New Insights into the Mechanisms of Chinese Herbal Products on Diabetes: A Focus on the “Bacteria-Mucosal Immunity-Inflammation-Diabetes” Axis

Diabetes, especially type 2, has been rapidly increasing all over the world. Although many drugs have been developed and used to treat diabetes, side effects and long-term efficacy are of great challenge. Therefore, natural health product and dietary supplements have been of increasing interest alternatively. In this regard, Chinese herbs and herbal products have been considered a rich resource of product development. Although increasing evidence has been produced from various scientific studies, the mechanisms of action are lacking. Here, we have proposed that many herbal monomers and formulae improve glucose homeostasis and diabetes through the BMID axis; B represents gut microbiota, M means mucosal immunity, I represents inflammation, and D represents diabetes. Chinese herbs have been traditionally used to treat diabetes, with minimal side and toxic effects. Here, we reviewed monomers such as berberine, ginsenoside, M. charantia extract, and curcumin and herbal formulae such as Gegen Qinlian Decoction, Danggui Liuhuang Decoction, and Huanglian Wendan Decoction. This review was intended to provide new perspectives and strategies for future diabetes research and product

Mendelian randomization analyses for the causal relationship between early age at first sexual intercourse, early age at first live birth, and postpartum depression in pregnant women

IntroductionThere are insufficient epidemiological studies on the impact of age at first sexual intercourse (AFS) and age at first live birth (AFB) on postpartum depression (PPD) in pregnant women, and the conclusions of these studies are inconsistent.MethodsWe performed a Mendelian randomization (MR) study to determine the causal relationship between AFS or AFB and the risk of PPD. The summary data were extracted from genome-wide association study (GWAS) summary datasets. We selected the instrumental variables according to the P value of exposure-related single nucleotide polymorphisms (P<5 ×10-9 for AFS and P<5 ×10-8 for AFB) and estimated the linkage disequilibrium using the clump parameter (10,000 kb, r2 < 0.001). Single nucleotide polymorphisms were considered instrumental variables that were significantly associated with exposure factors without linkage disequilibrium. The F-statistics of the instrumental variables should all be larger than 10. A random-effects model of IVW was constructed as the main method in our study.Results and discussionMR studies based on GWAS data revealed that both AFS (OR = 0.4, P <0.001) and AFB (OR = 0.38, P <0.001) were negatively correlated with the risk of PPD. Early AFS and early AFB should be studied as possible risk factors for PPD in the future. Public health departments should attach importance to sex education for young girls. The results of our TSMR should be verified by high-quality prospective epidemiological studies in the future

TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions

Recently, more and more people study online for the convenience of access to massive learning materials (e.g. test questions/notes), thus accurately understanding learning materials became a crucial issue, which is essential for many educational applications. Previous studies focus on using language models to represent the question data. However, test questions (TQ) are usually heterogeneous and multi-modal, e.g., some of them may only contain text, while others half contain images with information beyond their literal description. In this context, both supervised and unsupervised methods are difficult to learn a fused representation of questions. Meanwhile, this problem cannot be solved by conventional methods such as image caption, as the images may contain information complementary rather than duplicate to the text. In this paper, we first improve previous text-only representation with a two-stage unsupervised instance level contrastive based pre-training method (MCL: Mixture Unsupervised Contrastive Learning). Then, TQ-Net was proposed to fuse the content of images to the representation of heterogeneous data. Finally, supervised contrastive learning was conducted on relevance prediction-related downstream tasks, which helped the model to learn the representation of questions effectively. We conducted extensive experiments on question-based tasks on large-scale, real-world datasets, which demonstrated the effectiveness of TQ-Net and improve the precision of downstream applications (e.g. similar questions +2.02% and knowledge point prediction +7.20%). Our code will be available, and we will open-source a subset of our data to promote the development of relative studies.Comment: This paper has been accepted for the AAAI2023 AI4Edu Worksho