Objective Based on the theory of Service Quality to develop a scale for measuring personal digital assistant satisfaction by nurses and to detect the reliability and validity of this scale. Methods Through the literature research, cross-sectional study and present satisfaction evaluation tool for using personal digital assistant by nurse to build the item pool. The items were selected by 15 experts consultation and the pilot survey of 666 nurses. Results The satisfaction evaluation tool for using personal digital assistant by nurse scale consisted of 41 items;the exploratory factor analysis identified 8 principal factors and explained for 65.22%. Pearson correlation coefficient between each dimension was 0.213-0.684(P<0.01). Pearson correlation coefficient between each dimension and total scale was 0.574-0.798(P<0.01). The Cronbach α coefficient of the scale was 0.928 and test-retest reliability was 0.934. Conclusions The satisfaction evaluation tool for using personal digital assistant by nurse scale has good validity and reliability. It can be used as a tool to measure the satisfaction for using personal digital assistant by nurse.

Objective To investigate the status of undergraduates′ sub-health and explore the relationship between personality traits and sub-health and the mediating role of self-harmony. Methods A total of 196 undergraduate nursing students in Tianjin University of Traditional Chinese Medicine were investigated by Sub-health Self-rating Scale (SSS), Self Consistency and Congruence Scale (SCCS) and Eysenck Personality Questionnaire Short Scale. Results The the total scores of the undergraduate nursing students′personality traits, self-harmony and sub-health were 22.71±4.90, 102.74±14.41, 182.54± 31.76. The neuroticism and extroversion of personality traits were significantly correlated with self-harmony and sub-health (P<0.01). Neuroticism had a significant prediction on self-harmony (β=0.37, P<0.01) and sub-health (β=-0.64, P<0.01), after controlling self-harmony, the prediction on sub-health was reduced, but still significant (β=-0.56, P<0.01);extroversion had a significant prediction on self-harmony (β=-0.27, P<0.01) and sub-health (β=0.54, P<0.01), after controlling self-harmony, the prediction on sub-health was reduced, but still significant (β=0.46, P < 0.01). Conclusions The sub-health status of undergraduate nursing students was not optimistic. Nursing students′ personality traits of psychoticism, extroversion, which directly impact on the health status, and through self-harmony indirectly affect their health level, self-harmony played a intermediary role between personality traits and sub-health.

Objective:To evaluate the efficiency of ResNet50-OC model based on deep learning for multiple classification of color fundus photographs.Methods:The proprietary dataset (PD) collected in July 2018 in BenQ Hospital of Nanjing Medical University and EyePACS dataset were included.The included images were classified into five types of high quality, underexposure, overexposure, blurred edges and lens flare according to clinical ophthalmologists.There were 1 000 images (800 from EyePACS and 200 from PD) for each type in the training dataset and 500 images (400 from EyePACS and 100 from PD) for each type in the testing dataset.There were 5 000 images in the training dataset and 2 500 images in the testing dataset.All images were normalized and augmented.The transfer learning method was used to initialize the parameters of the network model, on the basis of which the current mainstream deep learning classification networks (VGG, Inception-resnet-v2, ResNet, DenseNet) were compared.The optimal network ResNet50 with best accuracy and Micro F1 value was selected as the main network of the classification model in this study.In the training process, the One-Cycle strategy was introduced to accelerate the model convergence speed to obtain the optimal model ResNet50-OC.ResNet50-OC was applied to multi-class classification of fundus image quality.The accuracy and Micro F1 value of multi-classification of color fundus photographs by ResNet50 and ResNet50-OC were evaluated.Results:The multi-classification accuracy and Micro F1 values of color fundus photographs of ResNet50 were significantly higher than those of VGG, Inception-resnet-v2, ResNet34 and DenseNet.The accuracy of multi-classification of fundus photographs in the ResNet50-OC model was 98.77% after 15 rounds of training, which was higher than 98.76% of the ResNet50 model after 50 rounds of training.The Micro F1 value of multi-classification of retinal images in ResNet50-OC model was 98.78% after 15 rounds of training, which was the same as that of ResNet50 model after 50 rounds of training.Conclusions:The proposed ResNet50-OC model can be accurate and effective in the multi-classification of color fundus photograph quality.One-Cycle strategy can reduce the frequency of training and improve the classification efficiency.

