This study aimed to evaluate the primary symptoms, comorbidities, and outcomes of inpatients with confirmed reverse transcription-PCR (RT-PCR) for SARS-CoV-2 infection among 2077 suspected/diagnosed cases of COVID-19. Based on the results of Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, age, and suggestive chest X-ray (CXR) findings for SARS-CoV-2 infection, cardiovascular diseases, diabetes mellitus, chronic lung diseases, and intensive care units admission had significant associations with positive RT-PCR results for COVID-19 infection. Also, the highest area under the curve (AUC) was related to cough (AUC = 0.53, 95% CI: 0.51-0.56), dyspnea (AUC = 0.52, 95% CI: 0.50-0.54), and abnormal CXR (AUC = 0.52, 95% CI: 0.50-0.54), as significant predictors. This study showed that some symptoms including cough and dyspnea, as well as abnormal CXR, could be proper predictors of positive RT-PCR result for SARS-CoV-2 infection. It seems that patients with underlying disease(s), such as cardiovascular diseases, diabetes mellitus, and chronic lung diseases, had a higher probability to have positive RT-PCR for SARS-CoV-2 infection than those with no underlying disease(s).
Background Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease. Several risk factors such as smoking, air pollution, inhaled toxins, high body mass index and infectious agents are involved in the pathogenesis of IPF. In the present study, this meta-analysis study investigates the prevalence of viral and bacterial infections in the IPF patients and any possible association between these infections with pathogenesis of IPF. Methods The authors carried out this systematic literature review from different reliable databases such as PubMed, ISI Web of Science, Scopus and Google Scholar to December 2020.Keywords used were the following “Idiopathic pulmonary fibrosis”, “Infection”, “Bacterial Infection” and “Viral Infection”, alone or combined together with the Boolean operators "OR”, “AND” and “NOT” in the Title/Abstract/Keywords field. Pooled proportion and its 95% CI were used to assess the prevalence of viral and bacterial infections in the IPF patients. Results In this systematic review and meta-analyses, 32 studies were selected based on the exclusion/inclusion criteria. Geographical distribution of included studies was: eight studies in American people, 8; in European people, 15 in Asians, and one in Africans. The pooled prevalence for viral and bacterial infections w ere 53.72% (95% CI 38.1–69.1%) and 31.21% (95% CI 19.9–43.7%), respectively. The highest and lowest prevalence of viral infections was HSV (77.7% 95% CI 38.48–99.32%), EBV (72.02%, 95% CI 44.65–90.79%) and Influenza A (7.3%, 95% CI 2.66–42.45%), respectively. Whereas the highest and lowest prevalence in bacterial infections were related to Streptococcus sp. (99.49%, 95% CI 96.44–99.9%) and Raoultella (1.2%, 95% CI 0.2–3.08%), respectively. Conclusions The results of this review were confirmed that the presence of viral and bacterial infections are the risk factors in the pathogenesis of IPF. In further analyses, which have never been shown in the previous studies, we revealed the geographic variations in the association strengths and emphasized other methodological parameters (e.g., detection method). Also, our study supports the hypothesis that respiratory infection could play a key role in the pathogenesis of IP.
Inflammatory cytokines have been established to be involved in the pathogenesis of rheumatoid arthritis (RA). The genetic polymorphisms in the interleukin (IL) 23 receptor (IL23R), IL21, and IL17 have been associated with RA risk. However, there is no conclusive understanding of the genes encoding the immunoinflammatory IL‐21–IL‐23R–IL‐17A pathway in RA aetiopathogenesis. This meta‐analysis was conducted to attain this goal. A comprehensive literature search was conducted in Scopus and PubMed to look for the relevant case–control studies up until 2018. A Bayesian hierarchical meta‐analysis was carried out to assess the association between the polymorphisms and the risk of RA. The association was estimated by calculating the logarithm of odds ratio (Log OR) and 95% credible interval (95% CI). In this meta‐analysis, 37 case–control studies comprising 23,506 RA patients and 25,984 healthy individuals were found for analyzing the IL23R, IL21, and IL1A gene polymorphism and risk of RA. In the IL23R gene rs1343151 SNP, the minor A allele significantly increased the risk of RA (Log OR = 0.085, 95% CI = 0.008, 0.156). Moreover, the minor AA genotype was significantly associated with increased RA risk (Log OR = 0.176, 95% CI = 0.028, 0.321). In addition, the C allele of the IL23R gene rs2201841 SNP significantly decreased the disease risk (Log OR = −0.544, 95% CI = −1.0, −0.065). Since Bayesian meta‐analysis is a powerful strategy to pool the data, it can be mentioned that genetic polymorphisms of IL23R, but not IL21 and IL17A, are involved in susceptibility to RA.
In recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs’ subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy.
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