Machine learning to identify non-invasive mechanical testing approaches to predict skin composition, micro-structure and progression of ageing

该项目旨在使用机器学习算法在皮肤的结构和组成与非侵入性机械测试之间建立预测联系。年龄在20-30岁、45-55岁和70-80岁的健康男性志愿者将从白种人背景中招募。照片保护和照片暴露的皮肤部位将被描述。将对所有志愿者进行非侵入性体内测量（机械和摩擦响应，表面几何形状，实时成像以表征组织在载荷下的变形，ATR-FTIR以表征皮肤表面特征[脂质和水分]）。将从每个年龄组的志愿者亚群中进行活检收集和检测。皮肤蛋白质组成和损伤（活检1）将通过常规质谱法和肽定位指纹法评估。皮肤力学和相互作用反应（活组织检查2）将使用胶体探针微摩擦测定法（[GTR]实时力，压痕和剪切载荷下的原位显微镜），表面特征（使用共聚焦显微镜的微观几何[Olympus]，使用FTIR的表面脂质和水分[Perkin Elmer]）和结构特征（表皮和真皮厚度，DEJ形状，原始胶原蛋白和弹性蛋白方向）进行评估。机器学习算法和组合数据集的多变量（主成分）分析将旨在确定非侵入性测量与皮肤结构/成分之间的预测联系。

This project aims to use machine learning algorithms to establish a predictive link between the structure and composition of the skin and non-invasive mechanical tests. Healthy male volunteers aged 20-30, 45-55 and 70-80 will be recruited from White Caucasian backgrounds. Photo-protected and photo-exposed skin sites will be characterised. Non-invasive in vivo measurements will be made for all volunteers (mechanical and friction response, surface geometry, real-time imaging to characterise tissue deformation under loading and ATR-FTIR to characterise skin surface characteristics [lipids and moisture]). Biopsy collection and testing will be undertaken from a sub population of volunteers from each age group. Skin protein composition and damage (biopsy 1) will be assessed by conventional mass spectrometry and peptide location fingerprinting. Skin mechanical and interaction response (biopsy 2) will be assessed using colloidal-probe micro-tribometry ([GTR] real-time force, in-situ microscopy under indentation and shear loading), surface characteristics (microgeometry using confocal microscopy [Olympus], surface lipids and moisture using FTIR [Perkin Elmer]) and structural characteristics (epidermal and dermal thickness, DEJ shape, primary collagen and elastin orientations). Machine learning algorithms and multivariate (principal component) analysis of the combined data sets will aim to identify a predictive link between non-invasive measurements and skin structure/composition.