Huang Lynn (uncleblood86)

Candida africana is a pathogenic species within the Candida albicans species complex. Due to the limited knowledge concerning its prevalence and antifungal susceptibility profiles, a comprehensive study is overdue. Accordingly, we performed a search of the electronic databases for literature published in the English language between 1 January 2001 and 21 March 2020. Citations were screened, relevant articles were identified, and data were extracted to determine overall intra-C. albicans complex prevalence, geographical distribution, and antifungal susceptibility profiles for C. africana. From a total of 366 articles, 41 were eligible for inclusion in this study. Our results showed that C. africana has a worldwide distribution. The pooled intra-C. albicans complex prevalence of C. africana was 1.67% (95% CI 0.98-2.49). Prevalence data were available for 11 countries from 4 continents. Iran (3.02%, 95%CI 1.51-4.92) and Honduras (3.03%, 95% CI 0.83-10.39) had the highest values and Malaysia (0%) had the lowest prevalence. Vaginal specimens were the most common source of C. africana (92.81%; 155 out of 167 isolates with available data). However, this species has also been isolated from cases of balanitis, from patients with oral lesions, and from respiratory, urine, and cutaneous samples. Data concerning the susceptibility of C. africana to 16 antifungal drugs were available in the literature. Generally, the minimum inhibitory concentrations of antifungal drugs against this species were low. In conclusion, C. africana demonstrates geographical variation in prevalence and high susceptibility to antifungal drugs. However, due to the relative scarcity of existing data concerning this species, further studies will be required to establish more firm conclusions.The majority of genome-wide association studies (GWAS) loci are not annotated to known genes in the human genome, which renders biological interpretations difficult. Transcriptome-wide association studies (TWAS) associate complex traits with genotype-based prediction of gene expression deriving from expression quantitative loci(eQTL) studies, thus improving the interpretability of GWAS findings. However, these results can sometimes suffer from a high false positive rate, because predicted expression of different genes may be highly correlated due to linkage disequilibrium between eQTL. We propose a novel statistical method, Gene Score Regression (GSR), to detect causal gene sets for complex traits while accounting for gene-to-gene correlations. We consider non-causal genes that are highly correlated with the causal genes will also exhibit a high marginal association with the complex trait. Consequently, by regressing on the marginal associations of complex traits with the sum of the gene-to-gene correlations in each gene set, we can assess the amount of variance of the complex traits explained by the predicted expression of the genes in each gene set and identify plausible causal gene sets. GSR can operate either on GWAS summary statistics or observed gene expression. Therefore, it may be widely applied to annotate GWAS results and identify the underlying biological pathways. We demonstrate the high accuracy and computational efficiency of GSR compared to state-of-the-art methods through simulations and real data applications. GSR is openly available at https//github.com/li-lab-mcgill/GSR.[This corrects the article DOI 10.1371/journal.pone.0231362.]. In the developed world, cardiovascular diseases still contribute to mortality and morbidity, leading to significantly increased deaths in recent years. Thus, it is necessary for a layperson to provide the best possible basic life support (BLS) until professional help is available. Since information on current BLS knowledge in Germany is not available, but necessary to be able to make targeted improvements in BLS education, we conducted this study. A cohort survey using convenience sampling (non-probability) metho