Literature DB >> 30453190

Study on the pathological and biomedical characteristics of spinal cord injury by confocal Raman microspectral imaging.

Jie Li1, Zhuowen Liang2, Shuang Wang3, Zhe Wang2, Xu Zhang1, Xueyu Hu2, Kaige Wang4, Qingli He5, Jintao Bai4.   

Abstract

Confocal Raman microspectral imaging (CRMI) in combination with multivariate analysis was used to study pathological progression after spinal cord injury (SCI). By establishing moderate contusion in rat models, ex vivo longitudinal spinal cord tissue sections were prepared for microspectroscopic analysis. Comparative studies were then performed to determine the pathological distinctions among before injury (BI), one day post-injury (1 DPI), seven days post-injury (7 DPI), and 14 days post-injury (14 DPI) groups. Multivariate analysis algorithms, including K-mean cluster analysis (KCA) and principal component analysis (PCA), were conducted to highlight biochemical and structural variations after tissue damage. It is confirmed that typical spectral features and profiles can illustrate some fundamental and significant pathological processes post-injury, such as neuron apoptosis, hemorrhage, demyelination, and chondroitin sulfate proteoglycans (CSPGs) upregulation. Further, by establishing spectra-structure correlations, the reconstructed spectral images revealed some minute and important morphological characteristics following tissue injury, such as glial scar formation surrounding the cavity structure. The observed spectral phenomena also provide a detailed view on relevant pathobiological factors, which are involved in the spread of secondary damage after traumatic spinal cord injury. Our findings not only provide a spectral perspective to the well-known cellular mechanisms underlying SCI, but further provide a sound basis for developing real-time Raman methodologies to evaluate the prognostic factors and therapeutic results of SCI.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Confocal Raman microspectral imaging; Multivariate analysis algorithms; Pathological progression; Spinal cord injury

Mesh:

Substances:

Year:  2018        PMID: 30453190     DOI: 10.1016/j.saa.2018.11.022

Source DB:  PubMed          Journal:  Spectrochim Acta A Mol Biomol Spectrosc        ISSN: 1386-1425            Impact factor:   4.098


  2 in total

1.  Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework.

Authors:  Rong Yi; Lanying Tang; Yuqiu Tian; Jie Liu; Zhihui Wu
Journal:  Neural Comput Appl       Date:  2021-05-20       Impact factor: 5.606

2.  Confocal Raman Spectral Imaging Study of DAPT, a γ-secretase Inhibitor, Induced Physiological and Biochemical Reponses in Osteosarcoma Cells.

Authors:  Jie Li; Rui Wang; Jie Qin; Haishan Zeng; Kaige Wang; Qingli He; Difan Wang; Shuang Wang
Journal:  Int J Med Sci       Date:  2020-02-10       Impact factor: 3.738

  2 in total

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