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

机器学习识别非侵入性机械测试方法来预测皮肤成分、微观结构和衰老进程

• 批准号: 2750253
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2750253
• 关键词: Machine; learning; identify; non; invasive

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.

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。将从白色高加索背景中招募年龄为20-30岁、45-55岁和70-80岁的健康男性志愿者。将对光防护和光暴露皮肤部位进行表征。将对所有志愿者进行无创体内测量（机械和摩擦响应、表面几何形状、实时成像以确定载荷下的组织变形和ATR-FTIR以确定皮肤表面特征[脂质和水分]）。将从每个年龄组的志愿者亚群中进行活检采集和检测。将通过常规质谱法和肽位置指纹法评估皮肤蛋白质组成和损伤（活检1）。将使用胶体探针显微摩擦测定法（[GTR]实时力，压痕和剪切载荷下的原位显微镜检查）、表面特征（使用共聚焦显微镜[Olympus]的微观几何结构，使用FTIR [Perkin Elmer]的表面脂质和水分）和结构特征（表皮和真皮厚度、DEJ形状、初级胶原蛋白和弹性蛋白方向）评估皮肤机械和相互作用反应（活检2）。组合数据集的机器学习算法和多变量（主成分）分析旨在识别非侵入性测量与皮肤结构/组成之间的预测联系。