Assessing the severity of psoriasis through multivariate analysis of optical images from non-lesional skin

通过对非皮损皮肤光学图像进行多变量分析来评估银屑病的严重程度

阅读:1

Abstract

Patients with psoriasis represent a heterogeneous population with individualized disease expression. Psoriasis can be monitored through gold standard histopathology of biopsy specimens that are painful and permanently scar. A common associated measure is the use of non-invasive assessment of the Psoriasis Area and Severity Index (PASI) or similarly derived clinical assessment based scores. However, heterogeneous manifestations of the disease lead to specific PASI scores being poorly reproducible and not easily associated with clinical severity, complicating the efforts to monitor the disease. To address this issue, we developed a methodology for non-invasive automated assessment of the severity of psoriasis using optical imaging. Our analysis shows that two-photon fluorescence lifetime imaging permits the identification of biomarkers present in both lesional and non-lesional skin that correlate with psoriasis severity. This ability to measure changes in lesional and healthy-appearing skin provides a new pathway for independent monitoring of both the localized and systemic effects of the disease. Non-invasive optical imaging was conducted on lesions and non-lesional (pseudo-control) skin of 33 subjects diagnosed with psoriasis, lesional skin of 7 subjects diagnosed with eczema, and healthy skin of 18 control subjects. Statistical feature extraction was combined with principal component analysis to analyze pairs of two-photon fluorescence lifetime images of stratum basale and stratum granulosum layers of skin. We found that psoriasis is associated with biochemical and structural changes in non-lesional skin that can be assessed using clinically available two-photon fluorescence lifetime microscopy systems.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。