Evidence map›Paper›PMID 41263641›Full record

ArticleVirulence2026

Surface proteome of plasma extracellular vesicles differentiates between SARS-CoV-2 and influenza infection.

Wilhelm Bertrams, Fabienne K Roessler, Rikke Bæk, Anna Lena Jung, Katrin Laakmann, Malene Møller Jørgensen, Mareike Lehmann, Barbara Weckler, Leon N Schulte, Gernot Rohde and 4 more

Abstract read
In one paragraph

Article in Virulence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

14 authors.

Wilhelm BertramsInstitute for Lung Research, Universities of Giessen and Marburg Lung Center (UGMLC), Philipps-Universität Marburg, Marburg, Germany.ORCID 0000-0002-0180-2529
Fabienne K RoesslerDepartment of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Rikke BækDepartment of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark.
Anna Lena JungInstitute for Lung Research, Universities of Giessen and Marburg Lung Center (UGMLC), Philipps-Universität Marburg, Marburg, Germany.ORCID 0000-0002-7762-4597
Katrin LaakmannInstitute for Lung Research, Universities of Giessen and Marburg Lung Center (UGMLC), Philipps-Universität Marburg, Marburg, Germany.
Malene Møller JørgensenDepartment of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark.
Mareike LehmannInstitute for Lung Research, Universities of Giessen and Marburg Lung Center (UGMLC), Philipps-Universität Marburg, Marburg, Germany.
Barbara WecklerDepartment of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps-University, Marburg, Germany.
Leon N SchulteInstitute for Lung Research, Universities of Giessen and Marburg Lung Center (UGMLC), Philipps-Universität Marburg, Marburg, Germany.ORCID 0000-0001-6814-9344
Gernot RohdeDepartment of Respiratory Medicine, Frankfurt/Main, Germany, and CAPNETZ STIFTUNG, Goethe University Frankfurt, University Hospital, Medical Clinic I, Hannover, Germany.
Nadav BarDepartment of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Grit BartenCapnetz Stiftung, Hannover, Germany.
Bernd SchmeckInstitute for Lung Research, Universities of Giessen and Marburg Lung Center (UGMLC), Philipps-Universität Marburg, Marburg, Germany.ORCID 0000-0002-2767-3606
CAPNETZ study group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small extracellular vesicles (sEVs) play a role in the pathophysiology of viral respiratory infections and may be suitable biomarkers for COVID-19 and Influenza infections, or targets for treatment. We investigated differences in the surface proteome of plasma sEVs in patients with COVID-19 and Influenza. In a discovery cohort with 117 patients, we used a random forest (RF) classifier in order to discriminate COVID-19 and Influenza patients based on routine clinical parameters. Furthermore, plasma samples from these patients were analyzed with an EV Array containing 33 antibodies to capture sEVs, which were then visualized with a combination of CD9, CD63, and CD81 antibodies. We applied an RF classifier and a random depth-first search (RDFS) approach to extract markers with the best discriminatory potential. Data were then validated in an independent set of patient samples on a chip-based ExoView platform.In the initial cohort of 117 patients, leukocyte numbers, and heart rate discriminated best between COVID-19 and Influenza infection. In the plasma samples, 32 EV surface markers could be detected. Feature panels containing CD9, CD81, and CD141 allowed a discrimination between COVID-19 and Influenza. Consecutively, increased CD9 abundance was validated in a second, independent cohort, with the ExoView technology. The increased CD9 signal in Influenza patients was confirmed and shown to be mostly driven by CD9/CD41a double positive sEVs, hinting at a thrombocyte origin.We identified leukocyte numbers and heart rate, as well as CD9 as a sEV surface marker to differentiate COVID-19 from Influenza patients.

Indexed as

COVID-19Extracellular VesiclesInfluenza, HumanProteomeAdultAgedBiomarkersDiagnosis, DifferentialFemaleHumansMaleMiddle AgedSARS-CoV-2BiomarkersProteomeCOVID-19extracellular vesiclesinfluenzamachine learning

Identifiers

PMID41263641
PMCPMC12710944

What OpenQuestion holds

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LicenceCC BY-NC
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.