Evidence map›Paper›PMID 34168074›Full record

ArticleLife science alliance2021

Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19.

Lucas Cardoso Lazari, Fabio De Rose Ghilardi, Livia Rosa-Fernandes, Diego M Assis, José Carlos Nicolau, Veronica Feijoli Santiago, Talia Falcão Dalçóquio, Claudia B Angeli, Adriadne Justi Bertolin, Claudio Rf Marinho and 4 more

Open access · goldAbstract read
In one paragraph

Article in Life science alliance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
2.1field-weighted citation impact, top 11% 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

32 citing papers in PubMed, 43 citations in OpenAlex.

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  11. The Impact of Serum/Plasma Proteomics on SARS-CoV-2 Diagnosis and Prognosis.International journal of molecular sciences · 2024
    Review
  12. Article
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  14. Review
  15. Prospective on Imaging Mass Spectrometry in Clinical Diagnostics.Molecular & cellular proteomics : MCP · 2023
    Review
  16. Article
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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

14 authors at 1 institution in 1 country.

Lucas Cardoso LazariDepartment of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Fabio De Rose GhilardiInstituto de Medicina Tropical, University of São Paulo, São Paulo, Brazil.
Livia Rosa-FernandesDepartment of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Diego M AssisBruker do Brasil, Atibaia, São Paulo, Brazil.
José Carlos NicolauHeart Institute (InCor), University of São Paulo Medical School, São Paulo, Brazil.ORCID 0000-0002-9680-3689
Veronica Feijoli SantiagoDepartment of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Talia Falcão DalçóquioHeart Institute (InCor), University of São Paulo Medical School, São Paulo, Brazil.
Claudia B AngeliDepartment of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.ORCID 0000-0002-3906-1973
Adriadne Justi BertolinHeart Institute (InCor), University of São Paulo Medical School, São Paulo, Brazil.
Claudio Rf MarinhoDepartment of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.ORCID 0000-0002-1227-3845
Carsten WrengerDepartment of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.ORCID 0000-0001-5987-1749
Edison Luiz DurigonDepartment of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Rinaldo Focaccia SicilianoHeart Institute (InCor), University of São Paulo Medical School, São Paulo, Brazil.
Giuseppe PalmisanoDepartment of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil palmisano.gp@gmail.com palmisano.gp@usp.br.ORCID 0000-0003-1336-6151
Universidade de São Paulo · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.

Indexed as

Machine LearningAdultAgedBiomarkersCOVID-19FemaleHumansMaleMiddle AgedPandemicsPrognosisProteomeProteomicsReproducibility of ResultsSARS-CoV-2Sensitivity and SpecificityBiomarkersProteomeSerum Amyloid A Protein

Identifiers

PMID34168074
PMCPMC8321665
OpenAlexW3173198934

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.