The American journal of forensic medicine and pathology · 2016
Identifying Diffuse Axonal Injury by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight: A New Method for Pathological Observation.
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Ren Guanheng, Zou Donghua, Huang Ping, Li Zhengdong, Shao Yu, Deng Kaifei, Chen Yijiu, Liu Ningguo
Аннотация
PURPOSE: The aim of this study was to identify the diffuse axonal injury (DAI) of rat through screening out differentially expressed proteins which may represent potential biomarkers by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry (IMS), which could make a contribution for the research of forensic pathology.
METHODS: A total of 15 brainstem sections of rats (10 with and 5 without DAI) were conducted by MALDI-TOF IMS. Specific protein expression patterns were obtained for normal and DAI regions within the tissue sections. "Supervised Neural Network" algorithm was used to classify the DAI group from the normal group.
RESULTS: Applying a "Supervised Neural Network" algorithm, we were able to distinguish between normal and DAI regions with an overall cross-validation, a sensitivity and specificity of 95.67%, 99.34%, and 92.01%, respectively. Additionally, 4 distinctively overexpressed peaks were identified: 7059 and 1518 Da for DAI sections, and 5077 and 4327 Da for normal sections.
CONCLUSIONS: This study reveals the value of MALDI-TOF IMS to classify between normal and injured tissues and identify candidates for DAI biomarkers which will provide data to eventually understand the pathological mechanisms relevant to DAI, which is a great help in forensic pathology.
METHODS: A total of 15 brainstem sections of rats (10 with and 5 without DAI) were conducted by MALDI-TOF IMS. Specific protein expression patterns were obtained for normal and DAI regions within the tissue sections. "Supervised Neural Network" algorithm was used to classify the DAI group from the normal group.
RESULTS: Applying a "Supervised Neural Network" algorithm, we were able to distinguish between normal and DAI regions with an overall cross-validation, a sensitivity and specificity of 95.67%, 99.34%, and 92.01%, respectively. Additionally, 4 distinctively overexpressed peaks were identified: 7059 and 1518 Da for DAI sections, and 5077 and 4327 Da for normal sections.
CONCLUSIONS: This study reveals the value of MALDI-TOF IMS to classify between normal and injured tissues and identify candidates for DAI biomarkers which will provide data to eventually understand the pathological mechanisms relevant to DAI, which is a great help in forensic pathology.