Objective:To explore the dietary avoidance compliance of children with cow's milk protein allergy (CMPA) and its correlation with the nutritional risk.Methods:From May 2017 to May 2019, convenience sampling was used to select 94 children with CMPA admitted to the Children's Hospital Affiliated of Zhengzhou University. After 6 months of dietary avoidance and nutritional intervention, 94 children with CMPA were divided into compliance group (36 cases, completing dietary avoidance within 6 months) and non-compliant group (58 cases, failing to adhere to the diet within 6 months) according to the difference in dietary avoidance compliance. We compared the general data, as well as nutrition-related clinical indicators, nutritional risk.Results:There were no statistically significant differences in the levels of PA, SF, Hb and ALB between the two groups before intervention ( P>0.05) . After intervention, the levels of PA, SF, Hb and ALB in compliance group were significantly higher than those in non-compliant group, and the differences were statistically significant ( P<0.05) . The nutritional risk of the compliance group is lower than that of the non-compliant group, the difference was statistically significant ( P<0.05) . Conclusions:High compliance can effectively reduce the incidence of nutritional risks, dietary avoidance has clinical application value. Nursing professionals should clarify the relationship between them, make parents of children clarify the importance of dietary avoidance, and provide help and guidance to improve children 's compliance so as to improve the prognosis of children with CMPA.

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.