Selected Antimicrobial Resistance during Composting of Manure from Cattle Administered Sub‐Therapeutic Antimicrobials
Bibliographic record
Abstract
Composting is being increasingly employed for the recycling of nutrients in manure from the livestock industry. However, composting manure from animals fed antimicrobials has not been well characterized. In this study, compost windrows were prepared using manure collected from cattle (Bos Taurus L.) fed tylosin (TY), chlortetracycline-sulphamethazine (TS), and control cattle (no antimicrobials). The objectives of the 18-wk trial were to quantitatively assess the survival of total E. coli, E. coli resistant to ampicillin (Amp(r)) and tetracycline (Tet(r)), and select tetracycline (tet) and erythromycin resistance methylase (erm) genes. We found that while compost windrows did not reach the recommended temperature of 55 degrees C for 15 d, composting reduced high initial levels of total, Amp(r), and Tet(r) E. coli as early as Week 2. A significant antimicrobial effect on total (P = 0.04) and Amp(r) (P = 0.03) E. coli was observed. Significant antimicrobial x time interactions were observed from Week 0 to Week 3 (Total E. coli: P = 0.04; Amp(r): P = 0.02; Tet(r): P = <0.001). Low absolute abundance of tet and erm genes (<10(6) copies g(-1)) was found and the resistance genes displayed different dynamics; tet(A,C) and erm(A) increased marginally at Week 11 relative to Week 0 and 5 and the remaining genes (tet(G), RPP tet, erm(B), erm(C), erm(F), erm(T), and erm(X)) decreased for most time points and treatments. These results indicate that even though composting reduces antimicrobial resistant E. coli, tet and erm genes could still be detected. Our experiments reiterate advantages of polymerase chain reaction (PCR)-based quantitative assays over cultivation-based methods for the rapid identification of composting effectiveness in eliminating resistance genes before land application.
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 | 0.000 | 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) | 0.001 | 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".