Aim To test the skin healing and repairing efficacy and the mechanism of Hibiscus rosa-sinensis L bud extract by using the animal models. Methods KM mice were randomly divided into three groups:the model group, the positive control group, and the n-bu-tyl alcohol extract ( HrBN) group. Using the boils and carbuncles model, the healing condition of all the animals were observed. KM mice were kept in the SPF condition room and divided into five groups: the model group, the positive control group, and the low, middle, high dose groups. Using the full-thickness loss model, the repairing results of all the mice were ob-served. Through the antimicrobial test, the results of MIC and inhibition zone were obtained. The carbon clearance test was used to collect the blood at the time 5min and 15min, and get the liver and spleen, and the results of K andαwere obtained. Results In vivo ex-periments showed there was significant difference be-tween groups;the HrBN extract had the outstanding ef-ficacy in healing and repairing skin boils and full-thickness loss models. It had higher recovery rate than other ethanol extract, such as ethyl acetate extract and chloroform extract. In vitro experiments showed that the HrBN extract, ethyl acetate extract ( HrBE) ,AB-8 macroporous resin 30% alcohol part and 60% alcohol part had obvious antimicrobial efficacy. The carbon clearance test showed HrBN had a good effect in im-proving immune function, and it can increase the K and α. Conclusion HrBN in animal models exerts good skin healing and repairing efficacy, which might be related to its antibacterial activity and immunologic enhancement function.

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 explore the relationship between premenarchal dietary carbohydrate and dietary fiber intakes and age at menarche in Chinese girls. Methods: Based on dietary and menarcheal information on 750 girls from the Chinese Health and Nutrition Survey, multivariate linear regression models and logistic models were used to analyze the influence of dietary carbohydrate and dietary fiber intakes before menarche onset and age at menarche. Carbohydrate intake was replaced by engergg ratio carbohydrate for a sensitivity analysis. Results: Adjusting for residence, per capita household income, body mass index standard deviation score, and energy intake, higher intake and higher energy ratio of carbohydrate were associated with later age at menarche(P<0.01). Compared to girls at the lowest tertile of carbohydrate intake level, those at the highest tertile had a 0.35 years(2.8%) delay in age at menarche, while 55% decreased[OR(95%CI)=0.45(0.30-0.69)]. Dietary fiber intake was not associated with age at menarche in any model(P>0.2). Conclusion Girls with higher premenarcheal carbohydrate intake experienced menarche later, while dietary fiber intake was not associated with age at menarche.

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.

Objective: To determine the association between the intake of five major types of prepackaged foods and the growth and development of school aged children, so as to provide a theoretical basis for guiding school aged children and their parents to make healthy prepackaged food choices. Methods: Based on data from the South West China Childhood Nutrition and Growth Cohort (SCCNG), 381 children (6-11 years of age) were selected by stratified cluster sampling. Dietary intake and pubertal development were collected using questionnaires, and anthropometric measurements were obtained. Children were followed up until November 2022. Binary Logistic regression models were used to analyze the prospective association between prepackaged food intake and the growth and development of school aged children. Results: The total intake of the five major types of prepackaged foods was 316.1 (197.1,501.4) g/d. After 2 years of follow up evaluations, 16.5% of school aged children were shown to be overweight and obese. Early spermarche occurred in 12.6% of boys and early menarche occurred in 15.4% of girls. The following findings were suggested after adjusting for the mothers education level, average gross monthly family income, whether or not the family had one child only, geographic area of residence, body mass index Z score, average duration of daily exercise, and total dietary energy intake: convenience food intake might increase the risk of early spermarche ( OR =9.37); fruit and vegetable intake might decrease the risk of early spermarche and menarche ( OR =0.33,0.17); and fish, poultry, meat, and egg intake might increase the risk of early menarche ( OR =7.59)( P <0.05). Intake of the five types of prepackaged foods was not associated with being overweight or obese after adjusting for confounders ( OR =1.40, 0.57, 0.73, 1.33,1.57, P >0.05). Conclusions The relationship between intake of the five major types of prepackaged foods and pubertal development is inconsistent and no significant correlation was detected between the intake of prepackaged foods and overweight or obese children. Nutrition education should be strengthened to help children and their parents choose healthy prepackaged foods.

Objective: This study examined the gut bacterial communities of dogs from different breeds, all kept under identical domestication conditions. Methods: Noninvasive sampling and 16S rRNA high-throughput sequencing were used to compare the composition and function of the gut microbiota of three dog breeds: the Chinese Kunming dog (CKD), German Shepherd dog (GSD), and Belgian Malinois dog (BMD). Results: The gut microbiota of the three dog breeds consisted of 257 species across 146 genera, 60 families, 35 orders, 15 classes, and 10 phyla. The dominant bacterial phyla across the three breeds were Firmicutes (57.44%), Fusobacteriota (28.86%), and Bacteroidota (7.63%), while the dominant bacterial genera across the three breeds were Peptostreptococcus (21.08%), Fusobacterium (18.50%), Lactobacillus (12.37%), and Cetobacter (10.29%). Further analysis revealed significant differences in the intestinal flora of the three breeds at the phylum and genus levels. The intestinal flora of BMD was significantly richer than that of CKD and GSD. The functional prediction and Kyoto Encyclopedia of Genes and Genomes analysis showed that the primary functions of the gut microbiota in these breeds were similar, with significant enrichment in various metabolic pathways, including carbohydrate and amino acid metabolism, secondary metabolite biosynthesis, and microbial metabolism in different environments. The intestinal flora of these breeds also played a crucial role in genetic information processing, including transcription, translation, replication, and material transport. Conclusions and Relevance: These results provide novel insights into the intestinal flora of intervention dogs and suggest novel methods to improve their health status, which help increase microbial diversity and normalize metabolite production in diseased dogs.

