Monitoring the Spread of Multidrug-Resistant Escherichia coli Throughout the Broiler Production Cycle
Bibliographic record
Abstract
The extensive use of antimicrobials in broiler production is changing the bird microbiota, fostering drug-resistant bacteria, and complicating therapeutic interventions, making the problem of multidrug resistance global. The monitoring of antimicrobial virulence and resistance genes are tools that have come to assist the breeding of these animals, directing possible treatments as already used in human medicine and collecting data to demonstrate possible dissemination of multidrug-resistant strains that may cause damage to industry and public health. This work aimed to monitor broiler farms in southern Brazil, isolating samples of E. coli and classifying them according to the profile of resistance to antimicrobials of interest to human and animal health. We also monitored the profile of virulence genes and conducted an epidemiological survey of possible risk factors that contribute to this selection of multidrug-resistant isolates. Monitoring was carried out on farms in the three southern states of the country, collecting samples of poultry litter, cloacal swabs, and beetles of the species Alphitobius diaperinus, isolating E. coli from each of these samples. These were evaluated by testing their susceptibility to antimicrobials of animal and human interest; detecting whether the samples were extended-spectrum β-lactamase enzyme (ESBL) producers; and when positive, selected for genotypic tests to identify resistant genes (CTX-M, TEM, and SHV) and virulence. Among the antimicrobials tested, enrofloxacin and ciprofloxacin demonstrated some of the highest frequencies of resistance in the isolated strains, with significant statistical results. The use of these antimicrobials increased the likelihood of resistance by over three times and was associated with a 1.5-fold higher probability of multidrug resistance. Of all isolates, 95% were multidrug-resistant, raising concerns for production and public health. Among 231 ESBL-positive samples, the CTX-M1 group predominated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".