Association Between Aspartate Aminotransferase to Alanine Aminotransferase Ratio and Incidence of Type 2 Diabetes Mellitus in the Japanese Population: A Secondary Analysis of a Retrospective Cohort Study.

BACKGROUND: The study on the association between aspartate aminotransferase to alanine aminotransferase (AST/ALT) ratio and the risk of type 2 diabetes mellitus (T2DM) was limited. Therefore, we conducted a secondary analysis based on online data to explore whether there was an association between the AST/ALT ratio and incident T2DM among a large number of Japanese people. METHODS: The study was a retrospective cohort study. We downloaded the NAGALA (NAfld in Gifu area) data from DATADRYAD website between 2004 and 2015. This present study included 15,291 participants. Cox proportional-hazards regression, generalized additive models and subgroup analyses were used to find out the association between the AST/ALT ratio and T2DM events. RESULTS: The negative relationship was shown between AST/ALT ratio and incident T2DM (HR = 0.617, 95% CI: 0.405-0.938) in our study. A non-linear relationship and saturation effect were found between them, and the inflection point was 0.882. It indicated that the AST/ALT ratio was negatively correlated with incident T2DM when the AST/ALT ratio was less than the inflection point (HR = 0.287, 95% CI: 0.126-0.655, p = 0.0030). We found that exercise modified their relationship (P for interaction = 0.0024), and people who did not exercise associated strongly (HR = 0.464 95% CI: 0.290-0.741). CONCLUSION: AST/ALT ratio was negatively associated with T2DM risk, and their relationship was non-linear and had a saturation effect. When the AST/ALT ratio was less than 0.882, they showed a significant negative correlation.

Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation.

The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed tomography (CT) scans is of great importance for supervising disease progression and further clinical treatment. As labeling COVID-19 CT scans is labor-intensive and time-consuming, it is essential to develop a segmentation method based on limited labeled data to conduct this task. In this paper, we propose a self-ensembled co-training framework, which is trained by limited labeled data and large-scale unlabeled data, to automatically extract COVID lesions from CT scans. Specifically, to enrich the diversity of unsupervised information, we build a co-training framework consisting of two collaborative models, in which the two models teach each other during training by using their respective predicted pseudo-labels of unlabeled data. Moreover, to alleviate the adverse impacts of noisy pseudo-labels for each model, we propose a self-ensembling strategy to perform consistency regularization for the up-to-date predictions of unlabeled data, in which the predictions of unlabeled data are gradually ensembled via moving average at the end of every training epoch. We evaluate our framework on a COVID-19 dataset containing 103 CT scans. Experimental results show that our proposed method achieves better performance in the case of only 4 labeled CT scans compared to the state-of-the-art semi-supervised segmentation networks.