Objectives Building a chest X-ray(CXR)image classification model based on convolutional neural networks in deep learning,providing reliable auxiliary diagnostic techniques for lung diseases.Methods Four kinds of chest X-ray pictures of COVID-19,mild pulmonary infection,viral pneumonia and normal were collected through KAGGLE database,and the data were randomly divided into training set,test set and verification set according to 3∶1∶1 ratio.Building a CXR image classification model based on convolutional neural network architecture,adjusting hyperparameters to strengthen and optimize the model.Subsequently,the model was validated and evaluated using metrics such as confusion matrix,accuracy,sensitivity,and K-fold cross validation results.Results The classification accuracy of this research model for lung imaging images is 0.81,the sensitivity is 0.80,and the loss values of the test and validation sets can be stable at a relatively low level.Compared with models with the same migration algorithm,the accuracy,sensitivity,and F1 score on the test dataset were improved by 1.7%,1.7%,1.3%,and 2.9%,respectively.Conclusion This model has stronger recognition and classification performance for CXR images,and can be more effectively applied to auxiliary analysis and judgment of lung diseases.

Objective In this study，we performed two sampie Mendelian Randomization to infer a causal association between Gastroesophageal reflux(GERD) and Atrial fibrillation(AF)，it can effectively avoid the problems such as reverse causation and confounds in traditional epidemiology. Methods We used the Summary data of GERD and AF from published Genome wide association study(GWAS) of European Individuals. Single Nucleotide Polymorphisms (SNPs) were extracted as Instrumental Variables (IVs).The main MR methods include Inverse Variance [] Weighted(IVW)，Weighted Median(WME)，MR-Egger，Simple Mode，and Weighted Mode.In addition，we used the sensitivity analysis such as MR-PRESSO，Cochran's Q test etc. Results The IVW shows a causal association between GERD and AF(P<0.0001，OR=1.16，95%CI:1.10-1.23).The WME shows P＜0.0001，OR=1.20，95%CI:1.11-1.30；Simple Mode shows P=0.01，OR=1.34，95%CI:1.07-1.69；Weighted Mode shows P=0.02，OR=1.33，95%CI:1.06-1.66. Conclusion This study based on genetic data supports the causal association between GERD and AF. The occurrence of GERD could increase the risk of AF.