98.1% Accuracy in CFS Testing

Belbyr

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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.
 
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Wishful

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If this is replicable, it could be a major breakthrough. I can't understand the paper well enough to judge it. I'll wait for the response from experts.
 

Wishful

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That's why I can't judge whether the high accuracy is just an artifact of how the experiment was carried out. It seems like a complex set of operations, so maybe the computer is accurately identifying cellular response to storage fluid or equipment cleaning fluid. I'll let someone else replicate the experiment, and then replicate it with changes.
 
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I had a look at the paper. A larger sample will definitely be needed (i think this is also stated in the paper) but it is a good thing to see that the technology is being put to use by more researchers now.

As Ron Davis stated, we need novel ways to generate, collect and analyse information. I cannot think of a better way to achieve this other than using advanced analytical methods such as machine learning.
 

roller

wiggle jiggle
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it is a good thing to see that the technology is being put to use by more researchers now.
im wondering... is it using more technology, or was there indeed a different type of researchers at work ?

Jiabao Xu
a, Michelle Potter b, Cara Tomas c, Joanna L. Elson c, Karl J. Morten
b, Joanna Poulton
b, Ning Wang d, Hanqing Jin d, Zhaoxu Hou d and Wei E. Huang
*a

a Department of Engineering Science, University of Oxford, Begbroke Science Park, Woodstock Road, Oxford, OX5 1PF, UK. E-mail: wei.huang@eng.ox.ac.uk; Fax: +44 (0)18653749; Tel: +44 (0)1865 283786

b Nuffield Department of Women's and Reproductive Health, University of Oxford, the Women Centre, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK

c Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK

d Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK