Veterinary pharmaceuticals and antibiotic resistance of Escherichia coli isolates in poultry litter from commercial farms and controlled feeding trials
Bibliographic record
Abstract
Veterinary pharmaceuticals are commonly used in poultry farming to prevent and treat microbial infections as well as to increase feed efficiency, but their use has created public and environmental health concerns. Poultry litter contains antimicrobial residues and resistant bacteria; when applied as fertilizer, the level and effects of these pharmaceuticals and antimicrobial-resistant bacteria in the environment are of concern. The purpose of this study was to investigate poultry litter for veterinary pharmaceuticals and resistance patterns of Escherichia coli. Litter samples were collected from controlled feeding trials and from commercial farms. Feed additives bacitracin, chlortetracycline, monensin, narasin, nicarbazin, penicillin, salinomycin, and virginiamycin, which were present in the feed on commercial farms and added to the feed in the controlled trials, were extracted in methanol and analyzed by liquid chromatography-mass spectrometry techniques. Sixty-nine E. coli were isolated and identified by API 20E. The susceptibility of the isolates to antibiotics was determined using Avian plates and the Sensititer automated system. This study confirmed the presence of antimicrobial residues in broiler litter from controlled environments as well as commercial farms, ranging from 0.07 to 66 mg/L depending on the compound. Concentrations of individual residues were higher in litter from controlled feeding trials than those from commercial farms. All E. coli isolates from commercial farms were multiresistant to at least 7 antibiotics. Resistance to beta-lactam antibiotics (amoxicillin, ceftiofur), tetracyclines, and sulfonamides was the most prevalent. This study concluded that broiler litter is a source of antimicrobial residues and represents a reservoir of multiple antibiotic-resistant E. coli.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".