Enriched Shotgun Sequencing to Assess the Effects of Interventions to Reduce Antimicrobial Use in Neonatal Dairy Calves
Bibliographic record
Abstract
BACKGROUND: Neonatal diarrhea accounts for 20%-25% of morbidity among calves, and antimicrobial drugs (AMDs) are often administered for treatment. Systematic approaches that mitigate antimicrobial use (AMU) can be effective in decreasing antimicrobial resistance (AMR). HYPOTHESIS/OBJECTIVES: To determine the effects of an algorithmic farm-based intervention that reduced AMU for diarrhea on the community structure of antimicrobial resistance genes (ARGs) identified in the feces of healthy dairy calves. ANIMALS: Thirty-one fecal dairy calf samples collected at two timepoints and farms (N = 7-8 per sampling point) were used. Samples were obtained before AMU reductions and 12 months afterward. METHODS: Target-enriched shotgun sequencing was performed to characterize all ARGs in samples. Bioinformatics processing and statistical analysis were performed using the AMR++ pipeline, MEGARes AMR database, and R. RESULTS: Pre-intervention comparisons showed increased relative abundances (RA) consistent with the AMU on each farm. Intra-farm results showed that on Farm 1, there were significant increases in the RA of ARGs for tetracyclines (22.1%-27.4%, q = 0.02) and fluoroquinolones (0%-0.1%, q < 0.0001) in the Post period. On Farm 2, significant decreases were seen over time in the RA of ARGs for sulfonamides (9.6%-5.1%, q = 0.006) and fluoroquinolones (0.77%-0.12%, q = 0.004). CONCLUSIONS AND CLINICAL IMPORTANCE: Despite similar reductions in AMU on both farms, implementing an antimicrobial stewardship algorithm was associated with differing effects on and changes to the fecal resistome.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".