Anthelmintic resistance: markers for resistance, or susceptibility?
Bibliographic record
Abstract
The Consortium for Anthelmintic Resistance and Susceptibility (CARS) brings together researchers worldwide, with a focus of advancing knowledge of resistance and providing information on detection methods and treatment strategies. Advances in this field suggest mechanisms and features of resistance that are shared among different classes of anthelmintic. Benzimidazole resistance is characterized by specific amino acid substitutions in beta-tubulin. If present, these substitutions increase in frequency upon drug treatment and lead to treatment failure. In the laboratory, sequence substitutions in ion-channels can contribute to macrocyclic lactone resistance, but there is little evidence that they are significant in the field. Changes in gene expression are associated with resistance to several different classes of anthelmintic. Increased P-glycoprotein expression may prevent drug access to its site of action. Decreased expression of ion-channel subunits and the loss of specific receptors may remove the drug target. Tools for the identification and genetic analysis of parasitic nematodes and a new online database will help to coordinate research efforts in this area. Resistance may result from a loss of sensitivity as well as the appearance of resistance. A focus on the presence of anthelmintic susceptibility may be as important as the detection of resistance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".