4.6 Article

Diagnosis of Gulf War Illness Using Laser-Induced Spectra Acquired from Blood Samples

期刊

APPLIED SPECTROSCOPY
卷 76, 期 8, 页码 887-893

出版社

SAGE PUBLICATIONS INC
DOI: 10.1177/00037028211042049

关键词

Laser-induced breakdown spectroscopy; Gulf War illness; chemometrics; spectral analysis; classification algorithms; blood plasma; chronic low back pain; chronic multisymptom illness; diagnostic biomarker

资金

  1. NASA [NNX15AP84A]
  2. University of Massachusetts Lowell
  3. U.S. Department of Veterans Affairs, Clinical Sciences Research and Development Service [I01 CX000827]
  4. National Center for Complementary and Integrative Health, National Institutes of Health [K01AT004916]
  5. Office of the Assistant Secretary of Defense for Health Affairs, through the GulfWar Illness Research Program [W81XWH16-1-0528, W81XWH-09-2-0064, W81XWH-15-10695, W81XWH-14-1-0533]
  6. NASA [802304, NNX15AP84A] Funding Source: Federal RePORTER

向作者/读者索取更多资源

The study applied LIBS technology to blood plasma samples of GWI and non-GWI patients, successfully establishing a classification model through differential spectra method, achieving effective differentiation between GWI patients and non-GWI patients with good classification accuracy.
Gulf War illness (GWI) is a chronic illness with no known validated biomarkers that affects the lives of hundreds of thousands of people. As a result, there is an urgent need for the development of an untargeted and unbiased method to distinguish GWI patients from non-GWI patients. We report on the application of laser-induced breakdown spectroscopy (LIBS) to distinguish blood plasma samples from a group of subjects with GWI and from subjects with chronic low back pain as controls. We initially obtained LIBS data from blood plasma samples of four GWI patients and four non-GWI patients. We used an analytical method based on taking the difference between a mean LIBS spectrum obtained with those of GWI patients from the mean LIBS spectrum of those of the control group, to generate a difference spectrum for our classification model. This model was cross-validated using different numbers of differential LIBS emission peaks. A subset of 17 of the 82 atomic and ionic transitions that provided 70% of correct diagnosis was selected test in a blinded fashion using 10 additional samples and was found to yield 90% classification accuracy, 100% sensitivity, and 83.3% specificity. Of the 17 atomic and ionic transitions, eight could be assigned unambiguously to species of Na, K, and Fe.

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