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The association of FKBP5 polymorphism with asthma susceptibility in asthmatic patients
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Abstract<sec id="j_jbcpp-2020-0450_abs_001"> <title>Objectives

Inhaled corticosteroids are the most effective controllers of asthma, although asthmatics vary in their response. FKBP51 is a major component of the glucocorticoid receptor which regulates its responses to corticosteroids. Therefore, the present study aims to identify the role of FKBP5 gene polymorphism in asthma susceptibility and corticosteroid resistance.

Methods

DNA was extracted from the blood of 68 asthmatic and 40 control subjects. FKBP5 gene fragments were amplified by PCR and sequenced by the Sanger method. The sequencing results were aligned by mapping on the reference sequences of National center of Biotechnology Information (NCBI) and single nucleotide polymorphisms (SNPs) which were checked. Finally, the genotype, allele frequency and odds ratio (OR) were calculated.

Results

The FKBP5 fragment sequencing revealed the presence of rs1360780 and one novel SNP found in 17 samples taken from asthmatic patients as compared to db SNP data in the NCBI database. The FKBP5 variant (rs1360780) indicated that the allele frequency of risk allele T was 41.18% in patients and 20% in control group members p<0.001 and OR=2.8 when compared to a wild C allele frequency of 58.82% in patients and 64% in the control group members. The novel SNP FKBP5 was compared to the SNP database in the NCBI database in which wild T allele was substituted with G. The novel SNP was submitted to the ClinVar Submission Portal at NCBI with accession number: rs1581842283 and confirmed an asthma susceptibility risk factor with allele G frequency of 11.76% in asthmatics and 2.5% in the control group members (OR=5.2, p<0.05), as compared to a wild T allele frequency of 88.24% in asthmatics and 97.5% in the control group members.

Conclusions

The risk allele T of rs1360780 and the novel SNP rs1581842283 risk allele G predict asthma susceptibility but show no association with corticosteroid resistant.

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Publication Date
Tue Jan 14 2025
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South Eastern European Journal Of Public Health
Deep learning-based threat Intelligence system for IoT Network in Compliance With IEEE Standard
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The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

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Tue Jan 01 2019
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Thu Oct 31 2019
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AN ENHANCED EVOLUTIONARY ALGORITHM WITH LOCAL HEURISTIC APPROACH FOR DETECTING COMMUNITY IN COMPLEX NETWORKS
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Publication Date
Tue Oct 29 2019
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Journal Of Engineering
Demand Priority in a Power System With Wind Power Contribution Load Shedding Scheme Based
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