0.001). LV was negatively correlated with the severity of pulmonary embolism ( r = -0.27, P < 0.001). The receiver operating characteristic curve (ROC curve) showed that the areas under the receiver operating characteristic curve of PPDI, PAOI, RV, LV, RV/LV used alone or in combination to predict the severity of pulmonary embolism were 0.941, 0.911, 0.721, 0.693, 0.726, and 0.951, respectively (all P < 0.001). Conclusion:PPDI, PAOI and right heart function parameters can be used as effective indexes to dynamically monitor the severity of pulmonary embolism.

Objective To investigate the relationship between polymorphism of resistin(RETN)gene and metabolic associated fatty liver disease(MAFLD)in type 2 diabetes mellitus(T2DM)patients in middle and high altitude areas.Methods A total of 400 patients with T2DM in Qinghai area were recruited and divided into simple T2DM group(T2DM,n=200)and T2DM combined with MAFLD group(T2DM+ MAFLD,n=200)according to liver ultrasonography.Healthy individuals confirmed by physical examination were selected as the normal control group(NC,n=180).Plasma resistin levels were measured by ELISA.The polymorphism of RETN-420C/G and +299G/A genes were detected by PCR sequencing.Results By comparing the polymorphism of RETN-420C/G gene in each group,it was found that the frequencies of G/G genotype and G allele frequency in T2DM+MAFLD group were higher than those in NC group and T2DM group(P<0.05),while the frequencies of C/C genotype and C allele frequency were lower than those in NC group and T2DM group(P<0.05).The risk of MAFLD increased by 1.571,2.126 and 1.537 times respectively in T2DM patients with C/G,G/G genotype and G allele.Logistic regression analysis showed that G/G genotype was a risk factor for MAFLD in T2DM patients.By comparing the polymorphism of RETN+299G/A gene in each group,it was found that A allele frequency in T2DM+MAFLD group was higher than that in NC group and T2DM group,while G allele frequency was lower than that in NC group and T2DM group(P<0.05).The allele A increased the risk of MAFLD in T2DM patients by 1.432 times compared to allele G.Conclusion RETN gene-420C/G locus G/G genotype increases the risk of T2DM combined with MAFLD in middle and high altitudeareas.

In this paper, we propose a multi-scale mel domain feature map extraction algorithm to solve the problem that the speech recognition rate of dysarthria is difficult to improve. We used the empirical mode decomposition method to decompose speech signals and extracted Fbank features and their first-order differences for each of the three effective components to construct a new feature map, which could capture details in the frequency domain. Secondly, due to the problems of effective feature loss and high computational complexity in the training process of single channel neural network, we proposed a speech recognition network model in this paper. Finally, training and decoding were performed on the public UA-Speech dataset. The experimental results showed that the accuracy of the speech recognition model of this method reached 92.77%. Therefore, the algorithm proposed in this paper can effectively improve the speech recognition rate of dysarthria.
Humans ; Dysarthria/diagnosis* ; Speech ; Speech Perception ; Algorithms ; Neural Networks, Computer