Evidence map›Paper›PMID 39920217›Full record

ArticleScientific reports2025

Comprehensive analysis of antimicrobial resistance dynamics among broiler and duck intensive production systems.

Zsombor Szoke, Peter Fauszt, Maja Mikolas, Peter David, Emese Szilagyi-Tolnai, Georgina Pesti-Asboth, Judit Rita Homoki, Ildiko Kovacs-Forgacs, Ferenc Gal, Laszlo Stundl and 5 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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  13. Antimicrobial Susceptibility Profiles ofAntibiotics (Basel, Switzerland) · 2025
    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

15 authors.

Zsombor SzokeFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary.
Peter FausztFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary.
Maja MikolasFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary.
Peter DavidFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary.
Emese Szilagyi-TolnaiFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary.
Georgina Pesti-AsbothFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary.
Judit Rita HomokiFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary.
Ildiko Kovacs-ForgacsFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary.
Ferenc GalFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary.
Laszlo StundlFaculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, Hungary.
Levente CzeglediFaculty of Agricultural and Food Sciences and Environmental Management, Institute of Animal Science, Biotechnology and Nature Conservation, Department of Animal Husbandry, University of Debrecen, Debrecen, Hungary.
Aniko StagelHungarian National Blood Transfusion Service Nucleic Acid Testing Laboratory, Budapest, Hungary.
Sandor BiroFaculty of Medicine, Department of Human Genetics, University of Debrecen, Debrecen, Hungary.
Judit RemenyikFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary.
Melinda PaholcsekFaculty of Agricultural and Food Sciences and Environmental Management, Complex Systems and Microbiome-innovations Centre, University of Debrecen, Debrecen, Hungary. paholcsek.melinda@agr.unideb.hu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antimicrobial resistance (AMR) is a global health challenge requiring cross-sector action, with research largely focused on chickens, leaving ducks underexplored. This study examines AMR dynamics in Ross 308 broilers and Cherry Valley ducks over 15 months and 15 stocking periods under consistent rearing conditions. A total of 96 pooled samples were collected: 50 from broiler farms (26 biological, 24 environmental) and 46 from duck farms (24 biological, 22 environmental). Using next-generation shotgun sequencing, 3,665 distinct AMR types were identified: 1,918 in broilers and 1,747 in ducks. Host-specific AMRs comprised 25.3% in broilers and 18% in ducks, while 56.7% were shared. AMR diversity declined across production phases, with broilers losing 641 types and ducks losing 308, yet AMR frequencies increased significantly by the finisher phase (p < 0.0001). Based on in silico data, prophylactic antibiotic use significantly reduced the prevalence of multidrug-resistant bacteria in both poultry species (p < 0.05). Hospital-acquired infection-associated AMRs were higher in broilers than in ducks at the start of production but declined significantly by the end of the rearing period (p < 0.0001). Above-average resistance markers accounted for approximately 10% of all detected resistance determinants. Tetracycline and phenicol resistances emerged as the most prevalent. 13 high-resistance carrier (HRC) species were shared between both hosts. Broiler-specific HRCs exhibited significantly higher abundances (relative frequency: 0.08) than duck-specific HRCs (relative frequency: 0.003, p = 0.035). The grower phase emerged as a critical intervention point. In farm environments 15 broiler-specific and 9 duck-specific biomarker species were identified, each strongly correlated with poultry-core HRCs (correlation coefficient > 0.7). Broiler exhibited higher abundances of key resistance genes, with tetracycline resistance predominantly associated with Bacteroides coprosuis, Pasteurella multocida, and Acinetobacter baumannii. Despite its limitations, this research provides key insights into AMR trends in two major poultry types, guiding targeted interventions and sustainable management strategies.

Indexed as

Anti-Bacterial AgentsBacteriaChickensDrug Resistance, BacterialDrug Resistance, Multiple, BacterialDucksAnimal HusbandryAnimalsPoultry DiseasesAnti-Bacterial AgentsBiomarkersIntensive broiler rearingIntensive duck rearingPoultry microbiomePoultry resistomeProphylactic antibiotic use

Identifiers

PMID39920217
PMCPMC11806100

What Socratic 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 Socratic graph.