Evidence map›Paper›PMID 32993123›Full record

ArticleMetabolites2020

Towards Best Practice in Hair Metabolomic Studies: Systematic Investigation on the Impact of Hair Length and Color.

Lisa Eisenbeiss, Tina M Binz, Markus R Baumgartner, Thomas Kraemer, Andrea E Steuer

Abstract read
In one paragraph

Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Segmental hair metabolomics analysis in pregnant women with pregnancy complications.Metabolomics : Official journal of the Metabolomic Society · 2023
    Article
  5. Review
  6. 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

5 authors.

Lisa EisenbeissDepartment of Forensic Pharmacology & Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, 8057 Zurich, Switzerland.ORCID 0000-0002-2229-4578
Tina M BinzCenter for Forensic Hair Analytics, Zurich Institute of Forensic Medicine, University of Zurich, 8006 Zurich, Switzerland.
Markus R BaumgartnerCenter for Forensic Hair Analytics, Zurich Institute of Forensic Medicine, University of Zurich, 8006 Zurich, Switzerland.
Thomas KraemerDepartment of Forensic Pharmacology & Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, 8057 Zurich, Switzerland.ORCID 0000-0002-3283-606X
Andrea E SteuerDepartment of Forensic Pharmacology & Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, 8057 Zurich, Switzerland.ORCID 0000-0001-8983-2353

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Untargeted metabolomic studies are used for large-scale analysis of endogenous compounds. Due to exceptional long detection windows of incorporated substances in hair, analysis of hair samples for retrospective monitoring of metabolome changes has recently been introduced. However, information on the general behavior of metabolites in hair samples is scarce, hampering correct data interpretation so far. The presented study aimed to investigate endogenous metabolites depending on hair color and along the hair strand and to propose recommendations for best practice in hair metabolomic studies. A metabolite selection was analyzed using untargeted data acquisition in genuine hair samples from different hair colors and after segmentation in 3 cm segments. Significant differences in metabolites among hair colors and segments were found. In conclusion, consideration of hair color and hair segments is necessary for hair metabolomic studies and, subsequently, recommendations for best practice in hair metabolomic studies were proposed.

Indexed as

endogenous compoundshair analysishair colorhair metabolomicsLC-MS/MSsegmentation

Identifiers

PMID32993123
PMCPMC7601250

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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.