Association between H. pylori infection and health Outcomes: an umbrella review of systematic reviews and meta-analyses.

OBJECTIVE: Systematic reviews and meta-analyses have revealed the associations between H. pylori infection and various health outcomes. We aimed to evaluate the strength and breadth of evidence on the associations. DESIGN: Umbrella review of systematic reviews and meta-analyses. SETTING: No settings. PARTICIPANTS: No patients involved. DATA SOURCES: Embase, PubMed, Web of Science, Cochrane Library Databases, CNKI, VIP database and Wangfang database from inception to February 1, 2019. OUTCOMES MEASURES: Diverse diseases (such as cancer and ischaemic heart disease). RESULTS: Sixty articles reporting 88 unique outcomes met the eligible criteria. 74 unique outcomes had nominal significance (p<0.05). Of the outcomes with significance, 61 had harmful associations and 13 had beneficial associations. Furthermore, 73% (64) of the outcomes exhibited significant heterogeneity . Of the these meta-analyses, 32 had moderate to high heterogeneity (I2=50%-75%) and 24 had high heterogeneity (I2>75%). Moreover, 20% exhibited publication bias (p<0.1). In addition, 97% of the methodological qualities were rated 'critically low'. 36% of the evidence qualities of outcomes were rated 'low', 56% of the evidence qualities were rated 'very low' and 8% of the evidence qualities were rated 'moderate'. H. pylori infection may be associated with an increased risk of five diseases and a decreased risk of irritable bowel syndrome. CONCLUSION: Although 60 meta-analyses explored 88 unique outcomes, moderate quality evidence only existed for six outcomes with statistical significance. H. pylori infection may be associated with a decreased risk of irritable bowel syndrome and an increased risk of hypertriglyceridemia, chronic cholecystitis and cholelithiasis, gestational diabetes mellitus, gastric cancer and systemic sclerosis. TRIAL REGISTRATION: CRD42019124680.

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.