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Investigation of metabolites for estimating blood deposition time

Overview of attention for article published in International Journal of Legal Medicine, August 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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6 X users
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Title
Investigation of metabolites for estimating blood deposition time
Published in
International Journal of Legal Medicine, August 2017
DOI 10.1007/s00414-017-1638-y
Pubmed ID
Authors

Karolina Lech, Fan Liu, Sarah K. Davies, Katrin Ackermann, Joo Ern Ang, Benita Middleton, Victoria L. Revell, Florence J. Raynaud, Igor Hoveijn, Roelof A. Hut, Debra J. Skene, Manfred Kayser

Abstract

Trace deposition timing reflects a novel concept in forensic molecular biology involving the use of rhythmic biomarkers for estimating the time within a 24-h day/night cycle a human biological sample was left at the crime scene, which in principle allows verifying a sample donor's alibi. Previously, we introduced two circadian hormones for trace deposition timing and recently demonstrated that messenger RNA (mRNA) biomarkers significantly improve time prediction accuracy. Here, we investigate the suitability of metabolites measured using a targeted metabolomics approach, for trace deposition timing. Analysis of 171 plasma metabolites collected around the clock at 2-h intervals for 36 h from 12 male participants under controlled laboratory conditions identified 56 metabolites showing statistically significant oscillations, with peak times falling into three day/night time categories: morning/noon, afternoon/evening and night/early morning. Time prediction modelling identified 10 independently contributing metabolite biomarkers, which together achieved prediction accuracies expressed as AUC of 0.81, 0.86 and 0.90 for these three time categories respectively. Combining metabolites with previously established hormone and mRNA biomarkers in time prediction modelling resulted in an improved prediction accuracy reaching AUCs of 0.85, 0.89 and 0.96 respectively. The additional impact of metabolite biomarkers, however, was rather minor as the previously established model with melatonin, cortisol and three mRNA biomarkers achieved AUC values of 0.88, 0.88 and 0.95 for the same three time categories respectively. Nevertheless, the selected metabolites could become practically useful in scenarios where RNA marker information is unavailable such as due to RNA degradation. This is the first metabolomics study investigating circulating metabolites for trace deposition timing, and more work is needed to fully establish their usefulness for this forensic purpose.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 10 24%
Student > Master 6 15%
Researcher 5 12%
Other 3 7%
Professor 2 5%
Other 4 10%
Unknown 11 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 24%
Agricultural and Biological Sciences 6 15%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Medicine and Dentistry 2 5%
Materials Science 2 5%
Other 5 12%
Unknown 14 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 June 2020.
All research outputs
#5,597,525
of 22,996,001 outputs
Outputs from International Journal of Legal Medicine
#273
of 2,081 outputs
Outputs of similar age
#88,023
of 317,463 outputs
Outputs of similar age from International Journal of Legal Medicine
#7
of 56 outputs
Altmetric has tracked 22,996,001 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,081 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 317,463 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.