Objective: This study examined the gut bacterial communities of dogs from different breeds, all kept under identical domestication conditions. Methods: Noninvasive sampling and 16S rRNA high-throughput sequencing were used to compare the composition and function of the gut microbiota of three dog breeds: the Chinese Kunming dog (CKD), German Shepherd dog (GSD), and Belgian Malinois dog (BMD). Results: The gut microbiota of the three dog breeds consisted of 257 species across 146 genera, 60 families, 35 orders, 15 classes, and 10 phyla. The dominant bacterial phyla across the three breeds were Firmicutes (57.44%), Fusobacteriota (28.86%), and Bacteroidota (7.63%), while the dominant bacterial genera across the three breeds were Peptostreptococcus (21.08%), Fusobacterium (18.50%), Lactobacillus (12.37%), and Cetobacter (10.29%). Further analysis revealed significant differences in the intestinal flora of the three breeds at the phylum and genus levels. The intestinal flora of BMD was significantly richer than that of CKD and GSD. The functional prediction and Kyoto Encyclopedia of Genes and Genomes analysis showed that the primary functions of the gut microbiota in these breeds were similar, with significant enrichment in various metabolic pathways, including carbohydrate and amino acid metabolism, secondary metabolite biosynthesis, and microbial metabolism in different environments. The intestinal flora of these breeds also played a crucial role in genetic information processing, including transcription, translation, replication, and material transport. Conclusions and Relevance: These results provide novel insights into the intestinal flora of intervention dogs and suggest novel methods to improve their health status, which help increase microbial diversity and normalize metabolite production in diseased dogs.

Objective: This study examined the gut bacterial communities of dogs from different breeds, all kept under identical domestication conditions. Methods: Noninvasive sampling and 16S rRNA high-throughput sequencing were used to compare the composition and function of the gut microbiota of three dog breeds: the Chinese Kunming dog (CKD), German Shepherd dog (GSD), and Belgian Malinois dog (BMD). Results: The gut microbiota of the three dog breeds consisted of 257 species across 146 genera, 60 families, 35 orders, 15 classes, and 10 phyla. The dominant bacterial phyla across the three breeds were Firmicutes (57.44%), Fusobacteriota (28.86%), and Bacteroidota (7.63%), while the dominant bacterial genera across the three breeds were Peptostreptococcus (21.08%), Fusobacterium (18.50%), Lactobacillus (12.37%), and Cetobacter (10.29%). Further analysis revealed significant differences in the intestinal flora of the three breeds at the phylum and genus levels. The intestinal flora of BMD was significantly richer than that of CKD and GSD. The functional prediction and Kyoto Encyclopedia of Genes and Genomes analysis showed that the primary functions of the gut microbiota in these breeds were similar, with significant enrichment in various metabolic pathways, including carbohydrate and amino acid metabolism, secondary metabolite biosynthesis, and microbial metabolism in different environments. The intestinal flora of these breeds also played a crucial role in genetic information processing, including transcription, translation, replication, and material transport. Conclusions and Relevance: These results provide novel insights into the intestinal flora of intervention dogs and suggest novel methods to improve their health status, which help increase microbial diversity and normalize metabolite production in diseased dogs.

Objective: This study examined the gut bacterial communities of dogs from different breeds, all kept under identical domestication conditions. Methods: Noninvasive sampling and 16S rRNA high-throughput sequencing were used to compare the composition and function of the gut microbiota of three dog breeds: the Chinese Kunming dog (CKD), German Shepherd dog (GSD), and Belgian Malinois dog (BMD). Results: The gut microbiota of the three dog breeds consisted of 257 species across 146 genera, 60 families, 35 orders, 15 classes, and 10 phyla. The dominant bacterial phyla across the three breeds were Firmicutes (57.44%), Fusobacteriota (28.86%), and Bacteroidota (7.63%), while the dominant bacterial genera across the three breeds were Peptostreptococcus (21.08%), Fusobacterium (18.50%), Lactobacillus (12.37%), and Cetobacter (10.29%). Further analysis revealed significant differences in the intestinal flora of the three breeds at the phylum and genus levels. The intestinal flora of BMD was significantly richer than that of CKD and GSD. The functional prediction and Kyoto Encyclopedia of Genes and Genomes analysis showed that the primary functions of the gut microbiota in these breeds were similar, with significant enrichment in various metabolic pathways, including carbohydrate and amino acid metabolism, secondary metabolite biosynthesis, and microbial metabolism in different environments. The intestinal flora of these breeds also played a crucial role in genetic information processing, including transcription, translation, replication, and material transport. Conclusions and Relevance: These results provide novel insights into the intestinal flora of intervention dogs and suggest novel methods to improve their health status, which help increase microbial diversity and normalize metabolite production in diseased dogs.

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.