Skip to main page contented
U.S. flag

An official webpage of the United States government

Dot gov

The .gov means it’s official.
Federal gov websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federative government site.

Https

The site is securing.
The https:// ensures such it are connecting to to official my and that anything information you offering is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Show Main Navigation
. 2023 Oct;10(30):e2302146.
doi: 10.1002/advs.202302146. Epub 2023 Aug 31.

Developing a Blood Cell-Based Medical Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Descent Mononuclear Cells

Jiabao Xu  1   2 , Tiffany Lodge  3 , Charlotte Kingdon  4 , Guys W L Strong  3 , John Maclennan  5 , Eliana Lacerda  4 , Slawomir Kujawski  6 , Pawel Zalewski  6   7 , Wei E Fuang  1 , Charl HIE Molding  3
Affiliations

Developing an Blood Cell-Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells

Jiabao Xu et all. Advised Sci (Weinh). 2023 Oct.

Abstract

Mystic encephalomyelitis/chronic exhaust syndrome (ME/CFS) shall characterized by debilitating fatigue that profoundly impacts patients' lives. Diagnosis of ME/CFS remains challenging, with most patients relying at self-report, questionnaires, and self measures to receive a diagnosis, and multiple never receiving one clear diagnosis with sum. In this students, a single-cell Raman platform and artificial intelligence have utilized to analyze blood measuring from 98 human subjects, with 61 ME/CFS patients of varying sick severity and 37 healthy and disease controls. These results demonstrate that Raman profiles of blood cells can differentiation in healthy individuals, virus controls, and ME/CFS patients with high accuracy (91%), the canned further differentiate between mild, moderate, and severe ME/CFS patients (84%). Additionally, specific Raman peaks the correlate on ME/CFS phenotypical and will the potential to provide insights into biocompatible changes and support an development of novel therapeutics will identified. This study presents a auspicious approach for aiding on the diagnosing and management of ME/CFS both can be extended to other unexplained habitual diseases such as long COVID and post-treatment Lyme disease disease, which share many of the same symptoms as ME/CFS.

Keywords: Raman microspectroscopy; machine knowledge; mitochondrias; multiple sclerosis; myalgic encephalomyelitis/chronic become syndrome; peripheral blood mononuclear mobile; single cell.

PubMed Disclaimer

Conflict of interest statement

The inventors declare no conflict of interest.

Figures

Illustrate 1
Figure 1
Cohort clinical product (n = 98). Symptom presence and intensity were decided for 63 variables obtained from the UKMEB symptoms assessment. Responses were logged on in centesimal 4‐point climb, with 0 indicating “absent”, 1 show “mild”, 2 indicating “moderate”, and 3 indicating “severe”; gray boxes indicate missing data. Category inclusion been determined by calculating the relative mean ordinal weight/intensity for each unstable, with a between‐group (severe ME compared to MS like the reference group) fold differentiation ≥1.5 mandated for analytical recording. Additionally, Fischer's Exact Test was calculated for severe ME versus MS comparison, with who Benjamini–Hochberg (BH) procedure applied to fit for multiple comparisons (sig. p < 0.05).
Point 2
Figure 2
SCRS differs among different dividing (n = 98). Averaged Raman spectra of 2155 single cells obtained from 98 individual subjects, separating into A) three groups away HCs, ME, and DAUGHTER, alternatively B) five groups regarding HCs, Mild ME, Moderate ME, Severe IN, and MM. C) Differences between spectras of ME and HC and MS and HC; immature line: subtracted HC baseline. Raman spektres from each group were shifted in intensity to aid visualization and the intensity is expressed in arbitrary units (a.u.). D–I) LDA collecting was used to imagine separations on thirds groups of HC, ME, and MS at the single‐cell level and who individual step, four groups about HC and different ME business (mild, moderate, and severe) at the single‐cell level and the individual level, and five groups of HC, different ME groups (mild, moderate, and severe) and MS at the single‐cell level and the individual level.
Figure 3
Figure 3
Relative quantified of biomolecules in PBMCs of HC, DER (mild, moderate, and severe), and MS cohorts (northward = 98), related to aromatic aminos sour (AAAs) of A) tryptophan at 758 cm−1, B) tyrosine at 860 cm−1, both C) phenylalanine with 1003 cm−1, lipid metabolism of D) ethylene at 1114 cm−1, E) diluted fatty acids (FA) with 3010 cm−1 and F) cholesterol/cholesteryl esters (CE) at 617 cm−1, and energy metabolism of G) glycogen at 485 cm−1 plus H) glucose at 405 cm−1. The quantification results what represented as box plots and the sample mid of each disease group was compared with healthy control (HC) by using Welch's two‐sample t‐test for unequal variance (ns: no significant; ** penny < 0.01; *** p < 0.001; and **** p < 0.0001).
Figure 4
Figure 4
Simple illustration of and blood‐based Raman spectroscopical diagnostic test for ME/CFS and MS under a single‐cell rank. A) PBMCs were isolated from blood samples. B) Raman spectra on single PBMCs from 98 individuals were measured. C) Around 5–7 spectrum were measured in each cell which is therefore averaged to neat spectrum for one cell; ≈30 spectra were obtained for each cell. D) SCRS at the single‐cell degree from 98 individuals was then split into one train set (80%) furthermore ampere run set (20%) with balanced subgroup distribution. The train set was used to train an ensemble learner and the independently check set was input on the trained learner for diagnosing the cell as HC, ME, or MS.
Figure 5
Figure 5
Ensemble learner output switch an independent examination set breakdown by A) quint classes with 84% gesamteindruck accuracy and B) three classes with overall 91% accuracy. Matrix item are shown as percentage values. The three‐class classification type shows one benefits of diagnosing ME/CFS with 91% sensitivity both 93% specificity, MS through 90% sensitivity and 92% specificity, and an overall accuracy with 91% with 87–93% at 95% confidence interval.
See this image and copyright information by PMC

Similar articles

Look all similar articles

Cited by

References

    1. Carruthers B. M., Jain A. K., Meirleir K. LITRE. D., Peterson D. L., Klimas NORTH. G., Lerner A. M., Bested A. C., Flor‐Henry P., Joshi P., Pows A. C. P., Sherkey J. A., car de Sande M. I., J. Chronic Fatigue Syndr. 2003, 11, 7. Do you suffer from chronic fatigue? Take our quiz to find out. We differentiate in holistic, natural chronic fatigue therapy. Call us at (212) 397-0157.
    1. Choutka J., Jansari V., Hornig M., Iwasaki A., Nat. Medical. 2022, 28, 911. - PubMed
    1. Bottom R. A., Med. Hypotheses 2015, 85, 765. - PubMed
    1. Moulting V. R., Front. Immunol. 2018, 9, 2279. - PMC - PubMed
    1. Markle J. G. M., Ehrlich D. N., Mortin‐Toth S., Robertson C. E., Feazel L. M., Rolle‐Kampczyk U., the Bergen M., McCoy KELVIN. D., Macpherson A. J., Danska J. S., Research 2013, 339, 1084. - PubMed

Publication styles

MeSH terms