https://pubs.rsc.org/en/content/articlehtml/2019/an/c8an01437j
Conclusions
This study is to evaluate the feasibility of single-cell Raman analysis for the detection of biomarkers related to mitochondrial dysfunction and CFS. Accordingly, we identified that the aromatic amino acid, phenylalanine, has an elevated intracellular concentration and can be used as a potential biomarker in ρ0 cells lacking mitochondrial DNA, as well as in the peripheral blood mononuclear cells of CFS patients. Moreover, a machine learning model achieved an accuracy of 98.1% correctly classifying patients and controls based on their Raman spectra. The combination of Raman biomarkers and classification models might lead to improvements in our understanding of CFS pathogenesis and have the potential to be used as a diagnostic tool of CFS.
Conclusions
This study is to evaluate the feasibility of single-cell Raman analysis for the detection of biomarkers related to mitochondrial dysfunction and CFS. Accordingly, we identified that the aromatic amino acid, phenylalanine, has an elevated intracellular concentration and can be used as a potential biomarker in ρ0 cells lacking mitochondrial DNA, as well as in the peripheral blood mononuclear cells of CFS patients. Moreover, a machine learning model achieved an accuracy of 98.1% correctly classifying patients and controls based on their Raman spectra. The combination of Raman biomarkers and classification models might lead to improvements in our understanding of CFS pathogenesis and have the potential to be used as a diagnostic tool of CFS.
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