Evidence map›Paper›PMID 31036648›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2019

A nanoelectronics-blood-based diagnostic biomarker for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).

R Esfandyarpour, A Kashi, M Nemat-Gorgani, J Wilhelmy, R W Davis

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
  2. Recent research in myalgic encephalomyelitis/chronic fatigue syndrome: an evidence map.Health technology assessment (Winchester, England) · 2025
    Pooled it
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  6. Clinical and Preclinical Systematic Review ofFrontiers in pharmacology · 2020
    Pooled it
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  13. Cardiovascular effects of the post-COVID-19 condition.Nature cardiovascular research · 2024
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

R EsfandyarpourDepartment of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697; rahimes@uci.edu krhong@stanford.edu.
A KashiStanford Genome Technology Center, Stanford University, Stanford, CA 94304.
M Nemat-GorganiStanford Genome Technology Center, Stanford University, Stanford, CA 94304.
J WilhelmyDepartment of Biochemistry, School of Medicine, Stanford University, Stanford, CA 94304.
R W DavisStanford Genome Technology Center, Stanford University, Stanford, CA 94304; rahimes@uci.edu krhong@stanford.edu.ORCID 0000-0003-3123-2333

Funding

WHOLE YEAST GENOME TRANSCRIPTIONAL AND FUNCTIONAL GENE MAPS--A FEASIBILITY STUDYP01HG000205 · NHGRI · STANFORD UNIVERSITY · PI DAVIS, RONALD WAYNE, HOWE, ROGER THOMAS · 1990 to 2016
$120.9M
NHGRI NIH HHS P01 HG000205
6 · The paper itself

Abstract

There is not currently a well-established, if any, biological test to diagnose myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The molecular aberrations observed in numerous studies of ME/CFS blood cells offer the opportunity to develop a diagnostic assay from blood samples. Here we 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. To pursue the goal of developing a reliable biomarker for ME/CFS and to demonstrate the utility of our platform for point-of-care diagnostics, we validated the array by testing patients with moderate to severe ME/CFS patients and healthy controls. The ME/CFS samples' response to the hyperosmotic stressor observed as a unique characteristic of the impedance pattern and dramatically different from the response observed among the control samples. We believe the observed robust impedance modulation difference of the samples in response to hyperosmotic stress can potentially provide us with a unique indicator of ME/CFS. Moreover, using supervised machine learning algorithms, we developed a classifier for ME/CFS patients capable of identifying new patients, required for a robust diagnostic tool.

Indexed as

AlgorithmsBiomarkersCase-Control StudiesCell LineFatigue Syndrome, ChronicHumansLeukocytes, MononuclearMachine LearningNanotechnologyBiomarkersartificial intelligencediagnostic biomarkermachine learningmyalgic encephalomyelitis/chronic fatigue syndromenanoelectronics biosensor

Identifiers

PMID31036648
PMCPMC6535016

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.