Skin-resident regulatory T cells (T _reg cells) cells play a critical role in subcutaneous allergen-specific immunotherapy (SIT) for atopic dermatitis (AD). However, a detailed description of the phenotype and origin of skin-resident T _reg cells during SIT is lacking. Therefore, we investigated the role and origin of specific T _reg lineages in SIT with human AD samples and with a mouse model of AD. In the blood of patients with AD who responded to SIT, T _reg cells showed a notable increase in retinoic acid–related orphan receptor γt (RORγt) expression. Moreover, RORγt-expressing T _reg cells expanded in the skin of patients with AD upon SIT. In a mouse model of AD, the absence of RORγt expression in skin T _reg cells led to reduced therapeutic efficacy with SIT. In addition, SIT-induced RORγt ⁺ T _reg cells originated from peripheral tissue rather than from the thymus. Using two-photon microscopy and a parabiosis mouse model, we observed the migration and accumulation of skin-resident RORγt ⁺ T _reg cells in response to the SIT-induced immune response. These findings indicate that SIT induces peripheral RORγt ⁺ T _reg cell expansion, which contributes to the therapeutic efficacy of SIT.

One of the limits of PET imaging is the low spatial resolution due to a predetermined detector width. To overcome this limit, we may increase the number of samples by using the wobbling motion. Since the line spread function (LSF) of the sinogram is determined by the detector width, however, the increase of the number of samples is not sufficient to improve the sinogram resolution. In this paper, based on oversampled data obtained from the wobbling motion, we propose a novel and efficient super-resolution (SR) scheme for the sinogram. Since the proposed SR scheme adopts the penalized expectation maximization (EM) algorithm, it guarantees non-negative values of the super-resolved sinogram data. Through the experiments, we demonstrate that the proposed SR scheme can noticeably improve the spatial image resolution.

Sensitive skin is characterized by hypersensitivity to normal stimuli, and objective diagnostic tools and treatments are still limited. Currently, cosmetics for sensitive skin are developed through the exclusion of known irritants rather than investigation into the underlying mechanisms of sensitivity. In this study, we developed an integrated pipeline combining transcriptome analysis via microneedle-based skin sampling (MISSM), bioinformatics, in vitro validation, and clinical assessment to identify sensitive skin-associated inflammatory biomarkers and cosmetic ingredients that regulate them. Candidate biomarkers and matched cosmetic ingredients were identified from transcriptomic data and validated in lactic acid-stimulated HaCaT and human dermal fibroblasts via qRT-PCR. A prototype emulsion was developed and evaluated in a 4-week open-label pilot clinical trial with longitudinal molecular monitoring via MISSM. After lactic acid stimulation, sensitive skin-associated biomarkers (MCOLN1, CYR61, PMAIP1, PTGS2, and HMGB2) were significantly upregulated in both cell types, and cosmetic ingredients that regulate these biomarkers were confirmed in vitro. The emulsion prototype demonstrated hypoallergenicity in a primary irritation test. In the pilot clinical trial, target biomarker expression was significantly reduced in MISSM-derived samples, with improvements in skin hydration, barrier function, redness, and sensory reactivity also observed. This integrated pipeline will enable the discovery of inflammatory biomarker-regulating cosmetic ingredients, with potential applicability to various inflammatory skin conditions.