Estimation value of plasma amino acid target analysis to the acute radiation injury early triage in the rat model

被引:22
作者
Tang, Xinxing [1 ]
Zheng, Mingchen [2 ]
Zhang, Yuanyuan [1 ]
Fan, Saijun [1 ]
Wang, Chang [1 ]
机构
[1] Soochow Univ, Sch Radiat Med & Protect, Sch Radiol & Interdisciplinary Sci RAD X, Jiangsu Prov Key Lab Radiat Med & Protect,Med Col, Suzhou 215123, Peoples R China
[2] Soochow Univ, Sch Math Sci, Suzhou 215123, Peoples R China
基金
中国国家自然科学基金;
关键词
Radiation biodosimetry; Triage; Radiation metabolomics; Amino acids; HPLC-MS; PARTIAL LEAST-SQUARES; CHROMATOGRAPHY-MASS SPECTROMETRY; NUCLEAR-MAGNETIC-RESONANCE; SMALL-BOWEL; BIOLOGICAL DOSIMETRY; BIOMARKER DISCOVERY; EPITHELIAL DAMAGE; METABOLOMICS; GENE; BIODOSIMETRY;
D O I
10.1007/s11306-013-0502-4
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Rapid radiation injury early triage for large-scale people after radiation exposure is vital for limited medical resources allocation and early treatment of a large number of wounded after a nuclear accident. Owing to the high-throughput analysis and minimally invasive of collection sample, radiation metabolomics has been recently applied to radiation damage researches. Here, exploration the feasibility of estimating the acute radiation injury early triage by means of plasma amino acid target analysis was attempted using high performance liquid chromatography-electrospray tandem mass spectrometry (HPLC-ESI-MS/MS) technique. The nonlinear kernel partial least squares (KPLS) model was used to classify the radiation damage levels. The classification accuracy of without radiation exposure was 92.3 % at 5 h after exposure. At 24 h after exposure, the triage accuracies were all above 83 % in the different doses of irradiated groups, the correct classification rates of moderate and severe radiation injury were 91.7 and 92.3 % respectively. At 72 h after exposure, the classification accuracies of all levels of radiation injury were more than 90 %, the correct classification rates of moderate and severe groups were up to 100 %. This approach is useful for early predicting different levels of radiation exposure and for developing metabolomics strategies for radiation biodosimetry in humans, but need more data to consummate.
引用
收藏
页码:853 / 863
页数:11
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