Objective In order to understand the current management status on education/ training and needs for training among new employees working at the provincial CDC in China during 2012-2014,so as to provide basis for setting up related programs at the CDC levels.Methods Based on data gathered through questionnaire surveys run by CDCs from 32 provincial and 5 specifically-designated cities,microsoft excel was used to analyze the current status on management of education and training,for new employees.Results There were 156 management staff members working on education and training programs in 36 CDCs,with 70％ of them having received intermediate or higher levels of education.Large differences were seen on equipment of training hardware in different regions.There were 1 214 teaching staff with 66 percent in the fields or related professional areas on public health,in 2014.5084 new employees conducted pre/post training programs,from 2012 to 2014 with funding as 750 thousand RMB Yuan.99.5％ of the new employees expressed the needs for further training while.74％ of the new staff members expecting a 2-5 day training program to be implemented.79％ of the new staff members claimed that practice as the most appropriate method for training.Conclusions Institutional programs set for education and training at the CDCs need to be clarified,with management team organized.It is important to provide more financial support on both hardware,software and human resources related to training programs which are set for new stuff members at all levels of CDCs.

Objective To explore the mechanism of Buyang Huanwu Decoction for"treating different diseases with same therapies"in type 2 diabetes mellitus(T2DM)and Alzheimer's disease(AD)based on network pharmacology and molecular docking techniques.Methods Firstly,the active ingredients of seven herbs in Buyang Huanwu Decoction were searched and screened by TCMSP,SymMap and other databases,the target prediction of these active ingredients was carried out by PharmMapper.The disease targets of T2DM and AD were collected from OMIM,DrugBank,GeneCards and Disgenet databases.The potential targets of Buyang Huanwu Decoction for"treating different diseases with same therapies"in T2DM and AD were obtained by intersecting with targets of active ingredients and the disease targets.Then STRING database and Cytoscape software were used to construct the PPI network and"herbs-components-targets"network,respectively.The core targets and pharmacodynamic components were screened through network topology analysis.Furthermore,GO functional and KEGG enrichment analysis was performed for potential targets using Metascape database.Finally,AutoDock software was used to verify the molecular docking between the selected components and targets.Results Ninety-four active components of Buyang Huanwu Decoction can act on 342 protein targets,and 100 intersection targets were obtained by comparing with 3 140 AD targets and 1 708 T2DM targets.GO functional enrichment analysis showed that these targets were mainly involved in MAPK cascade-mediated regulation,hormone-mediated signaling pathways,cellular response to lipids,regulation of inflammation response and other biological processes.MAPK,PI3K/Akt,FoxO,AGE/RAGE,insulin resistance,lipid and atherosclerosis,and non-alcoholic fatty liver signaling pathway were significantly enriched in KEGG analysis.PPI and topology analysis of"herbs-components-targets"network were used to screen out 10 core targets such as MAPK8,MAPK14,GSK3B,PPARG,and 10 core pharmacodynamic components such as paeoniflorin,benzoyl paeoniflorin,(+)-catechin.The results of molecular docking showed that these components had strong binding ability to the targets.Conclusion The core components of Buyang Huanwu Decoction,such as paeoniflorin and catechin,may act on PPARG,GSK3B and other key targets,and participate in the regulation of signaling pathways including MAPK and PI3K/Akt,which play a role in"treating different diseases with the same therapies"of T2DM and AD.

Background: Computer-aided diagnosis based on deep learning technology is a research hotspot in the field of gastroenterology, and computer-aided diagnosis of colorectal polyps has received more and more attention. Aims: To validate a model based on deep learning for the automatic identification of colorectal polyps, and to analyze its auxiliary learning function for helping novice endoscopists. Methods: A total of 1 200 colonoscopy images (600 colorectal polyp images and 600 normal images) in the endoscopy center database of Qingdao Municipal Hospital (East) from January 2019 to January 2020 were retrospectively collected. Deep learning model was used to identify the 1 200 images. The sensitivity, specificity, accuracy and diagnosis time of deep learning model and 5 novice endoscopists for diagnosis of colorectal polyps were compared. Results: The deep learning model showed a sensitivity of 93.2%, specificity of 98.7%, accuracy of 95.9% for detecting colorectal polyps, and the diagnosis time of each image was (0.20±0.03) second. The sensitivity, accuracy, and diagnosis time of the model were superior to 5 novice endoscopists, and the specificity was superior to some novice endoscopists. The accuracies of model for polyps with size ≤5 mm and 6~9 mm were 88.1% and 96.8%, respectively, and were superior to 5 novice endoscopists; the accuracy of model for polyps with size ≥10 mm was 100%, and was similar to 5 novice endoscopists. The accuracy of model for polyps with protrude type was 94.8%, and was superior to some novice endoscopists; the accuracy of model for polyps with flat type was 91.7%, and was superior to 5 novice endoscopists. Missing the polyps with flat type (38.8%), polyps at mucosal folds (32.7%), and mistaking the mucosal folds as polyps (12.2%) were the main causes of false negative or false positive results of the model. Conclusions: The deep learning model has a high accuracy, sensitivity, specificity and shorter diagnosis time for diagnosis of colorectal polyps, and can be used to assist novice endoscopists in diagnosing small polyps and flat polyps.