Comparative analysis of antimicrobial resistance and genetic characteristics of <i>Escherichia coli</i> from broiler breeder farms in Korea
Bibliographic record
Abstract
Broiler breeder farms could be a reservoir of Escherichia coli, disseminating antimicrobial resistance and virulence factors. We investigated the antimicrobial resistance of E. coli from nine broiler breeder farms and characterised their resistance and virulence genes. A total of 256 E. coli showed a high level of resistance to tetracycline, nalidixic acid, ampicillin, and cephalothin, followed by trimethoprim–sulfamethoxazole and chloramphenicol. The resistance to nalidixic acid, ampicillin, trimethoprim–sulfamethoxazole, and chloramphenicol showed significant differences among the farms. Among 202 β-lactam-resistant E. coli, 138 carried β-lactamase genes. The most prevalent β-lactamase gene was blaTEM-1, of which the presence differed significantly across the farms. Out of 197 tetracycline-resistant E. coli isolates, tetA and tetB were detected in 164 and 50, with significant differences among the farms. Also, 45 of 196 nalidixic acid-resistant E. coli carried qnrS, whereas 67 of 149 trimethoprim–sulfamethoxazole-resistant E. coli carried sul2. Among the five virulence genes tested, ompT was the most prevalent, and all genes except for iutA distributed significantly different among the farms. The phenotypic and genotypic characteristics of E. coli were significantly different among the farms; therefore, management at the breeder level is required to control the vertical transmission of E. coli.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".