Evidence map›Paper›PMID 33925360›Full record

ArticleFoods (Basel, Switzerland)2021

A Proteomic Study for the Discovery of Beef Tenderness Biomarkers and Prediction of Warner-Bratzler Shear Force Measured on

Yao Zhu, Mohammed Gagaoua, Anne Maria Mullen, Alan L Kelly, Torres Sweeney, Jamie Cafferky, Didier Viala, Ruth M Hamill

Open access · goldAbstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.6field-weighted citation impact, top 7% 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

12 citing papers in PubMed, 34 citations in OpenAlex.

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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

8 authors at 4 institutions in 2 countries.

Yao ZhuFood Quality and Sensory Science Department, Teagasc Food Research Centre, Ashtown, D15 KN3K Dublin 15, Ireland.ORCID 0000-0002-5121-0820
Mohammed GagaouaFood Quality and Sensory Science Department, Teagasc Food Research Centre, Ashtown, D15 KN3K Dublin 15, Ireland.ORCID 0000-0001-6913-3379
Anne Maria MullenFood Quality and Sensory Science Department, Teagasc Food Research Centre, Ashtown, D15 KN3K Dublin 15, Ireland.ORCID 0000-0003-1769-0768
Alan L KellySchool of Food and Nutritional Sciences, University College Cork, T12 K8AF Cork, Ireland.
Torres SweeneySchool of Veterinary Sciences, University College Dublin, D04 V1W8 Dublin 4, Ireland.ORCID 0000-0003-2639-395X
Jamie CafferkyFood Quality and Sensory Science Department, Teagasc Food Research Centre, Ashtown, D15 KN3K Dublin 15, Ireland.ORCID 0000-0002-9186-5135
Didier VialaMetabolomic and Proteomic Exploration Facility (PFEM), INRAE, F-63122 Saint-Genès-Champanelle, France.
Ruth M HamillFood Quality and Sensory Science Department, Teagasc Food Research Centre, Ashtown, D15 KN3K Dublin 15, Ireland.
Teagasc - The Irish Agriculture and Food Development Authority · IEInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement · FRUniversity College Cork · IEUniversity College Dublin · IE

Funding

Department of Agriculture, Food and the Marine, Ireland 11SF311Enterprise Ireland grant agreement No. 713654FP7 People: Marie-Curie Actions grant agreement No. 713654Teagasc Walsh Scholarship 2016022
6 · The paper itself

Abstract

Beef tenderness is of central importance in determining consumers' overall liking. To better understand the underlying mechanisms of tenderness and be able to predict it, this study aimed to apply a proteomics approach on the

Indexed as

beef tendernessbovine biomarkersfoodomicsliquid chromatography-tandem mass spectrometry (LC-MS/MS)muscleproteome

Identifiers

PMID33925360
PMCPMC8145402
OpenAlexW3157949423

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.