Objective: To analyze the epidemiological characteristics and related factors of obesity in children aged 3-6 years old in Hebei Province, and to provide a reference for childhood obesity prevention and control strategies. Methods: A total of 6 034 children aged 3-6 years were randomly selected from 11 cities in Hebei Province for physical examination and questionnaire survey. Results: The prevalence of obesity in 3-6 year old boys and girls in Hebei Province were 23.00% and 17.48 %, which differed significantly ( χ 2=28.51, P <0.01); The prevalence of obesity was higher in rural areas and children with ethnic minorities ( 20.06 %, 21.68%) than that of urban are and Han children (19.97%, 20.09%), with no significant differences ( χ 2= 0.01 , 0.78, P ＞0.05). Multivariate Logistic regression analysis revealed that boys( OR =1.45), birth weight no less than 4 000 g ( OR =2.80), high fat food consumption at least 3 times a week （OR =1.64), carbonated drinks consumption at least 3 times a week ( OR =4.71), insufficient fruits and vegetables consumption ( OR =1.22), physical activities less than 2 hours per day ( OR =1.82), maternal obesity ( OR =2.0), and lack of physical exercise of fathers ( OR =1.95) were significantly associated with higher risk for obesity among young children in Hebei Province ( P <0.01). Conclusion The prevalence of obesity among children aged 3-6 years in Hebei Province is at a high level at present. Many factors contribute to this epidemic such as genetics, poor diet and living habits. Promotion of healthy eating and lifestyle, as well as dissemination of reliable knowledge about childhood obesity are greatly needed.

Objective:To develop the Traditional Chinese Medicine (TCM) symptom scores of qi depression and phlegm obstruction patternfor dyslipidemia patients, in order to optimize the TCM pattern evaluation method.Methods:According to Delphi method, the two roundsquestionnaires were distributed to experts through face-to-face interviews or emails, survey were recorded and analyzed via Excel 2016 and SPSS 17.0.Results:The experts are members of the Standing Committee of the Cardiovascular Diseases Branch of the China Association of Chinese Medicine, and the number of experts in the two rounds was 30 and 33, respectively. Active coefficients of both rounds were 100%. The Cronbach’s Alpha of consultations in the first and second rounds were 0.896 and 0.885, respectively. There were 36 symptom items in the initial scales of TCM symptom scores. In the first round, 17 items were eliminated, and in the second round, 1 item was added, 5 items were eliminated. The 15 items scales includededobesity, dizziness, head weight such as wrapping, head faintness, mouth viscosity, phlegm, chest depression, chest and flank distension, abdominal distension, heavy numbness, fatigue and fatigue, body weight, depression, stagnant stool and sighing frequently. The items for symptom rating scale were suitable.Conclusion:The TCM symptom scores of qi depression and phlegm obstruction pattern can be used to evaluate the effect of TCM treatment for the patients with dyslipidemia and TCM pattern of qi depression with phlegm obstruction, and the studyprovides important guidance for TCM in the diagnosis and treatment of dyslipidemia. In addition, the enthusiasm and authority of experts, as well as its concentration, reliability and coordination, are all well considered in this study, and the results of this consultation are desirable.

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.
Drug Delivery Systems ; Proteins ; Transcriptome