Evidence map›Paper›PMID 39384817›Full record

ArticleScientific data2024

A dataset for developing proteomic tools for pathogen detection via differential cell lysis of whole blood samples.

Jéssica de Oliveira Veloso Rezende, Michel Batista, Kelly Cavalcanti Machado, Thiago Bousquet Bandini, Igor Alexandre Côrtes de Menezes, Fernanda do Carmo De Stefani, Marlon Dias Mariano Santos, Paulo Costa Carvalho, Louise Ulrich Kurt, Rodrigo Soares Caldeira Brant and 2 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

12 authors.

Jéssica de Oliveira Veloso Rezende *Laboratory for Applied Science and Technology in Health, Carlos Chagas Institute, Fiocruz Paraná, Paraná, Brazil. jessica_oliveiraveloso@hotmail.com.
Michel Batista *Laboratory for Applied Science and Technology in Health, Carlos Chagas Institute, Fiocruz Paraná, Paraná, Brazil.
Kelly Cavalcanti MachadoMass Spectrometry Facility RPT02H, Carlos Chagas Institute, Fiocruz Paraná, Paraná, Brazil.
Thiago Bousquet BandiniMass Spectrometry Facility RPT02H, Carlos Chagas Institute, Fiocruz Paraná, Paraná, Brazil.
Igor Alexandre Côrtes de MenezesInternal Medicine Department, Federal University of Paraná, Paraná, Brazil.
Fernanda do Carmo De StefaniClinical Hospital, Federal University of Paraná, Paraná, Brazil.
Marlon Dias Mariano SantosLaboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz Paraná, Curitiba, Brazil.
Paulo Costa CarvalhoLaboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz Paraná, Curitiba, Brazil.ORCID 0000-0001-6530-3350
Louise Ulrich KurtLaboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz Paraná, Curitiba, Brazil.
Rodrigo Soares Caldeira BrantMass Spectrometry Facility RPT02H, Carlos Chagas Institute, Fiocruz Paraná, Paraná, Brazil.
Luis Gustavo MorelloLaboratory for Applied Science and Technology in Health, Carlos Chagas Institute, Fiocruz Paraná, Paraná, Brazil.
Fabricio Klerynton MarchiniLaboratory for Applied Science and Technology in Health, Carlos Chagas Institute, Fiocruz Paraná, Paraná, Brazil. fabricio.marchini@fiocruz.br.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This data descriptor presents a curated dataset for pathogen detection and identification (Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans) directly from whole-blood samples. The dataset was created using differential cell lysis combined with rapid extraction, digestion, and mass spectrometry-based proteomics. Our method offers a rapid diagnostic alternative to traditional culture, enabling timely disease management, such as sepsis. Highlighting our dataset's uniqueness, it features a three-tier structure: Spectral Libraries of Pathogens for identifying peptide peaks for putative biomarkers; Spiked pathogen in blood MS data for biomarker panel optimization through varied concentration samples; and Parallel Reaction Monitoring (PRM) data from sepsis patients for validating our biomarker panel, achieving 83.3% sensitivity within seven hours without microbial enrichment culture. This dataset serves as a comprehensive reference for bioinformatic tool development and biomarker panel proposals, advancing microbial detection, antimicrobial resistance, and epidemiological studies.

Indexed as

BiomarkersCandida albicansProteomicsPseudomonas aeruginosaSepsisStaphylococcus aureusHumansMass SpectrometryBiomarkers

Identifiers

PMID39384817
PMCPMC11464718

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Registered trials

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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.