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Combining our results on sensitivity and positive predictive value (PPV), VarDict [PPV = 0.999 and Matthews correlation coefficient (MCC) = 0.832] and BCFtools (PPV = 0.999 and MCC = 0.813) perform best when using African population data on high and low coverage data. Overall, current VC tools produce high false positive and false negative rates when analysing African compared with European data. This highlights the need for development of VC approaches with high sensitivity and precision tailored for populations characterized by high genetic variations and low linkage disequilibrium.Insulin secretion from pancreatic beta cells is tightly regulated by glucose and paracrine signals within the microenvironment of islets of Langerhans. Extracellular matrix from islet microcapillary endothelial cells (IMEC) affect beta-cell spreading and amplify insulin secretion. This study was aimed at investigating the hypothesis that contact-independent paracrine signals generated from IMEC may also modulate beta-cell insulin secretory functions. For this purpose, conditioned medium (CMp) preparations were prepared from primary cultures of rat IMEC and were used to simulate contact-independent beta cell-endothelial cell communication. Glucose-stimulated insulin secretion (GSIS) assays were then performed on freshly isolated rat islets and the INS-1E insulinoma cell line, followed by fractionation of the CMp, mass spectroscopic identification of the factor, and characterization of the mechanism of action. The IMEC-derived CMp markedly attenuated first- and second-phase GSIS in a time- and dose-dependent manner without altering cellular insulin content and cell viability. Size exclusion fractionation, chromatographic and mass-spectroscopic analyses of the CMp identified the attenuating factor as the enzyme triosephosphate isomerase (TPI). An antibody against TPI abrogated the attenuating activity of the CMp while recombinant human TPI (hTPI) attenuated GSIS from beta cells. This effect was reversed in the presence of tolbutamide in the GSIS assay. In silico docking simulation identified regions on the TPI dimer that were important for potential interactions with the extracellular epitopes of the sulfonylurea receptor in the complex. This study supports the hypothesis that an effective paracrine interaction exists between IMEC and beta cells and modulates glucose-induced insulin secretion via TPI-sulfonylurea receptor-KATP channel (SUR1-Kir6.2) complex attenuating interactions.During mRNA translation, tRNAs are charged by aminoacyl-tRNA synthetases and subsequently used by ribosomes. A multi-enzyme aminoacyl-tRNA synthetase complex (MSC) has been proposed to increase protein synthesis efficiency by passing charged tRNAs to ribosomes. An alternative function is that the MSC repurposes specific synthetases that are released from the MSC upon cues for functions independent of translation. To explore this, we generated mammalian cells in which arginyl-tRNA synthetase and/or glutaminyl-tRNA synthetase were absent from the MSC. Protein synthesis, under a variety of stress conditions, was unchanged. Most strikingly, levels of charged tRNAArg and tRNAGln remained unchanged and no ribosome pausing was observed at codons for arginine and glutamine. Thus, increasing or regulating protein synthesis efficiency is not dependent on arginyl-tRNA synthetase and glutaminyl-tRNA synthetase in the MSC. Alternatively, and consistent with previously reported ex-translational roles requiring changes in synthetase cellular localizations, our manipulations of the MSC visibly changed localization.The coronavirus disease 2019 (COVID-19) pandemic in Japan is not as disastrous as it is in other Western countries, possibly because of certain lifestyle factors. One such factor might be the seaweed-rich diet commonly consumed in Japan. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which binds to angiotensin-converting enzyme 2 (ACE2) on the cell surface and downregulates ACE2, likely elevating the ratio of angiotensin-converting enzyme (ACE) to ACE2. The overreaction of the immune system, combined with the cytokine storm and ACE dominance, is purported to cause the condition of COVID-19 patients to deteriorate rapidly. Dietary seaweeds contain numerous components, including ACE inhibitory peptides, soluble dietary fibers (eg, fucoidan, porphyran), omega-3 fatty acids, fucoxanthin, fucosterol, vitamins D3 and B12, and phlorotannins. Streptozotocin order These components exert antioxidant, anti-inflammatory, and antiviral effects directly as well as indirectly through prebiotic effects. It is possible that ACE inhibitory components could minimize the ACE dominance caused by SARS-CoV-2 infection. Thus, dietary seaweeds might confer protection against COVID-19 through multiple mechanisms. Overconsumption of seaweeds should be avoided, however, as seaweeds contain high levels of iodine.The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.