Evidence map›Paper›PMID 37367850›Full record

ReviewMetabolites2023

The Importance of Monitoring Cortisol in the Agri-Food Sector-A Systematic Review.

Dayana Aguiar, Carlos Marques, Ana C Pereira

Abstract readReview
In one paragraph

Review in Metabolites, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

3 authors.

Dayana AguiarDepartment of Physics, University of Aveiro, 3810-193 Aveiro, Portugal.ORCID 0000-0002-3527-447X
Carlos MarquesDepartment of Physics, University of Aveiro, 3810-193 Aveiro, Portugal.ORCID 0000-0002-8596-5092
Ana C PereiraISOPlexis, Centre for Sustainable Agriculture and Food Technology, University of Madeira, Campus da Penteada, 9020-105 Funchal, Portugal.ORCID 0000-0003-3097-7704

Funding

Fundação para a Ciência e Tecnologia 2021.00667.CEECINDFundação para a Ciência e Tecnologia BI/UI96/10165/2022Fundação para a Ciência e Tecnologia PTDC/EEI-EEE/0415/2021
6 · The paper itself

Abstract

Cortisol monitoring in the agri-food sector is considered a valuable tool due to its direct correlation with growth, reproduction, the immune system, and overall animal welfare. Strategies to monitor this stress hormone and its correlation to food quality and security have been studied in fish farming and the livestock industry. This review discusses studies on monitoring cortisol in the food industry for the first time. The impact of cortisol on animal production, quality, and the security of food products, and the analytical procedures commonly implemented for sample pre-concentration and quantification by liquid chromatography coupled to mass spectrometry, are reviewed and discussed according to the results published in the period 2012-2022. Aquaculture, or fish farming, is the leading agri-food sector, where cortisol's impact and usefulness are better known than in livestock. The determination of cortisol in fish not only allows for an increase in the production rate, but also the ability to monitor the water quality, enhancing the sustainable development of this industry. In cattle, further studies are needed since it has mainly been used to detect the administration of illicit substances. Current analytical control and monitoring techniques are expensive and often depend on invasive sampling, not allowing fast or real-time monitoring.

Indexed as

animal welfarecortisolextractionfood industryLC-MS/MSstress monitoring

Identifiers

PMID37367850
PMCPMC10302238

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.