Objective:To research the curative effect of moxifloxacin solution atomization inhalation in the treatment of chronic obstructive pulmonary disease (COPD) combined with respiratory failure.Methods:94 cases of COPD patients combined with respiratory failure from May 2014 to May 2016 were selected and divided into the control group(n=47) and the research group (n=47) acording to the lottery method,the control group received routine treatment,while the research group was treated based on the control group with moxifloxacin solution inhalation treatment.The curative effect,serum tumor necrosis factor-α(TNF-α),c-reactive protein (CRP) and interleukin 10 (IL-10),creatine phosphokinase (CK),aspertate aminotransferase (AST) levels,the blood oxygen partial pressure (PaO2),CO2 partial pressure (PaCO2),APACHE Ⅱ score and occurrence of adverse reactions were compared between two groups.Results:After treatment,the total effective rate of research group was higher than that of the control group (P＜0.05).The serum levels of TNF alpha,CRP,PaCO2,CK,AST,APACHE Ⅱ score of research group were evidently lower than those of the control group (P＜0.05).The serum levels ofIL-10,PaO2 of research group were evident higher than those of the control group (P＜0.05).The occurrence of adverse reactions showed no differences between the two groups (P＞0.05).Conclusion:Moxifloxacin solution atomization inhalation was effective in the treatment of COPD combined with respiratory failure,which might be related to the inhibition of inflammatory response,and improvment of breath.

Objective To identify the pathogenic bacteria such as Staphylococcus aureus and Enterococcus faecalis in bloodstream infec-tions with high confidence based on three deep learning models such as GoogleNet,ResNet101,and Vgg19,compare the performance and classification ability of these models,and explore the feasibility of applying the deep learning models for the rapid identification of pathogenic bacteria in bloodstream infections.Methods The preprocessed Gram-stained bacterial images,including 1 682 images for Staphylococcus aureus and 1 723 for Enterococcus faecalis,and 688 blank control microscopic images were input into three models for training and validation,respectively.Among them,1 344 images for Staphylococcus aureus,1 376 for Enterococcus faecalis,and 544 blank control images were used for training,and the remaining images were used for validation.The model with the best performance was identified according to the classification parameters between the models.Results The ResNet101 model had the lowest cross-en-tropy loss value(0.008 710 3),the largest Epoch value(93),and the highest accuracy rate(99％)for identifying the three types of validation set images.The cross-entropy loss value,Epoch value,and accuracy rate of the GoogleNet model were 0.063 89,86 and 98.6％,respectively,for identifying the three types of validation set images.Those of the Vgg19 model were 0.035 682,86 and 97.7％,respectively.Conclusion The ResNet101 model has the best performance in the classification of three kinds of images.The deep learning model may accurately,reliably and rapidly identify the Gram-stained images of pathogenic bacteria such as Staphylococcus aureus and Enterococcus faecalis in bloodstream infections.