Longitudinal Characterization of Resistant <i>Escherichia coli</i> in Fecal Deposits from Cattle Fed Subtherapeutic Levels of Antimicrobials
Bibliographic record
Abstract
Model fecal deposits from cattle fed or not fed antimicrobial growth promoters were examined over 175 days in the field for growth and persistence of total Escherichia coli and numbers and proportions of ampicillin-resistant (Amp(r)) and tetracycline-resistant (Tet(r)) E. coli. In addition, genotypic diversity and the frequency of genetic determinants encoded by Amp(r) and Tet(r) E. coli were investigated. Cattle were fed diets containing chlortetracycline (44 ppm; A44 treatment group), chlortetracycline plus sulfamethazine (both at 44 ppm; AS700 treatment group), or no antibiotics (control). Fecal deposits were sampled 12 times over 175 days. Numbers of Tet(r) E. coli in A44 and AS700 deposits were higher (P < 0.001) than those of controls and represented up to 35.6% and 20.2% of total E. coli, respectively. A time-by-treatment interaction (P < 0.001) was observed for the numbers of Tet(r) and Amp(r) E. coli. Except for Amp(r) E. coli in control deposits, all E. coli numbers increased (P < 0.001) in deposits up to day 56. Even after 175 days, high Tet(r) E. coli numbers were detected in A44 and AS700 deposits [5.9 log(10) CFU (g dry matter)(-1) and 5.4 log(10) CFU (g dry matter)(-1), respectively]. E. coli genotypes, as determined by pulsed-field gel electrophoresis, were diverse and were influenced by the antimicrobial growth promoter and the sampling time. Of the determinants screened, bla(TEM1), tetA, tetB, tetC, sul1, and sul2 were frequently detected. Occurrence of determinants was influenced by the feeding of antimicrobials. Fecal deposits remain a source of resistant E. coli even after a considerable period of environmental exposure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".