Objective:To develop the screening checklist of brief interview for autism disorder suitable for Chinese children and evaluate its reliability and validity.Methods:Based on existed research results and diagnostic criteria for autism spectrum disorder of DSM-5, the screening checklist of brief interview for autism spectrum disorder(SCAD) was developed. A sample of 238 children were selected for investigation and 28 of them were retested for test-retest reliability with 2-4 weeks interval. Cronbach's α coefficient, split-half correlation coefficient, test-retest reliability, and evaluator consistency were used to test the reliability of the scale. Content validity, construct validity and empirical validity were used to test the validity of the scale.All statistical analysis were conducted by SPSS 22.0 and AMOS 17.0.Results:The SCAD contains two components and six dimensions, with a total of 25 items. The Cronbach's α coefficient was 0.936 for the total scale and were 0.938, 0.771 for the two components. The split-half coefficient for the total scale and the two components were 0.962, 0.938 and 0.794. The test-retest reliability for the total scale and the two components were 0.806, 0.795 and 0.766. The Kendall coefficient for the total scale and the two components were 0.968, 0.982 and 0.950. The SCAD item-level content validity index (I-CVI) ranged from 0.66 to 0.98 and the Kappa value ranged from 0.66 to 0.98. The scale-level content validity S-CVI/UA and S-CVI/Ave were 0.89 and 0.94. The correlations between SCAD and calibration tests such as ABC, CARS and M-CHAT were 0.54, 0.53 and 0.87, and the correlation coefficients with the M-CHAT-R/F between the two components were 0.87 and 0.76 respectively (both P<0.01). The result of CFA demonstrated that the model fitted the data with well construct validity(χ 2/ df=0.910, RMR=0.049, AGFI=0.974, RMSEA=0.010, PNFI=0.530, PCFI=0.533, NFI=0.994, RFI=0.988, CFI=1.000). The correlation coefficient of the two components was 0.88 and that with the total scale were 0.97 and 0.90, each dimensions with the total scale ranged from 0.72 to 0.93. Conclusion:The SCAD has a good reliability and validity, and it can be used as a clinical screening tool for children with autism spectrum disorder.

Objective To investigate the solubility and stability of Tetrodotoxin (TTX) in different solvents, and the effect of temperature and pH on its stability. Methods Solutions of TTX in different matrices were prepared. Their concentrations at different temperatures and pH buffers were determined by high performance liquid chromatography and their solubility and stability were analyzed and calculated. Results TTX was most soluble at pH 3.5 and its solubility decreased as the pH increased. TTX degraded most rapidly under strong alkali conditions, with complete degradation after 20 min of reaction at 0.1 mol/L sodium hydroxide and 70 ℃. The stability test results similarly demonstrated that TTX was least stable under alkaline conditions. In a PBS buffered solution at 37 ℃, pH 7.4, TTX concentration began to decrease consistently at 1~10h, with a degradation rate of 88.07±0.27% after 28 days. Conclusion TTX is readily soluble in acidic aqueous solutions at pH 3.5 and almost insoluble in alkaline aqueous solutions. Its stability is closely related to the temperature and pH of the medium. It is more stable in acidic aqueous solutions and easily degrades under alkaline conditions, and its degradation process could be accelerated by increasing temperature.

Objective To establish a simple model for arteriosclerosis(AS)screening to provide a viable tool for the timely identification of AS risk among residents aged 40～65 years.Methods Data were obtained from the Sleep and Chronic Diseases Program in Fuquan City.The original dataset was divided into a training subset and a validation subset(80%:20%).LASSO and logistic regression models were used to screen variables,perform multivariate regression analyses.Internal validation was performed using the Bootstrap method.Nomogram Plot was constructed,and risk score thresholds were determined based on ROC curves to classify high-risk populations.Results RS Model was established to include age,gender,napping,sleep efficiency,sleep disorders,hyperten-sion and diabetes,with AUC=74.80%and a model risk score threshold=84.20.PHC Model was established to include age,gender,napping,sleep efficiency,systolic blood pressure,fasting blood glucose,and pulse variables,with AUC=82.80%and a risk score threshold of 78.00.Decision curves showed that both models performed well in terms of calibration and actual benefits for health management.Conclusion The two AS screening models exhibit acceptable accuracy and differentiation.Therefore,it can be applied in residents'self-health management and in primary care organizations'screening work in a large scale.

Objective:To analyze the association between body health score and the risk of hypertension among health examination population aged 40-65 years.Methods:This study was a cross-sectional study, and 1 104 people aged 40-65 years who underwent physical examination at the Physical Examination Centre of the First People′s Hospital of Fuquan City from March to November 2022 were selected. Clinical data, such as general information, physical examination, body composition and history of hypertension diseases, were collected. The body health score was reported by the Xiaomi Body Fat Scale′s accompanying exercise health software, and was calculated by combining body fat, water and other body composition data. The association between body health score and the risk of hypertension was analyzed using restricted cubic spline regression models, while a sensitivity analysis and sex-stratified analyses were performed. Multivariate logistic regression combined with stratified analysis was used to explore the association between dimensions of body composition and the risk of hypertension.Results:The body health score was significantly lower in hypertensive patients than in non-hypertensive patients among the 1 104 health examination population [52.0(30.0) vs 69.0(35.8) points] ( Z=-8.547, P<0.001). The lower the body health score, the higher the risk of hypertension ( χ2=18.48, PNonlinear<0.001). In the total population, high body mass index was associated with an increased risk of hypertension ( OR=1.744, 95% CI: 1.104-2.765), high protein content was associated with a reduced risk of hypertension ( OR=0.587, 95% CI: 0.344-0.982) (both P<0.05). Gender-stratified analyses showed that high protein content was associated with a reduced risk of hypertension only in men ( OR=0.233, 95% CI: 0.080-0.592) ( P=0.004). High body mass index was positively associated with the risk of hypertension when the body health score was ≥60 points ( OR=2.378, 95% CI: 1.255-4.542) ( P=0.008). High visceral adiposity index (VAI) was positively associated with the risk of hypertension when the body health score was <60 points ( OR=4.395, 95% CI: 1.466-13.620), and high protein content was negatively associated with the risk of hypertension ( OR=0.255, 95% CI: 0.091-0.638) (all P<0.05). Conclusions:Health examination population aged 40-65 years with lower scores of physical health are more likely to have a risk of hypertension. Men should pay attention to the impact of body protein in hypertension risk prevention and control. The effect of body mass index should be noted when body health scores are ≥60 points, and the effect of VAI and body protein should be considered when body health scores are <60 points.