In the United States, more than 3 million people have chronic fatigue syndrome (CFS), a complex condition characterized by extreme and persistent fatigue. Neither sleep nor rest were relieving their exhaustion, and among the many unknowns associated with SFC, is to know how to diagnose the condition with precision.
Scientists at Cornell University hope to change this. Research newly published in the journal evaluated by peers Proceedings of the National Academy of Sciences Describes a “concrete step” towards the development of a diagnostic test.
Diagnosis of chronic fatigue syndrome
Currently, there is no diagnostic tool for the SFC, also known as the myalgic encephalomyelitis. Instead, doctors are counting on a large set of patient symptoms – such as exhaustion, dizziness and brain fog – in tandem with long and arduous effort to exclude other potential causes from these conditions.
This key is maintained in RNA, a critical component of human cells which transports DNA instructions to other body proteins. When the cells die, they leave behind a genetic recording in RNA which is released in the blood circulation, revealing changes that occur during a lifetime. Several mechanisms contribute to the release of cell RNA in the blood circulation, including normal cell death, physical stress or cell cell communication.
“Once outside the cell, these circulating RNA molecules are called cell without cells (CFRNA). These CFRNA molecules reflect the dynamics of the expression of genes at the crucial moment of cell renewal or signaling which led to their release, which makes them ideal biomarkers for the study of complex diseases, “explains Anne Gardella, co-author and molecular biomarkers.
“By measuring RNA in our cells at a given moment, we understand which genes are actively expressed in response to the current cellular environment,” adds Gardella.
Learn more: What do your blood test results mean?
Automatic learning and CFRNA
The Gardella team has created machine learning models capable of punch through the CFRNA to identify biomarkers, or molecular fingerprints, associated with the CFS.
“Me / CFS affects many different parts of the body,” said Maureen Hanson, director of the Cornell Center for Enervatation Neuro-Immune, in a press release. “The nervous system, the immune system, [and] cardiovascular system. Plasma analysis gives you access to what is happening in these different parts. “”
The researchers collected blood samples in two groups participating in the study: those diagnosed with a SFC and a healthy but sedentary group. Because people with SFC generally have limited daily activity levels, comparing them to sedentary persons allowed to control the differences in physical activity.
“If we compare them to people with normal levels of activity, AFR alterations could reflect the differences in physical conditioning rather than real biological effects caused by the disease itself,” explains Gardella.
The sampled blood was turned using a centrifuge to separate and isolate its components. Then, the characteristics of RNA molecules were genetically sequenced to learn which genes in the body coded for the CFRNA.
“Essentially, these computer algorithms” learn “which genes best separate groups and can then classify new samples according to their CFRNA expression profiles,” explains Gardella.
Better diagnostic tools for the future
Researchers collected more than 700 RNA transcribed from the two groups, which have all been paid for machine learning to develop a classification tool capable of identifying signs of immune stress and other factors observed in CFS patients. RNA molecules were then mapped, showing that there were six types of cells specific to CFS patients.
“When there is a disproportionate signal of certain types of cells, this suggests that there is an underlying deregulation of these cells in the disease,” explains Gardella.
Although the test is 77% precise in CF detection using these indicators, this rate is not high enough to be considered as a reliable diagnostic tool.
However, it represents an important progression in the field of diagnosis of chronic diseases.
“For clinical use, a test would be very useful with precision greater than 90%. However, given the complexity of Me / CFS and the relatively small size of the sample, this model is a promising start for a non -invasive test, “explains Gardella, adding that his team hopes to collect more samples that still improve the performance of these models.
In addition, researchers hope to assess how AFRNA changes in the various stages of the symptoms of the CFS, as after the intense exercise. Patients with SFC sometimes feel worse after physical effort that would give a healthy person otherwise.
“In the end, we hope that this work would not only contribute both to a reliable diagnostic tool and to a more in -depth understanding of Me / CFS, but also continues to understand the biological problems that lead to the lived experiences of these patients,” explains Gardella.
Learn more: Without cause or known remedy, here is what we know about chronic fatigue syndrome
Article Sources
Our Discovermagazine.com writers use studies evaluated by high -quality peers and sources for our articles, and our publishers examine scientific precision and editorial standards. Review the sources used below for this article:
