Asthma is a complex inherited disease. The study was undertaken to identify the association of RANTES promoter polymorphisms with atopy and asthma using family-based association tests (FBATs) and generation-specific case-control analyses. We identified 154 nuclear families (453 individuals) in whom we established RANTES promoter status using the RFLP-PCR method. Of the two known promoter polymorphisms À403G/A and À28C/G, only the former appeared with a clinically relevant frequency. A total of 61 families were eligible for assessment of transmission of the allele with asthma and atopy by the pedigree disequilibrium test (PDT). Overall, allele frequency for À403A was 38.3% and 84 of 89 (94.3%) alleles were transmitted with physician diagnosed asthma (PDA) (P ¼ 0.001). All 89 children with atopy received the mutant allele, which was more than expected following Mendelian Laws of transmission (P ¼ 0.0001). In 303 unrelated parents, significant associations of the mutant allele were for atopy with or without asthma (P ¼ 0.001). In 150 unrelated children, significant associations were for atopy alone (P ¼ 0.001) and asthma (P ¼ 0.001). No associations were found for bronchial hyperresponsiveness (BHR). The À403 G-A is transmitted with atopy and atopic asthma, although its contribution appears to relate more to atopy than asthma and BHR.

The COVID-19 pandemic has caused havoc all around the world. The causative agent of COVID-19 is the novel form of the coronavirus (CoV) named SARS-CoV-2, which results in immune system disruption, increased inflammation, and acute respiratory distress syndrome (ARDS). T cells have been important components of the immune system, which decide the fate of the COVID-19 disease. Recent studies have reported an important subset of T cells known as regulatory T cells (Tregs), which possess immunosuppressive and immunoregulatory properties and play a crucial role in the prognosis of COVID-19 disease. Recent studies have shown that COVID-19 patients have considerably fewer Tregs than the general population. Such a decrement may have an impact on COVID-19 patients in a number of ways, including diminishing the effect of inflammatory inhibition, creating an inequality in the Treg/Th17 percentage, and raising the chance of respiratory failure. Having fewer Tregs may enhance the likelihood of long COVID development in addition to contributing to the disease’s poor prognosis. Additionally, tissue-resident Tregs provide tissue repair in addition to immunosuppressive and immunoregulatory activities, which may aid in the recovery of COVID-19 patients. The severity of the illness is also linked to abnormalities in the Tregs’ phenotype, such as reduced expression of FoxP3 and other immunosuppressive cytokines, including IL-10 and TGF-beta. Hence, in this review, we summarize the immunosuppressive mechanisms and their possible roles in the prognosis of COVID-19 disease. Furthermore, the perturbations in Tregs have been associated with disease severity. The roles of Tregs are also explained in the long COVID. This review also discusses the potential therapeutic roles of Tregs in the management of patients with COVID-19.

BACKGROUND AND OBJECTIVES:Genomic scan analyses have suggested that the chemokine receptor cluster (CCR2, CCR3, CCR5 <300 kb span) on the short arm of chromosome 3 may contribute to susceptibility to HIV-1 infection and to the expression of a number of inflammatory diseases. Two single nucleotide polymorphisms (SNP) and a deletion in these chemokine receptors have also been found in case-control studies to be associated with susceptibility for asthma and related phenotypes. We extended these case-control studies by establishing whether these polymorphisms were in linkage and linkage disequilibrium with asthma and related phenotypes using linkage and haplotype analyses.METHODS:We genotyped 154 nuclear families identified through two child probands with physician-diagnosed asthma (453 unrelated individuals) including 303 unrelated parents and 150 unrelated children. Atopy was defined as a positive skin prick test (SPT 3 mm) to a panel of common inhaled allergens.RESULTS:From a panel of ten known SNPs, only three polymorphisms: –G190A in CCR2, –T51C in CCR3, and a 32 bp deletion in CCR5 were found to occur at clinically relevant frequencies. All 154 families were used for haplotype analysis but only 12 nuclear families were eligible for linkage analysis. Both analyses confirmed that the mutations were in linkage with asthma, but not with atopy.CONCLUSION:The chemokine receptor genes on 3p21.3 are significantly plausible candidate genes that can influence the expression of asthma. The previous association of the CCR5Δ32 deletion with protection from childhood asthma appears to be explained by linkage disequilibrium with the –G190A mutation in the CCR2 receptor gene.

As medical science and technology progress towards the era of “big data”, a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC’s diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.