Characterization of tetracycline resistance genes in <i>Escherichia coli</i> isolated from feedlot cattle administered therapeutic or subtherapeutic levels of tetracycline
Bibliographic record
Abstract
The effect of administering feedlot cattle subtherapeutic levels of chlortetracycline (CT) or CT and therapeutic levels of oxytetracycline (CT-OX) on resistance genotypes in Escherichia coli was investigated. Detection of genes tet(A), tet(B), and tet(C) encoded by tetracycline-resistant isolates (CT, N = 77; CT-OX, N = 99) was performed by multiplex polymerase chain reaction (PCR). Prevalence of tet(A) was similar in isolates across treatment regimes; however, prevalence of tet(B) was lower (18% versus 34%; P < 0.05) and tet(C) was higher (46% versus 28%; P < 0.05) in CT isolates compared with CT-OX isolates. To further characterize selection of resistance genotypes in E. coli, a group of intermediately tetracycline-resistant E. coli (N = 48) was analyzed. The tet(C) gene was present in 92% of these isolates. Copies of tet(C) transcripts, analyzed by real-time PCR, indicated that upregulation did not occur in tetracycline-resistant isolates when compared with intermediately resistant isolates. The minimum inhibitory concentrations of tetracycline, chlortetracycline, and oxytetracycline were also tested on isolates with different resistance genes. The minimum inhibitory concentration was dependent on the tetracycline analogue and the nature of encoded resistance. These data indicate that tetracycline analogues should not be used interchangeably to evaluate resistance and that prevalence of resistance genes in E. coli can vary according to the tetracycline analogue administered to cattle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".