Pathogens of Bovine Respiratory Disease in North American Feedlots Conferring Multidrug Resistance via Integrative Conjugative Elements
Bibliographic record
Abstract
In this study, we determined the prevalence of bovine respiratory disease (BRD)-associated viral and bacterial pathogens in cattle and characterized the genetic profiles, antimicrobial susceptibilities, and nature of antimicrobial resistance determinants in collected bacteria. Nasopharyngeal swab and lung tissue samples from 68 BRD mortalities in Alberta, Canada (n = 42), Texas (n = 6), and Nebraska (n = 20) were screened using PCR for bovine viral diarrhea virus (BVDV), bovine respiratory syncytial virus, bovine herpesvirus 1, parainfluenza type 3 virus, Mycoplasma bovis, Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni. Excepting bovine herpesvirus 1, all agents were detected. M. haemolytica (91%) and BVDV (69%) were the most prevalent, with cooccurrence in 63% of the cattle. Isolates of M. haemolytica (n = 55), P. multocida (n = 8), and H. somni (n = 10) from lungs were also collected. Among M. haemolytica isolates, a clonal subpopulation (n = 8) was obtained from a Nebraskan feedlot. All three bacterial pathogens exhibited a high rate of antimicrobial resistance, with 45% exhibiting resistance to three or more antimicrobials. M. haemolytica (n = 18), P. multocida (n = 3), and H. somni (n = 3) from Texas and Nebraska possessed integrative conjugative elements (ICE) that conferred resistance for up to seven different antimicrobial classes. ICE were shown to be transferred via conjugation from P. multocida to Escherichia coli and from M. haemolytica and H. somni to P. multocida. ICE-mediated multidrug-resistant profiles of bacterial BRD pathogens could be a major detriment to many of the therapeutic antimicrobial strategies currently used to control BRD.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".