Assessing anthelmintic resistance risk in the post-genomic era: a proof-of-concept study assessing the potential for widespread benzimidazole-resistant gastrointestinal nematodes in North American cattle and bison
Bibliographic record
Abstract
As genomic research continues to improve our understanding of the genetics of anthelmintic drug resistance, the revolution in DNA sequencing technologies will provide increasing opportunities for large-scale surveillance for the emergence of drug resistance. In most countries, parasite control in cattle and bison has mainly depended on pour-on macrocyclic lactone formulations resulting in widespread ivermectin resistance. Consequently, there is an increased interest in using benzimidazole drugs which have been used comparatively little in cattle and bison in recent years. This situation, together with our understanding of benzimidazole resistance genetics, provides a practical opportunity to use deep-amplicon sequencing to assess the risk of drug resistance emergence. In this paper, we use deep-amplicon sequencing to scan for those mutations in the isotype-1 β-tubulin gene previously associated with benzimidazole resistance in many trichostrongylid nematode species. We found that several of these mutations occur at low frequency in many cattle and bison parasite populations in North America, suggesting increased use of benzimidazole drugs in cattle has the potential to result in widespread emergence of resistance in multiple parasite species. This work illustrates a post-genomic approach to large-scale surveillance of early emergence of anthelmintic resistance in the field.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".