An analysis of the population genetics of potential multi-drug resistance in<i>Wuchereria bancrofti</i>due to combination chemotherapy
Bibliographic record
Abstract
Currently, annual mass treatments with albendazole (ABZ) plus ivermectin (IVM) or diethylcarbamazine (DEC) are administered under the Global Programme to Eliminate Lymphatic Filariasis (GPELF). Drug resistance against both ABZ and IVM is prevalent in nematodes of veterinary importance, raising awareness that if anthelmintic resistance were to develop among Wuchereria bancrofti populations, this would jeopardize GPELF's goals. Genetic structure was incorporated into an existing transmission dynamics model for lymphatic filariasis (LF) to investigate the potential development of concurrent resistance to ABZ and IVM. The resultant models explore the impact of different inheritance modes of resistance to ABZ and IVM on the likely risk of treatment failure under our model assumptions. Results indicate that under ABZ+IVM combination, selection for resistance to one drug is enhanced if resistance to the other drug is already present. Excess parasite homozygosity may increase selection for dominant IVM resistance via enhancing the frequency of recessive ABZ resistance. The model predicts that if multiple resistance genes are associated with different efficacy properties of a drug combination, then examining changes at single loci may be misleading. Sampling schemes in genetic epidemiological surveys investigating the frequency of an allele under selection should consider host age, as individuals of different ages may acquire parasites at different rates.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".