Biomarker Test Developed for Chronic Fatigue Syndrome

Myalgic encephalomyelitis, or chronic fatigue syndrome (ME/CFS), is a serious condition that may affect up to 2.5 million people in the USA. Symptoms include extreme tiredness, difficulty sleeping, trouble with thinking and remembering things, muscle pain and aches, a recurring sore throat, and tender lymph nodes.

Currently, physicians can only diagnose ME/CFS by examining a person's symptoms and medical history, and by excluding other possible illnesses. This can make the diagnosis process difficult, lengthy, and inaccurate. A new diagnostic test looks at how a person's immune cells react to stress.


Scientists at the Stanford University School of Medicine (Stanford, CA, USA) have developed a nanoelectronics assay designed as an ultrasensitive assay capable of directly measuring biomolecular interactions in real time, at low cost, and in a multiplex format. The team applied the test to the blood samples of 40 people, 20 of who had ME/CFS and 20 whom did not.

The scientists used a nanoelectronic assay, which measures small changes in energy to assess the health of immune cells and blood plasma, to see how the immune cells and blood plasma process stress. To develop the test, the team took advantage of "advancements in micro/nanofabrication, direct electrical detection of cellular and molecular properties, microfluidics, and artificial intelligence techniques."

The test detects "biomolecular interactions in real time" by using thousands of electrodes to create an electrical current, and by using small chambers that contain blood samples with only immune cells and blood plasma. Inside the small chambers, the immune cells and plasma interact with the electrical current, altering its flow. The scientists used salt to stress the blood samples of some people with ME/CFS and some people without the condition. They then assessed the changes in electrical current. Their test accurately identified all of the people with ME/CFS without misidentifying any of the people who did not have the condition.

The team concluded that they had observed robust impedance modulation difference of the samples in response to hyperosmotic stress can potentially provide a unique indicator of ME/CFS. Moreover, using supervised machine learning algorithms, they developed a classifier for ME/CFS patients capable of identifying new patients, required for a robust diagnostic tool.

Rahim Esfandyarpour, PhD, a Bioengineer and first author of the study, said, “Using the nanoelectronics assay, we can add controlled doses of many different potentially therapeutic drugs to the patient's blood samples and run the diagnostic test again.” The study was published on April 29, 2019, in the journal Proceedings of the National Academy of Sciences.

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Stanford University School of Medicine