A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes

Predicting phenotypes from genotypes is a fundamental task in quantitative genetics. With technological advances, it is now possible to measure multiple phenotypes in large samples. Multiple phenotypes can share their genetic component; therefore, modeling these phenotypes jointly may improve prediction accuracy by leveraging effects that are shared across phenotypes. However, effects can be shared across phenotypes in a variety of ways, so computationally efficient statistical methods are needed that can accurately and flexibly capture patterns of effect sharing. Here, we describe new Bayesian multivariate, multiple regression methods that, by using flexible priors, are able to model and adapt to different patterns of effect sharing and specificity across phenotypes. Simulation results show that these new methods are fast and improve prediction accuracy compared with existing methods in a wide range of settings where effects are shared. Further, in settings where effects are not shared, our methods still perform competitively with state-of-the-art methods. In real data analyses of expression data in the Genotype Tissue Expression (GTEx) project, our methods improve prediction performance on average for all tissues, with the greatest gains in tissues where effects are strongly shared, and in the tissues with smaller sample sizes. While we use gene expression prediction to illustrate our methods, the methods are generally applicable to any multi-phenotype applications, including prediction of polygenic scores and breeding values. Thus, our methods have the potential to provide improvements across fields and organisms

Trace element zinc and skin disorders

Zinc is a necessary trace element and an important constituent of proteins and other biological molecules. It has many biological functions, including antioxidant, skin and mucous membrane integrity maintenance, and the promotion of various enzymatic and transcriptional responses. The skin contains the third most zinc in the organism. Zinc deficiency can lead to a range of skin diseases. Except for acrodermatitis enteropathic, a rare genetic zinc deficiency, it has also been reported in other diseases. In recent years, zinc supplementation has been widely used for various skin conditions, including infectious diseases (viral warts, genital herpes, cutaneous leishmaniasis, leprosy), inflammatory diseases (hidradenitis suppurativa, acne vulgaris, rosacea, eczematous dermatitis, seborrheic dermatitis, psoriasis, Behcet's disease, oral lichen planus), pigmentary diseases (vitiligo, melasma), tumor-associated diseases (basal cell carcinoma), endocrine and metabolic diseases (necrolytic migratory erythema, necrolytic acral erythema), hair diseases (alopecia), and so on. We reviewed the literature on zinc application in dermatology to provide references for better use

Sentinel lymph node biopsy in oral cavity cancer using indocyanine green: A systematic review and meta-analysis

This meta-analysis was conducted to evaluate the value of indocyanine green (ICG) in guiding sentinel lymph node biopsy (SLNB) for patients with oral cavity cancer. An electronic database search (PubMed, MEDLINE, Cochrane Library, Embase, and Web of Science) was performed from their inception to June 2020 to retrieve clinical studies of ICG applied to SLNB for oral cavity cancer. Data were extracted from 14 relevant articles (226 patients), and 9 studies (134 patients) were finally included in the meta-analysis according to the inclusion and exclusion criteria. The pooled sentinel lymph node (SLN) sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 88.0% (95% confidence interval [CI], 74.0-96.0), 64.0% (95% CI, 61.0-66.0), 2.45 (95% CI, 1.31-4.60), 0.40 (95% CI, 0.17-0.90), and 7.30 (95% CI, 1.74-30.68), respectively. The area under the summary receiver operating characteristic curve was 0.8805. In conclusion, ICG applied to SLNB can effectively predict the status of regional lymph nodes in oral cavity cancer

Do Bird Assemblages Predict Susceptibility by E-Waste Pollution? A Comparative Study Based on Species- and Guild-Dependent Responses in China Agroecosystems

Indirect effects of electronic waste (e-waste) have been proposed as a causal factor in the decline of bird populations, but analyses of the severity impacts on community assembly are currently lacking. To explore how population abundance/species diversity are influenced, and which functional traits are important in determining e-waste susceptibility, here we surveyed breeding and overwintering birds with a hierarchically nested sampling design, and used linear mixed models to analyze changes in bird assemblages along an exposure gradient in South China. Total bird abundance and species diversity decreased with e-waste severity (exposed < surrounding < reference), reflecting the decreasing discharge and consequent side effects. Twenty-five breeding species exclusively used natural farmland, and nine species decreased significantly in relative abundance at e-waste polluted sites. A high pairwise similarity between exposed and surrounding sites indicates a diffuse effect of pollutants on the species assembly at local scale. We show that sensitivity to e-waste severity varies substantially across functional guild, with the prevalence of woodland insectivorous and grassland specialists declining, while some open farmland generalists such as arboreal frugivores, and terrestrial granivores were also rare. By contrast, the response of waterbirds, omnivorous and non-breeding visitors seem to be tolerable to a wide range of pollution so far. These findings underscore that improper e-waste dismantling results in a severe decline of bird diversity, and the different bird assemblages on polluted and natural farmlands imply species- and guild-dependent susceptibility with functional traits. Moreover, a better understanding of the impact of e-waste with different pollution levels, combined multiple pollutants, and in a food-web context on bird is required in future

Inherited Cardiomyopathies: Genetics and Clinical Genetic Testing

Inherited cardiomyopathies are major causes of morbidity and mortality and include a group of cardiac disorders such as hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy, arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C), left ventricular noncompaction (LVNC), and restrictive cardiomyopathy (RCM). These diseases have a substantial genetic component and predispose to sudden cardiac death. Since the first gene was identified as a disease-causing gene for HCM over two decades ago, more than eighty genes have been identified to be associated with inherited cardiomyopathies and genetic testing has become prevalent in making clinical diagnosis. With the advent of next-generation sequencing technology, genetic panel testing of inherited cardiomyopathies has become feasible and cost efficient. In this review, we summarize the individual cardiomyopathies with the emphasis on cardiomyopathy genetics and genetic testing

Bioinformatics and systems biology approach to identify the pathogenetic link of neurological pain and major depressive disorder

Neurological pain (NP) is always accompanied by symptoms of depression, which seriously affects physical and mental health. In this study, we identified the common hub genes (Co-hub genes) and related immune cells of NP and major depressive disorder (MDD) to determine whether they have common pathological and molecular mechanisms. NP and MDD expression data was downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (Co-DEGs) for NP and MDD were extracted and the hub genes and hub nodes were mined. Co-DEGs, hub genes, and hub nodes were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Finally, the hub nodes, and genes were analyzed to obtain Co-hub genes. We plotted Receiver operating characteristic (ROC) curves to evaluate the diagnostic impact of the Co-hub genes on MDD and NP. We also identified the immune-infiltrating cell component by ssGSEA and analyzed the relationship. For the GO and KEGG enrichment analyses, 93 Co-DEGs were associated with biological processes (BP), such as fibrinolysis, cell composition (CC), such as tertiary granules, and pathways, such as complement, and coagulation cascades. A differential gene expression analysis revealed significant differences between the Co-hub genes ANGPT2, MMP9, PLAU, and TIMP2. There was some accuracy in the diagnosis of NP based on the expression of ANGPT2 and MMP9. Analysis of differences in the immune cell components indicated an abundance of activated dendritic cells, effector memory CD8+ T cells, memory B cells, and regulatory T cells in both groups, which were statistically significant. In summary, we identified 6 Co-hub genes and 4 immune cell types related to NP and MDD. Further studies are needed to determine the role of these genes and immune cells as potential diagnostic markers or therapeutic targets in NP and MDD