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Background Genetic predisposition to psoriasis, an inflammatory skin disease affecting 0.2 – 4% of world populations, is well established. Thus far, 41 psoriasis susceptibility loci reach genome-wide significance (p ≤ 5 × 10−8). Identification of genetic susceptibility loci in diverse populations will help understand the underlying biology of psoriasis susceptibility. Objectives The primary objective of this study is to examine psoriasis susceptibility associations previously reported in Chinese and Caucasian populations in a Pakistani cohort. Methods Blood samples and phenotype data were collected from psoriasis cases and controls in Islamabad, Pakistan. DNA was isolated and genotypes of selected susceptibility markers were determined. The data were analyzed by chi square tests or logistic regression for psoriasis association. Results HLA-Cw6 showed the strongest association (OR = 2.43, p = 2.3 × 10−12). HLA-Cw1 showed marginally significant association (OR = 1.66, p = 0.049), suggesting that the HLA-Cw1-B46 risk haplotype may be present in the Pakistani population. Three other loci (IL4/IL13, NOS2, TRAF3IP2) showed nominally significant association (p < 0.05). Conclusions HLA-Cw6 is strongly associated with psoriasis susceptibility in the Pakistani population, as has been found in every other population studied. In addition, HLA-Cw1 showed marginal association, reflecting the relative geographic proximity and thus likely genetic relatedness to other populations in which HLA-Cw1–B46 haplotype is known to be associated. A larger cohort and a denser marker set will be required for further analysis of psoriasis associations in the South Asian population.

Background Ankylosing spondylitis (AS) is a chronic rheumatological condition affecting sacroiliac joint and spine and occurs more often in younger patients than in the elderly population. Objective The purpose of the study was to determine the association of the neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) with the disease activity of AS.

Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

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