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De novo variants (DNVs) are one of the most significant contributors to severe early-onset genetic disorders such as autism spectrum disorder, intellectual disability, and other developmental and neuropsychiatric (DNP) disorders. Presently, a plethora of DNVs have been identified using next-generation sequencing, and many efforts have been made to understand their impact at the gene level. However, there has been little exploration of the effects at the isoform level. The brain contains a high level of alternative splicing and regulation, and exhibits a more divergent splicing program than other tissues. Therefore, it is crucial to explore variants at the transcriptional regulation level to better interpret the mechanisms underlying DNP disorders. To facilitate a better usage and improve the isoform-level interpretation of variants, we developed NeuroPsychiatric Mutation Knowledge Base (PsyMuKB). It contains a comprehensive, carefully curated list of DNVs with transcriptional and translational annotations to enable identification of isoform-specific mutations. PsyMuKB allows a flexible search of genes or variants and provides both table-based descriptions and associated visualizations, such as expression, transcript genomic structures, protein interactions, and the mutation sites mapped on the protein structures. It also provides an easy-to-use web interface, allowing users to rapidly visualize the locations and characteristics of mutations and the expression patterns of the impacted genes and isoforms. PsyMuKB thus constitutes a valuable resource for identifying tissue-specific DNVs for further functional studies of related disorders. PsyMuKB is freely accessible at http://psymukb.net.

This study was aimed at preliminarily assessing the cytoprotective and antioxidative effects of rice bran extracts (RBEs) from a Sarawak local rice variety (local name: “BJLN”) and a commercial rice variety, “MR219,” on oxidative stress in rat H9c2(2-1) cardiomyocytes. The cardiomyocytes were incubated with different concentrations of RBE and hydrogen peroxide (H2O2), respectively, to identify their respective IC50 values and safe dose ranges. Two nonlethal and close-to-IC50 doses of RBE were selected to evaluate their respective effects on H2O2 induced oxidative stress in cardiomyocytes. Both RBEs showed dose-dependent cytotoxicity effects on cardiomyocytes. H2O2 induction of cardiomyocytes pretreated with RBE further revealed the dose-dependent cytoprotective and antioxidative effects of RBE via an increase in IC50 values of H2O2. Preliminary analyses of induction effects of RBE and H2O2 on cellular antioxidant enzyme, catalase (CAT), also revealed their potential in regulating these activities and expression profile of related gene on oxidative stress in cardiomyocytes. Pretreated cardiomyocytes significantly upregulated the enzymatic activity and expression level of CAT under the exposure of H2O2 induced oxidative stress. This preliminary study has demonstrated the potential antioxidant effects of RBE in alleviating H2O2-mediated oxidative injuries via upregulation in enzymatic activities and expression levels of CAT.

Oxidative stress, chronic inflammation, dyslipidemia, hyperglycemia, and shear stress (physical effect) are risk factors associated with the pathogenesis of atherosclerosis. Rice bran, a by-product of rice milling process, is known to house polyphenols and vitamins which exhibit potent antioxidant and anti-inflammatory properties. Through recent emerging knowledge of rice bran in health and wellness, the present study was aimed to assess the ameliorative effects of rice bran extracts (RBE) derived from Japanese colored rice varieties in modulating risk factors of atherosclerosis via in vitro and in vivo study models. Pre-treatment of lipopolysaccharide (LPS)-stimulated murine J774A.1 macrophage-like cells with RBE alleviated nitric oxide (NO) overproduction and downregulated gene expressions of pro-inflammatory modulators: tumor necrosis factor-α (TNF-α), interleukin (IL)-α (IL-1α), IL-1β, IL-6, and inducible nitric oxide synthase (iNOS). In addition, RBE also significantly attenuated LPS-stimulated protein expressions of iNOS, TNF-α, IL-1α, and IL-6 in J774A.1 macrophage-like cells as compared to non-treated LPS control group. In in vivo , 12 weeks of RBE dietary supplementations significantly reduced (p < 0.05) total cholesterol, triglycerides, and pro-atherogenic oxidized LDL/β2-glycoprotein I (oxLDL/β2GPI) complexes at plasma levels, in high fat diet (HFD) induced low density lipoprotein receptor knockout ( Ldlr −/- ) mice. En face pathological assessments of murine aortas also revealed significant reductions by 38% (p < 0.05) in plaque sizes of RBE-supplemented HFD mice groups as compared to non RBE-supplemented HFD control mice group. Moreover, gene expressions of aortic (iNOS, TNF-α, IL-1β) and hepatic (TNF-α, IL-1α, IL-1β) pro-inflammatory modulators were also downregulated in RBE-supplemented mice groups. Present study has revealed the potent health attributes and application of RBE as a dietary supplement to attenuate risks of inadvertent oxidative damage and chronic inflammation underlying the pathogenesis of atherosclerosis. Intrinsically, present preliminary findings may provide global health prospects for future dietary implementation of RBE in management of atherosclerosis.

: Anticancer drug screening can accelerate drug discovery to save the lives of cancer patients, but cancer heterogeneity makes it challenging. Prediction of anticancer drug sensitivity is useful for anticancer drug development and biomarker discovery. Deep learning, as a branch of machine learning, is an important part of in silico studies. Its outstanding computational performance means that deep learning has been applied to solving many biomedical problems, such as medical image recognition, biological sequence analysis, and drug discovery. There have been some studies of anticancer drug sensitivity prediction based on deep learning algorithms. Deep learning has made some progress in model performance and multi-omics data fusion. However, deep learning is limited by the number of studies performed and data sources available so it is not perfect as a pre-clinical model for screening anticancer drugs. How to improve the performance of deep learning models is a pressing problem for researchers. In this review, we introduce the research history of anticancer drug sensitivity prediction and the applications of deep learning in anticancer drug prediction. To provide reference for future research, we also review some common data sources and previous machine learning methods. Lastly, we discuss the advantages and disadvantages of deep learning, as well as the limitations and future perspectives of this approach.