인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Despite the skin microbiome has been linked to skin health and diseases, its role in modulating human skin appearance remains understudied. Using a total of 1244 face imaging phenomes and 246 cheek metagenomes, we first established three skin age indices by machine learning, including skin phenotype age (SPA), skin microbiota age (SMA), and skin integration age (SIA) as surrogates of phenotypic aging, microbial aging, and their combination, respectively. Moreover, we found that besides aging and gender as intrinsic factors, skin microbiome might also play a role in shaping skin imaging phenotypes (SIPs). Skin taxonomic and functional α diversity was positively linked to melanin, pore, pigment, and ultraviolet spot levels, but negatively linked to sebum, lightening, and porphyrin levels. Furthermore, certain species were correlated with specific SIPs, such as sebum and lightening levels negatively correlated with Corynebacterium matruchotii, Staphylococcus capitis, and Streptococcus sanguinis. Notably, we demonstrated skin microbial potential in predicting SIPs, among which the lightening level presented the least error of 1.8%. Lastly, we provided a reservoir of potential mechanisms through which skin microbiome adjusted the SIPs, including the modulation of pore, wrinkle, and sebum levels by cobalamin and heme synthesis pathways, predominantly driven by Cutibacterium acnes. This pioneering study unveils the paradigm for the hidden links between skin microbiome and skin imaging phenome, providing novel insights into how skin microbiome shapes skin appearance and its healthy aging.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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