Evidence map›Paper›PMID 29882373›Full record

ArticleProteomics. Clinical applications2018

Eccrine Sweat as a Biofluid for Profiling Immune Biomarkers.

Benjamin A Katchman, Meilin Zhu, Jennifer Blain Christen, Karen S Anderson

Open access · hybridAbstract read
In one paragraph

Article in Proteomics. Clinical applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
4.2field-weighted citation impact, top 5% 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

38 citing papers in PubMed, 81 citations in OpenAlex.

  1. Trial
  2. Tailoring NaSensors (Basel, Switzerland) · 2026
    Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Review
  14. Review
  15. Review
  16. Review
  17. Review
  18. Article
  19. Article
  20. 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

4 authors at 1 institution in 1 country.

Benjamin A KatchmanCenter for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA.
Meilin ZhuCenter for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA.
Jennifer Blain ChristenSchool of Electrical Engineering, Arizona State University, Tempe, AZ, 85281, USA.
Karen S AndersonCenter for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA.
Arizona State University · US

Funding

NSF
6 · The paper itself

Abstract

purposeSweat is a relatively unexplored biofluid for diagnosis and monitoring of disease states. In this study, the proteomic profiling of immune-related biomarkers from healthy individuals are presented. EXPERIMENTAL

designEccrine sweat samples are collected from 50 healthy individuals. LC-MS/MS is performed on two pools of sweat samples from five male and female participants. Individual sweat samples are analyzed by antibody isotyping microarrays (n = 49), human cytokine arrays (n = 30), and quantitative ELISAs for interleukin-1α (n = 16), epidermal growth factor (n = 6), and angiogenin (n = 7).

resultsIn sweat, 220 unique proteins are identified by shotgun analysis. Detectable antibody isotypes include IgA (100% positive; median 1230 ± 28 700 pg mL CONCLUSIONS AND CLINICAL RELEVANCE: Sweat can be used for profiling antibodies and innate immune biomarkers.

Indexed as

AdolescentAdultAgedBiomarkersChromatography, LiquidEccrine GlandsFemaleGene Expression RegulationHumansMaleMiddle AgedProteinsProteomicsSweatTandem Mass SpectrometryYoung AdultBiomarkersProteins

Identifiers

PMID29882373
PMCPMC6282813
OpenAlexW2806578062

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.