Objective To study the efficiency of the compound active carbon filter in reducing the content of free radicals in the smoke of cigarettes. Methods 9 trademarks of cigarettes installed the compound active carbon filters were collected and ESR was used to analyze the content of free radicals in the particle and gas phases. Results The results showed that the efficiency of the compound active carbon filters to reduce the content of free radicals in the cigarette smoke was not identical,some samples even showed an inverse result. Conclusion Based on the results of the present paper,it is not considered that the compound active carbon filter can always reduce the free radicals in the smoke of cigarettes.

Objective: To determine the distribution characteristics of waist circumference (WC), waist height ratio (WHtR) of 6–18 years olds in Guangzhou, and to put forward the WC and WHtR appropriate boundary values for 6–18 years olds on the basis of cardiovascular disease (CVD) risk factor assessment. Methods: We analyzed the height, weight, WC and its metabolic indication data (blood pressure, fasting blood glucose, and blood lipids) of 15 000 children in Guangzhou, aged 6–18, with the receiver-operating characteristic curve (ROC), and explored the best value point of WC and WHtRfor the prediction of cardiovascular diseases. Results: When the WC percent reached P85, and WHtR reached 0.48, the cardiovascular risk factors of fasting blood-glucose, blood pressure, and blood fat were signiifcantly higher. Conclusion: The 85th percentile value of WC and 0.48 of WHtR are the appropriate boundary values in increasing the cardiovascular disease risk factors in Chinese children and teenagers. WC and WHtR as a relatively simple inspection method, can well predict cardiovascular diseases, and be used in the conventional measuring items among students.

Recently, deep neural networks (DNNs) have been widely used in the field of electrocardiogram (ECG) signal classification, but the previous models have limited ability to extract features from raw ECG data. In this paper, a deep residual network model based on pyramidal convolutional layers (PC-DRN) was proposed to implement ECG signal classification. The pyramidal convolutional (PC) layer could simultaneously extract multi-scale features from the original ECG data. And then, a deep residual network was designed to train the classification model for arrhythmia detection. The public dataset provided by the physionet computing in cardiology challenge 2017(CinC2017) was used to validate the classification experiment of 4 types of ECG data. In this paper, the harmonic mean of classification accuracy and recall was selected as the evaluation indexes. The experimental results showed that the average sequence level ( ) of PC-DRN was improved from 0.857 to 0.920, and the average set level ( ) was improved from 0.876 to 0.925. Therefore, the PC-DRN model proposed in this paper provided a promising way for the feature extraction and classification of ECG signals, and provided an effective tool for arrhythmia classification.
Arrhythmias, Cardiac ; Disease Progression ; Electrocardiography ; Humans ; Neural Networks, Computer