Objective: To investigate the factors influencing the development of gastric cancer in Chinese populations, so as provide insights into creating a model for predicting gastric cancer incidence among Chinese populations. Methods: The case-control and cohort studies pertaining to factors affecting the development of gastric cancer were retrieved in electronic Chinese and English databases, including CNKI, Wanfang Data, VIP, PubMed, Web of Science and Embase from their inception until September 30, 2021. A meta-analysis was performed using R package version 4.1.0. Sensitivity analysis was performed using the “leave-one-out” evaluation procedure, and the publication bias was evaluated using the Egger regression test and the trim-and-fill procedure. Results: A total of 5 301 publications were screened and 116 eligible studies were included in the final analysis, including 103 case-control studies and 13 cohort studies, which covered approximately 3.23 million study subjects. A total of 45 factors affecting the development of gastric cancer were collected, and there were less than 4 publications reporting 7 factors, which were only qualitatively described. There were 38 factors included in the final meta-analysis. A total of 21 factors were identified as risk factors of gastric cancer, including a history of gastrointestinal diseases (pooled OR=4.85, 95%CI: 3.74-6.29), H. pylori infection (pooled OR=3.18, 95%CI: 2.35-4.32), binge eating and drinking (pooled OR=2.88, 95%CI: 2.09-3.97) and a family history of tumors (pooled OR=2.78, 95%CI: 2.17-3.56), and 10 factors as protective factors, including vegetable intake (pooled OR=0.48, 95%CI: 0.38-0.61), tea consumption (pooled OR=0.55, 95%CI: 0.47-0.64), administration of aspirin (pooled OR=0.53, 95%CI: 0.31-0.92) and administration of statins (pooled OR=0.59, 95%CI: 0.44-0.80). Sensitivity analyses of eating moldy food frequently, white meat intake, favoring spicy food and administration of sulfonylureas were not robust. Following correction with the trim-and-fill procedure, there was still a publication bias pertaining to high income, diabetes, administration of stains, alcohol consumption, tea consumption and white meat intake. Conclusions The development of gastric cancer is associated with a medical history of gastrointestinal disease, H. pylori infection, family history of tumors and poor dietary habits. Risk and protective factors of gastric cancer are recommended to be included in models used to predict gastric cancer incidence among Chinese populations.

Objective To investigate the correlation between the types of constitution in TCM and the sleep status in the PLA Navy divers. Methods Eighty-nine PLA Navy divers who performed 10m diving professional training were selected. Constitution in TCM was classified and determined by the standardized standard Constitution in Chinese Medicine Questionnaire, sleep status was evaluated by the Pittsburgh Sleep Quality Index. Results 62.9% of Navy divers were the mild constitution in TCM. The eight kinds of the biased constitution in TCM are ranked with yang deficiency, phlegm-dampness, dampness-heat, qi deficiency, yin deficiency, blood stasis, qi stagnation and special intrinsic quality. Among 78 Navy divers with good sleep quality, there were 49 divers (72.1%) with the mild constitution in TCM and 19 (27.9%) with the biased constitution in TCM. Among 21 Navy divers with poor sleep quality, there were 7 divers (33.3%) with the mild constitution in TCM (accounting for) and 14 (66.7%) with the biased constitution in TCM. For Navy divers with poor sleep quality, the sleep quality scores were positive correlated with the blood stasis constitution in TCM (r=0.481,P<0.05). Conclusion Sleep status is correlated with the types of the constitution in TCM, and regulating constitution in TCM can improve sleep quality in PLA Navy divers.

Epidemiology is one of main courses for undergraduate students majoring in preventive medicine.There are some limitations in the traditional epidemiology teaching,which is usually characterized in indoctrinated education:"lectured by the teachers and listened by the students." In Zhejiang University,staff of the epidemiology division tried to explore a new teaching mode as ‘student-centered,teacher-leading,question-based,and combining with literature discussion and course practice.'After practicing for two years,students were inspired in learning initiatives,with teaching effectiveness obviously improved.

Objective To use feature selection and Likert grading method to quantify the data of lung cancer medical records,to construct a deep extreme learning machine model optimized by the sparrow search algorithm,to classify and predict the syndrome types of traditional Chinese medicine medical record data of lung cancer,and to provide scientific and effective research on syndrome type classification of traditional Chinese medicine.means.Methods The medical records of 497 cases diagnosed with lung cancer from January 2015 to December 2021 were collected from the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine,and 412 medical records were screened as the research objects.Syndromic factors of different syndromes were summarized by feature selection and feature importance ranking,and the syndrome factors were quantified by Likert grading method.Build a deep extreme learning machine optimized based on the sparrow search algorithm,and train and test the model.Finally,the model built in this paper is compared with other machine learning models according to three evaluation criteria.Results The average classification accuracy of the SSA-DELM model established in this paper was 88.44%,while the average accuracy of the support vector machine and Bayesian network was 83.39%and 84.53%,respectively.The recall rate and F1 value of the SSA-DELM model on the five syndrome types are mostly above 80%,which is also better than other traditional machine learning models.Conclusion The results of the study show that the use of feature selection combined with Likert grading method to quantify the lung cancer medical record data,compared with the 0-1 processing data,can show the characteristics of the data,improve the accuracy of the classification model,SSA-DELM new Compared with other traditional machine learning classification models,the model has better representation learning ability and learning speed.This model not only provides a scientific and technical means for the clinical treatment of lung cancer,but also provides a useful reference for the informatization and intelligent development of TCM syndrome differentiation and treatment.