Evidence map›Paper›PMID 29376041›Full record

ArticleFrontiers in pediatrics2017

Skin Biomarkers for Cystic Fibrosis: A Potential Non-Invasive Approach for Patient Screening.

Cibele Zanardi Esteves, Letícia de Aguiar Dias, Estela de Oliveira Lima, Diogo Noin de Oliveira, Carlos Fernando Odir Rodrigues Melo, Jeany Delafiori, Carla Cristina Souza Gomez, José Dirceu Ribeiro, Antônio Fernando Ribeiro, Carlos Emílio Levy and 1 more

Abstract read
In one paragraph

Article in Frontiers in pediatrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. No skin off your back: the sampling and extraction of sebum for metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2023
    Review
  5. Article
  6. Review
  7. Article
  8. Proof of concept for identifying cystic fibrosis from perspiration samples.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  9. Urinary metabolomics reveals unique metabolic signatures in infants with cystic fibrosis.Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society · 2019
    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

11 authors.

Cibele Zanardi EstevesInnovare Biomarkers Laboratory, Medicine and Experimental Surgery Nucleus, University of Campinas, Campinas, Brazil.
Letícia de Aguiar DiasInnovare Biomarkers Laboratory, Medicine and Experimental Surgery Nucleus, University of Campinas, Campinas, Brazil.
Estela de Oliveira LimaInnovare Biomarkers Laboratory, Medicine and Experimental Surgery Nucleus, University of Campinas, Campinas, Brazil.
Diogo Noin de OliveiraInnovare Biomarkers Laboratory, Medicine and Experimental Surgery Nucleus, University of Campinas, Campinas, Brazil.
Carlos Fernando Odir Rodrigues MeloInnovare Biomarkers Laboratory, Medicine and Experimental Surgery Nucleus, University of Campinas, Campinas, Brazil.
Jeany DelafioriInnovare Biomarkers Laboratory, Medicine and Experimental Surgery Nucleus, University of Campinas, Campinas, Brazil.
Carla Cristina Souza GomezPediatric Department, University of Campinas, Campinas, Brazil.
José Dirceu RibeiroPediatric Department, University of Campinas, Campinas, Brazil.
Antônio Fernando RibeiroPediatric Department, University of Campinas, Campinas, Brazil.
Carlos Emílio LevyClinical Pathology Department, University of Campinas, Campinas, Brazil.
Rodrigo Ramos CatharinoInnovare Biomarkers Laboratory, Medicine and Experimental Surgery Nucleus, University of Campinas, Campinas, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCystic fibrosis (CF) is a disabling genetic disease with an increased prevalence in European heritage populations. Currently, the most used technique for collection of CF samples and diagnosis is provided through uncomfortable tests, with uncertain results, mostly based on chloride concentration in sweat. Since CF mutation induces many metabolic changes in patients, exploring these alterations might be an alternative to visualize potential biomarkers that could be used as interesting tools for further diagnostic upgrade, prioritizing simplicity, low cost, and quickness.

methodsThis contribution describes an accurate strategy to provide potential biomarkers related to CF, which may be understood as a potential tool for new diagnostic approaches and/or for monitoring disease evolution. Therefore, the present proposal consists of using skin imprints on silica plates as a way of sample collection, followed by direct-infusion high-resolution mass spectrometry and multivariate data analysis, intending to identify metabolic changes in skin composition of CF patients.

resultsMetabolomics analysis allowed identifying chemical markers that can be traced back to CF in patients' skin imprints, differently from control subjects. Seven chemical markers from several molecular classes were elected, represented by bile acids, a glutaric acid derivative, thyrotropin-releasing hormone, an inflammatory mediator, a phosphatidic acid, and diacylglycerol isomers, all reflecting metabolic disturbances that occur due to of CF.

conclusionThe comfortable method of sample collection combined with the identified set of biomarkers represent potential tools that open the range of possibilities to manage CF and follow the disease evolution. This exploratory approach points to new perspectives about the development of diagnostic assay using biomarkers and the management CF.

Indexed as

biomarkerscystic fibrosiscystic fibrosis transmembrane conductance regulatormetabolomicsscreeningskin

Identifiers

PMID29376041
PMCPMC5767587

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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.