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Objective: To construct a prediction model for the risk of ischemic stroke (IS) by classification tree model, and evaluate its application value., Methods: By cluster sampling, 858 IS patients with perfect clinical data from January to December 2017 in the Affiliated Hospital of Guilin Medical College (IS group) were enrolled, and 844 health checkups matched with the gender and age of IS patients in the same period were enrolled as controls (healthy control group). The metabolic characteristics of the two groups were compared and analyzed. The classification tree model was used to construct the prediction model of the risk of IS, and the gain diagram, index chart, risk value of misclassification probability and receiver operating characteristic curve (ROC) were used to evaluate the application value of the model., Results: Compared with the healthy control group, body mass index (BMI), fasting blood glucose (FPG), triglyceride (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C) in IS group were significantly increased [BMI (kg/m 2 ): 25.34±3.70 vs. 24.24±3.10, FPG (mmol/L): 6.79±2.89 vs. 5.73±1.17, TG (mmol/L): 1.62±1.06 vs. 1.44±1.06, TC (mmol/L): 4.70±2.73 vs. 4.35±0.79, LDL-C (mmol/L): 3.18±0.94 vs. 2.73±0.73, all P < 0.01], high density lipoprotein cholesterol (HDL-C) was significantly decreased (mmol/L: 1.12±0.33 vs. 1.35±0.36, P < 0.01), and the proportion of hypertension, smoking and drinking were significantly increased (69.0% vs. 41.9%, 23.1% vs. 16.8%, 19.2% vs. 13.4%, all P < 0.01). By assigning values to each factor [IS: No = 0,Yes = 1; BMI: < 24.0 kg/m 2 = 0, ≥ 24.0 kg/m 2 = 1; FPG: < 7.0 mmol/L = 0, ≥ 7.0 mmol/L = 1; TG: < 2.26 mmol/L = 0, ≥ 2.26 mmol/L = 1; TC: < 6.22 mmol/L = 0, ≥ 6.22 mmol/L = 1; LDL-C: < 4.14 mmol/L = 0, ≥ 4.14 mmol/L = 1; HDL-C: < 1.04 mmol/L = 0, ≥ 1.04 mmol/L = 1; hypertension: No = 0,Yes = 1; smoking: No = 0,Yes = 1; drinking: No = 0,Yes = 1], a classification tree model was established to analyze the risk factors of IS. The classification tree model consisted of 4 layers and 17 nodes: the first layer was hypertension, the second layer was FPG and HDL-C, the third layer was HDL-C and FPG, and the fourth layer was LDL-C and smoking. There were five explanatory variables screened out in the model, including hypertension, FPG, HDL-C, LDL-C and smoking. The first layer of the tree showed that the incidence of IS in hypertensive population (62.6%) was significantly higher than that in non-hypertensive population (35.2%). The second layer of the tree showed that the incidence of IS in people with hypertension with HDL-C ≥ 1.04 mmol/L (53.6%) was lower than that in people with HDL-C < 1.04 mmol/L (78.5%). However, in the population without hypertension, the probability of IS occurrence in the population with FPG ≥ 7.0 mmol/L (71.1%) was significantly higher than that in the population with FPG < 7.0 mmol/L (28.3%). The third layer of the tree showed that the IS incidence of HDL-C ≥ 1.04 mmol/L (21.8%) was lower than that of HDL-C < 1.04 mmol/L (48.7%) in the population without hypertension and FPG < 7.0 mmol/L. However, in the population with hypertension and HDL-C ≥ 1.04 mmol/L, the probability of IS occurrence in the population with FPG ≥ 7.0 mmol/L (78.6%) was significantly higher than that in the population with FPG < 7.0 mmol/L (46.7%). The fourth layer of the tree showed that the IS incidence of people with LDL-C ≥ 4.14 mmol/L (53.8%) was higher than that of people with LDL-C < 4.14 mmol/L (19.0%) in the population without hypertension, FPG < 7.0 mmol/L and HDL-C ≥ 1.04 mmol/L. In the population without hypertension, the incidence of IS in smokers (76.9%) was higher than that in non-smokers (39.1%) of people with FPG < 7.0 mmol/L and HDL-C < 1.04 mmol/L. In the population with hypertension, the probability of IS occurrence in the population with LDL-C ≥ 4.14 mmol/L (72.5%) was higher than that in the population with LDL-C < 4.14 mmol/L (44.4 %) of people with HDL-C ≥ 1.04 mmol/L and FPG < 7.0 mmol/L. The gain diagram of IS classification tree model shown that the gain value increased rapidly from 0% to 100% and then tended to be stable. The index chart shown that the index value kept stable in the moving direction from above 100% and then dropped rapidly to 100%, indicating the model was very well. The risk value of misclassification probability of the classification tree model was 0.291, and the correct rate of risk factor for IS patients was 70.90%. The area under ROC curve (AUC) was 78.0% [95% confidence interval (95%CI) = 75.9%-79.9%, P < 0.001], the sensitivity was 62.5% (95%CI = 59.1%-65.7%) and the specificity was 79.4% (95%CI = 76.5%-82.1%)., Conclusions: Classification tree model can properly predict the risk factor of IS, and the most important risk factors are hypertension, hyperglycemia, high LDL-C and smoking.

Objective: To determine the normal level of nitric oxide through mouth and nose in healthy adult people. From it the normal values were obtained to provide theory support for the prevention and cure of breathe system diseases, such as asthma, allergic rhinitis and so on., Method: One hundred and eighty two healthy nanjing adults were recruited,including 89 males and 93 females. NIOX system was used to obtain the values of exhaled NO through mouth and nose. The relativity between NO and gender, age, height, body mess index, time, ambient NO were analyzed with multiple linear regression and correlation., Result: Exhaled NO values were (17 +/- 7) ppb and correlated significantly with height and age. Regression equation: Y (FENO) = -65.784 + 0.102X1 (age) + 0.442X2 (height), P < 0.05. NNO values were (79 +/- 35) ppb., Conclusion: FENO normal values and NNO normal values are easy to obtain and have good compliance and high sensitivity, which correlated with height, age, et al.

A novel method for the determination of trace amounts of mercury in sediments by sequential injection cold vapor atomic fluorescence spectrometry coupled with microwave assisted digestion was developed in this paper. Satisfactory results were found when the digestion was carried out at 140 degrees C for 5 min by using 10% HCl-50% HNO3- 40% H2O or 30% HCl-20% HNO3- 50% H2O media. The linear range was 0.02-30 ng x mL(-1) with detection limit of 0.5 ng x g(-1). Relative standard deviation for the complete procedure of the analysis of 10 digested sediment samples was 3.7%. Recoveries of the spiked samples were between (91.2 +/- 4.3)% and (96.5 +/- 4.6)%. The analytical results for three certified reference materials GSD-2, GSD-9 and GSD-10 were consistent with the certified values. The method has been successfully applied to six natural sediment samples. The results indicated that the method was rapid, highly sensitive and precision, and suitable for the determination of trace amounts of mercury in sediment samples.