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Objective To understand the application and research progress of Mendelian randomization (MR) studies related to gastric cancer and provide a scientific basis for gastric cancer prevention. Methods Published studies on risk factors of gastric cancer based on MR methods were searched in PubMed, Web of Science, EMBASE, CNKI, and WANFANG DATA from the establishment of each database to November 19th, 2022. Two researchers examined the eligibility of studies, extracted key information, and assessed the research quality independently. Results A total of 30 publications published from 2016 to 2022 were included in the study, and 20 were judged to be of high quality. These studies examined the relationship between behaviors and lifestyle factors, anthropometric characteristics, indicators of biological exposure, and other pathological conditions and gastric cancer, and the results suggest potential causal associations between smoking and other factors and the risk of gastric cancer. Conclusion Previous MR studies extensively investigated the causal association between internal and external exposures or traits and gastric cancer and provided positive evidence of gastric cancer etiology. However, MR studies may be subject to methodological limitations. Interpretation of results needs to be approached with caution, which necessitates the integration with biological plausibility and evidence from observation studies.

A finite element (FE) model was developed to predict the mechanical behavior of a three-story-three-bay planar frame with concrete-filled steel tubular (CFST) columns after exposure to complete fire process under different fire cases based on multi-scale FE method, considering the steel high-temperature creep and loading at ambient, heating, cooling and post-fire conditions. The FE model was verified by others' experimental results. The deflection of beam and axial deformation of columns were calculated under heating and cooling conditions, respectively. The lateral load P versus lateral displacement Δ relationship of planar frame was simulated after exposure to fire. The results show that the steel high-temperature creep is a part of steel thermal strains and should be considered in the FE model. The frame columns are pushed toward outside due to the thermal expansion of steel beams in heating stage and shrinkage deformation during cooling phase. The beam mid-span sectional deflection is the maximum when fire regions located at three-span in first floor frame and the axial compression deformation of columns is less than the inflation deformation in early heating stage. The bearing capacity and initial stiffness of planar frame are reduced with the fire regions increased. [ABSTRACT FROM AUTHOR]

With the rapid development of network structure, convolutional neural networks (CNN) consolidated its position as a leading machine learning tool in the field of image analysis. Therefore, semantic segmentation based on CNN has also become a key high-level task in medical image understanding. This paper reviews the research progress on CNN-based semantic segmentation in the field of medical image. A variety of classical semantic segmentation methods are reviewed, whose contributions and significance are highlighted. On this basis, their applications in the segmentation of some major physiological and pathological anatomical structures are further summarized and discussed. Finally, the open challenges and potential development direction of semantic segmentation based on CNN in the area of medical image are discussed.

Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.

The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.