The ABCB Multidrug Resistance Proteins Do Not Contribute to Ivermectin Detoxification in the Colorado Potato Beetle, Leptinotarsa decemlineata (Say)
Bibliographic record
Abstract
The Colorado potato beetle, Leptinotarsa decemlineata (Say), is a significant agricultural pest that has developed resistance to many insecticides that are used to control it. Investigating the mechanisms of insecticide detoxification in this pest is important for ensuring its continued control, since they may be contributors to such resistance. Multidrug resistance (MDR) genes that code for the ABCB transmembrane efflux transporters are one potential source of insecticide detoxification activity that have not been thoroughly examined in L. decemlineata. In this study, we annotated the ABCB genes found in the L. decemlineata genome and then characterized the expression profiles across midgut, nerve, and Malpighian tubule tissues of the three full transporters identified. To investigate if these genes are involved in defense against the macrocyclic lactone insecticide ivermectin in this insect, each gene was silenced using RNA interference or MDR protein activity was inhibited using a chemical inhibitor, verapamil, before challenging the insects with a dose of ivermectin. Survival of the insects did not significantly change due to gene silencing or protein inhibition, suggesting that MDR transporters do not significantly contribute to defense against ivermectin in L. decemlineata.
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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) | 0.001 | 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".