Antibiotic Resistance in Diarrheagenic Escherichia coli Isolated from Broiler Chickens in Pakistan
Bibliographic record
Abstract
Background: Diarrheagenic Escherichia coli (DEC) strains are predominant cause of gastrointestinal tract illnesses. The main objective of the study was to determine antibiotic resistance in various types of DEC isolated from chicken broilers farmed in Pakistan. Methods: A total of 200 feces and 200 meat samples from broiler chickens were collected from the slaughtering shops in Southern Punjab, Pakistan. The confirmed fecal (n=150) and meat (n=150) E. coli isolates were investigated against 16 antibiotics. Fourteen virulence genes specific for Enteropathogenic (EPEC), Shiga Toxin-producing (STEC), Enteroinvasive (EIEC), Enteroaggregative (EAEC), and Enterotoxigenic (ETEC) E. coli were identified using Polymerase Chain Reaction. Results: EPEC was the most detected pathotype in both feces (76%) and meat (90%) samples, followed by STEC, EIEC, and ETEC. The highest resistance (40-90%) was observed against penicillin, oxytetracycline, and nalidixic acid in fecal isolates. More than 50% EPEC and EAEC fecal isolates, and 60% EAEC meat isolates were simultaneously resistant to 6 or more antibiotics. Conclusion: Conclusively, the broiler meat sold in open markets of Pakistan was considerably contaminated with multi-drug resistant DEC. To mitigate the issue, the gov- ernment should regulate the use of antibiotics at poultry farms and monitor slaughtering practices in slaughterer houses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".