Objective:To observe and analyze the accuracy of the optic disc positioning and segmentation method of fundus images based on deep learning.Methods:The model training strategies were training and evaluating deep learning-based optic disc positioning and segmentation methods on the ORIGA dataset. A deep convolutional neural network (CNN) was built on the Caffe framework of deep learning. A sliding window was used to cut the original image of the ORIGA data set into many small pieces of pictures, and the deep CNN was used to determine whether each small piece of picture contained the complete disc structure, so as to find the area of the disc. In order to avoid the influence of blood vessels on the segmentation of the optic disc, the blood vessels in the optic disc area were removed before segmentation of the optic disc boundary. A deep network of optic disc segmentation based on image pixel classification was used to realize the segmentation of the optic disc of fundus images. The accuracy of the optic disc positioning and segmentation method was calculated based on deep learning of fundus images. Positioning accuracy=T/N, T represented the number of fundus images with correct optic disc positioning, and N represented the total number of fundus images used for positioning. The overlap error was used to compare the difference between the segmentation result of the optic disc and the actual boundary of the optic disc.Results:On the dataset from ORIGA, the accuracy of the optic disc localization can reach 99.6%, the average overlap error of optic disc segmentation was 7.1%. The calculation errors of the average cup-to-disk ratio for glaucoma images and normal images were 0.066 and 0.049, respectively. Disc segmentation of each image took an average of 10 ms.Conclusion:The algorithm can locate the disc area quickly and accurately, and can also segment the disc boundary more accurately.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition characterized by persistent airflow obstruction caused by long-term airway inflammation or alveolar abnormalities, often manifested as chronic respiratory symptoms and decreased lung function. In recent years, experimental research has shown that mesenchymal stem cells (MSC) have anti-inflammatory, immunomodulatory, and repairing properties of lung epithelial cells, which can be used to treat various diseases including COPD. This article is mainly based on the main findings of in vitro and in vivo animal model experiments and clinical studies of MSC treatment for COPD. It summarizes and discusses the possible mechanisms of action of MSC as a new therapy, and provides new ideas for clinical treatment of COPD.

Objective:To propose and evaluate the cycle-constraint adversarial network (CycleGAN) for enhancing the low-quality fundus images such as the blurred, underexposed and overexposed etc.Methods:A dataset including 700 high-quality and 700 low-quality fundus images selected from the EyePACS dataset was used to train the image enhancement network in this study.The selected images were cropped and uniformly scaled to 512×512 pixels.Two generative models and two discriminative models were used to establish CycleGAN.The generative model generated matching high/low-quality images according to the input low/high-quality fundus images, and the discriminative model determined whether the image was original or generated.The algorithm proposed in this study was compared with three image enhancement algorithms of contrast limited adaptive histogram equalization (CLAHE), dynamic histogram equalization (DHE), and multi-scale retinex with color restoration (MSRCR) to perform qualitative visual assessment with clarity, BRISQUE, hue and saturation as quantitative indicators.The original and enhanced images were applied to the diabetic retinopathy (DR) diagnostic network to diagnose, and the accuracy and specificity were compared.Results:CycleGAN achieved the optimal results on enhancing the three types of low-quality fundus images including the blurred, underexposed and overexposed.The enhanced fundus images were of high contrast, rich colors, and with clear optic disc and blood vessel structures.The clarity of the images enhanced by CycleGAN was second only to the CLAHE algorithm.The BRISQUE quality score of the images enhanced by CycleGAN was 0.571, which was 10.2%, 7.3%, and 10.0% higher than that of CLAHE, DHE and MSRCR algorithms, respectively.CycleGAN achieved 103.03 in hue and 123.24 in saturation, both higher than those of the other three algorithms.CycleGAN took only 35 seconds to enhance 100 images, only slower than CLAHE.The images enhanced by CycleGAN achieved accuracy of 96.75% and specificity of 99.60% in DR diagnosis, which were higher than those of oringinal images.Conclusions:CycleGAN can effectively enhance low-quality blurry, underexposed and overexposed fundus images and improve the accuracy of computer-aided DR diagnostic network.The enhanced fundus image is helpful for doctors to carry out pathological analysis and may have great application value in clinical diagnosis of ophthalmology.

Objective:To preliminarily evaluate the immunogenicity and efficacy of two novel tuberculosis vaccine candidates (a fusion multicomponent protein EPDPA015f and a mixed multicomponent protein EPDPA015m) and to provide a new antigen combination for the development of tuberculosis vaccines.Methods:Recombinant plasmids for the expression of EPDPA015f and EPDPA015m proteins were constructed. Six-week-old BALB/c mice were immunized with EPDPA015f or EPDPA015m in combination with aluminium adjuvant (50 μg/mouse) for three times with an interval of 10 d. The mice were sacrificed 10 d after the last immunization to collect blood and spleen samples. Serum antibody titers and cytokine levels were measured by ELISA, Luminex technique and enzyme-linked immunospot assay (ELISPOT). Mycobacterial growth inhibition assay (MGIA) was used to detect the ability of mouse splenocytes to inhibit the growth of Mtb in vitro. One-way analysis of variance and t-test were used for statistical analysis. Results:Both EPDPA015f and EPDPA015m could induce the production of various cytokines and IgG antibodies at a high level. The levels of cytokines related to Th1 (IL-2, TNF-α, IFN-γ), Th2 (IL-4, IL-6, IL-10) and Th17 (IL-17) as well as other proinflammatory cytokines (GM-CSF, IL-12) were higher in the EPDPA015f group than in the adjuvant group ( P<0.05). The titer of IgG antibody induced by EPDPA015f was as high as 1∶4×10 6. The results of MGIA showed that the numbers of Mtb (lgCFU) in the PBS, adjuvant, EPDPA015f and EPDPA015m groups were 3.46±0.11, 3.51±0.06, 2.98±0.09 and 3.19±0.08, respectively. The number of colonies in the EPDPA015f group was the least as compared with that in the other three groups ( P<0.001, P<0.001, P<0.01). Conclusions:The vaccine candidate EPDPA015f could elicit more comprehensive and high-level cellular and humoral immune responses, and exhibited superior in vitro inhibitory activity against the growth of Mtb. EPDPA015f had the potential to be used as a preventive vaccine or a booster vaccine