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).

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.

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.

Common innovation performance measures, which are based on research and development activities, are not relevant to dominant innovation behaviour of developing countries. The main functions of the innovation system of developing countries are capture, imitation, learning by doing and diffusion of knowledge, to reduce technology gaps with technology frontiers. Hence, the purpose of measuring innovation performance in a developing geographical area should be the evaluation on its success in technological learning and catching-up. Using the learning and technology gap concepts, in this paper we develop a novel model of measuring innovation performance of developing countries/regions, based on common regional statistics. In the virtual absence of mandatory firm level surveys, this model can provide a useful method to compare the innovation behaviour of regions for policymaker. Running the model with the data from the Iranian provinces, the paper concludes each province has its own specific innovation behaviour stemming from different historical development paths and geographical characteristics.

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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Background Behçet's disease (BD) is a chronic multi-systemic vasculitis with a considerable prevalence in Asian countries. There are many genes associated with a higher risk of developing BD, one of which is endoplasmic reticulum aminopeptidase-1 (ERAP1). In this study, we aimed to investigate the interactions of ERAP1 single nucleotide polymorphisms (SNPs) using a novel data mining method called Model-based multifactor dimensionality reduction (MB-MDR). Methods We have included 748 BD patients and 776 healthy controls. A peripheral blood sample was collected, and eleven SNPs were assessed. Furthermore, we have applied the MB-MDR method to evaluate the interactions of ERAP1 gene polymorphisms. Results The TT genotype of rs1065407 had a synergistic effect on BD susceptibility, considering the significant main effect. In the second order of interactions, CC genotype of rs2287987 and GG genotype of rs1065407 had the most prominent synergistic effect (β = 12.74). The mentioned genotypes also had significant interactions with CC genotype of rs26653 and TT genotype of rs30187 in the third-order (β = 12.74 and β = 12.73, respectively). Conclusion To the best of our knowledge, this is the first study investigating the interaction of a particular gene's SNPs in BD patients by applying a novel data mining method. However, future studies investigating the interactions of various genes could clarify this issue.

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