Atopic dermatitis (AD) is a chronic, pruritic skin disease characterized by inflammation and skin lesion cornification. While the use of corticosteroids like dexamethasone (DXM), an antiinflammatory drug, improves symptoms temporarily and quickly, this use is not a cure. Thus, we aimed to identify a new therapeutic strategy for AD using quantum molecular resonance (QMR), a novel non-invasive technique with an electromagnetic field-based therapeutic approach as an alternative to pain killers. An AD mouse model presenting AD-like skin lesions was generated by treating BALB/c mice with dinitrochlorobenzene (DNCB), and then DNCB-induced AD mice were administered DXM or QMR, and the change of AD-like skin lesions was observed. QMR ameliorated AD-like skin lesions in DNCB-induced AD mice and reduced the numbers of infiltrated mast cells and macrophages in mouse skin. QMR also alleviated thickening of the epidermis and restored integrity of the epidermal basement membrane. Several genes regulated by DNCB and counterregulated by QMR were identified through transcriptome analysis in mouse skin, and RNA silencing experiments on these genes in TNF-α/IFN-γ- or DNCB-treated human keratinocytes revealed that IL36G and SPRR2B play important roles in inflammation and keratinization. The expression of IL36G and SPRR2B was significantly reduced by QMR in skin of DNCB-induced AD mice. These results underscore the promising role of QMR in ameliorating AD characterized by inflammation and skin lesion hyperkeratosis via targeting IL36G and SPRR2B.
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BMB Rep
January 2025
Department of Pharmacology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; Department of Medical Sciences, Graduate School, The Catholic University of Korea, Seoul 06591, Korea; Institute for Aging and Metabolic Diseases, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
Biochem Genet
December 2024
Department of CSE, Meghnad Saha Institute of Technology, Behind Urbana Complex Near Ruby General Hospital, Anandapur Rd, Uchhepota, Kolkata, West Bengal, 700150, India.
Identifying the set of genes collectively responsible for causing a disease from differential gene expression data is called gene selection problem. Though many complex methodologies have been applied to solve gene selection, formulated as an optimization problem, this study introduces a new simple, efficient, and biologically plausible solution procedure where the collective power of the targeted gene set to discriminate between diseased and normal gene expression profiles was focused. It uses Simulated Annealing to solve the underlying optimization problem and termed here as Differential Gene Expression Based Simulated Annealing (DGESA).
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