Accelerated Biodegradation of Veterinary Antibiotics in Agricultural Soil following Long-Term Exposure, and Isolation of a Sulfamethazine-degrading <i>Microbacterium</i> sp.
Bibliographic record
Abstract
The World Health Organization has identified antibiotic resistance as one of the top three threats to global health. There is concern that the use of antibiotics as growth promoting agents in livestock production contributes to the increasingly problematic development of antibiotic resistance. Many antibiotics are excreted at high rates, and the land application of animal manures represents a significant source of environmental exposure to these agents. To evaluate the long-term effects of antibiotic exposure on soil microbial populations, a series of field plots were established in 1999 that have since received annual applications of a mixture of sulfamethazine (SMZ), tylosin (TYL), and chlortetracycline (CTC). During the first 6 yr (1999-2004) soils were treated at concentrations of 0, 0.01 0.1, and 1.0 mg kg soil, in subsequent years at concentrations of 0, 0.1, 1.0, and 10 mg kg soil. The lower end of this concentration range is within that which would result from an annual application of manure from medicated swine. Following ten annual applications, the fate of the drugs in the soil was evaluated. Residues of SMZ and TYL, but not CTC were removed much more rapidly in soil with a history of exposure to 10 mg/kg drugs than in untreated control soil. Residues of C-SMZ were rapidly and thoroughly mineralized to CO in the historically treated soils, but not in the untreated soil. A SMZ-degrading sp. was isolated from the treated soil. Overall, these results indicate that soil bacteria adapt to long-term exposure to some veterinary antibiotics resulting in sharply reduced persistence. Accelerated biodegradation of antibiotics in matrices exposed to agricultural, wastewater, or pharmaceutical manufacturing effluents would attenuate environmental exposure to antibiotics, and merits investigation in the context of assessing potential risks of antibiotic resistance development in environmental matrices.
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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 | 0.000 | 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".