Evidence map›Paper›PMID 38633161›Full record

ArticleOpen veterinary journal2024

Bacteriological evaluation and advanced SYBR-green multiplex real-time PCR assay for detection of minced meat adulteration.

Soad H El-Sheikh, Reham M Abdel Whab, Rania A ElDaly, Mona T Raslan, Hanan A Fahmy, Azza S El-Demerdash

Abstract read
In one paragraph

Article in Open veterinary journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

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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

6 authors.

Soad H El-SheikhDepartment of Food Hygiene, Agriculture Research Centre (ARC), Animal Health Research Institute (AHRI), Zagazig, Egypt.
Reham M Abdel WhabDepartment of Food Hygiene, Agriculture Research Centre (ARC), Animal Health Research Institute (AHRI), Zagazig, Egypt.
Rania A ElDalyDepartment of Botany and Microbiology, Faculty of Science, Arish University, Al-Arish, Egypt.
Mona T RaslanDepartment of Food Hygiene, Agriculture Research Centre (ARC), Animal Health Research Institute (AHRI), Giza, Egypt.
Hanan A FahmyDepartment of Biotechnology, Agricultural Research Centre, Animal Health Research Institute, Giza, Egypt.
Azza S El-DemerdashLaboratory of Biotechnology, Department of Microbiology, Agriculture Research Centre (ARC), Animal Health Research Institute (AHRI), Zagazig, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Minced meat is a valuable source of nutrients, but it is vulnerable to contamination by microorganisms commonly present in the environment. In addition, there is a risk of adulteration with cheaper meat sources, which can be harmful to consumers. Aim: It is crucial to identify meat adulteration with distinct microbiological analysis for legal, economic, religious, and public health purposes. Methods: A total of 100 minced meat samples were collected from several markets in Sharkia Governorate, Egypt. These samples were then subjected to bacteriological testing and an advanced multiplex PCR method. This method enables the detection of bovine, equine, porcine, and dog species in meat samples with just one step. Results: The adulterated samples had a higher total bacterial count and pH values compared to pure bovine meat. These differences in bacterial count and pH values were statistically significant, with Conclusion: This method provides a sensitive and specific approach to detect issues related to well-being and safety.

Indexed as

BenzothiazolesDiaminesFood ContaminationMeatQuinolinesAnimalsCattleDogsEscherichia coliHorsesMultiplex Polymerase Chain ReactionReal-Time Polymerase Chain ReactionSwineBenzothiazolesDiaminesQuinolinesSYBR Green IBacteriological examinationMeat adulterationMultiplex PCR assaypH values

Identifiers

PMID38633161
PMCPMC11018440

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.