Evidence map›Paper›PMID 36809921›Full record

ArticleMolecular medicine (Cambridge, Mass.)2023

Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning.

Maitray A Patel, Michael J Knauer, Michael Nicholson, Mark Daley, Logan R Van Nynatten, Gediminas Cepinskas, Douglas D Fraser

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular medicine (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
13.4field-weighted citation impact, top 1% of its field
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

31 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.

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  20. Utility of Protein Markers in COVID-19 Patients.International journal of molecular sciences · 2025
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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

7 authors at 1 institution in 1 country.

Maitray A PatelEpidemiology and Biostatistics, Western University, London, ON, N6A 3K7, Canada.ORCID 0000-0003-1141-8253
Michael J KnauerPathology and Laboratory Medicine, Western University, London, ON, N6A 3K7, Canada.ORCID 0000-0002-2240-4730
Michael NicholsonMedicine, Western University, London, ON, N6A 3K7, Canada.
Mark DaleyEpidemiology and Biostatistics, Western University, London, ON, N6A 3K7, Canada.
Logan R Van NynattenMedicine, Western University, London, ON, N6A 3K7, Canada.
Gediminas CepinskasLawson Health Research Institute, London, ON, N6C 2R5, Canada.ORCID 0000-0002-0267-6659
Douglas D FraserLawson Health Research Institute, London, ON, N6C 2R5, Canada. douglas.fraser@lhsc.on.ca.ORCID 0000-0002-5635-3791
Western University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSurvivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as "Long-COVID". A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID.

methodsA case-control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase.

resultsMachine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P < 0.01). Protein combinations were narrowed down to two optimal models, with nine and five proteins each, and with both having excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, F1 = 1.00). NLP expression analysis highlighted the diffuse organ system involvement in Long-COVID, as well as the involved cell types, including leukocytes and platelets, as key components associated with Long-COVID.

conclusionsProteomic analysis of plasma from Long-COVID patients identified 119 highly relevant proteins and two optimal models with nine and five proteins, respectively. The identified proteins reflected widespread organ and cell type expression. Optimal protein models, as well as individual proteins, hold the potential for accurate diagnosis of Long-COVID and targeted therapeutics.

Indexed as

COVID-19BiomarkersCase-Control StudiesHumansMachine LearningPost-Acute COVID-19 SyndromeProteomicsBiomarkersBiomarkerCell typesCOVID-19Long-COVIDMachine learningOrgan systemTargeted proteomics

Identifiers

PMID36809921
PMCPMC9942653
OpenAlexW4321611749

What OpenQuestion holds

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LicenceCC BY
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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.