Jin-Muk Lim

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.

The clinical spectrum of Alzheimer’s disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been developed, current therapies can only delay disease progression rather than halt it entirely. Therefore, the early detection of MCI and the identification of MCI patients at high risk of progression to AD remain urgent unmet needs in the super-aged era. This study utilized transcriptomics data from cognitively unimpaired (CU) individuals, MCI, and AD patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and leveraged machine learning models to identify biomarkers that differentiate MCI from CU and also distinguish AD from MCI individuals. Furthermore, Cox proportional hazards analysis was conducted to identify biomarkers predictive of the progression from MCI to AD. Our machine learning models identified a unique set of gene expression profiles capable of achieving an area under the curve (AUC) of 0.98 in distinguishing those with MCI from CU individuals. A subset of these biomarkers was also found to be significantly associated with the risk of progression from MCI to AD. A linear mixed model demonstrated that plasma tau phosphorylated at threonine 181 (pTau181) and neurofilament light chain (NFL) exhibit the prognostic value in predicting cognitive decline longitudinally. These findings underscore the potential of integrating machine learning (ML) with transcriptomic profiling in the early detection and prognostication of AD. This integrated approach could facilitate the development of novel diagnostic tools and therapeutic strategies aimed at delaying or preventing the onset of AD in at-risk individuals. Future studies should focus on validating these biomarkers in larger, independent cohorts and further investigating their roles in AD pathogenesis.