Heidarifard, Maryam, Moezzi, Atefeh, Dallaire, Frédérick et al. · International journal of molecular sciences · 2026 · DOI
Researchers used a laboratory technique called Raman spectroscopy combined with computer learning to identify chemical signatures in blood plasma that are associated with ME/CFS. They collected blood samples from 115 ME/CFS patients and 45 healthy controls both at rest and 90 minutes after a standardized stress test, and found the method could distinguish between the two groups with moderate-to-good accuracy (79–84%). However, this is an early-stage study and much more work is needed before such a test could be used in clinical practice.
ME/CFS currently lacks validated biomarkers for rapid diagnosis or disease monitoring. This study reports that spectroscopic and machine-learning methods can detect biochemical differences in plasma associated with ME/CFS status, which may eventually support objective diagnostic approaches and biomarker discovery efforts.
This case–control study does not establish causation or mechanistic pathways underlying PEM or cognitive dysfunction. It does not demonstrate clinical utility, prospective diagnostic validity, or whether the observed spectral differences are disease-specific rather than markers of general deconditioning. The study does not confirm that Raman spectroscopy should replace clinical diagnostic criteria, nor does it validate any therapeutic intervention.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Heidarifard, Maryam, Moezzi, Atefeh, Dallaire, Frédérick, Ember, Katherine, Elremaly, Wesam, Caraus, Iurie, et al. (2026). Raman Spectroscopy Combined with Machine Learning Reveals Myalgic Encephalomyelitis-Associated Biomolecular Signatures at Rest and After Standardized Stress.. International journal of molecular sciences. https://doi.org/10.3390/ijms27114937
BibTeX
@article{mecfsatlas-heidarifard-2026-raman-spectroscopy,
author = {Heidarifard, Maryam and Moezzi, Atefeh and Dallaire, Frédérick and Ember, Katherine and Elremaly, Wesam and Caraus, Iurie and Franco, Anita and Leblond, Frédéric and Moreau, Alain and Dehaes, Mathieu},
title = {Raman Spectroscopy Combined with Machine Learning Reveals Myalgic Encephalomyelitis-Associated Biomolecular Signatures at Rest and After Standardized Stress.},
journal = {International journal of molecular sciences},
year = {2026},
doi = {10.3390/ijms27114937},
note = {PubMed: 42278463},
url = {https://www.mecfsatlas.com/evidence/heidarifard-2026-raman-spectroscopy},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-06-14. https://www.mecfsatlas.com/evidence/heidarifard-2026-raman-spectroscopy
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