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Record W2605340136 · doi:10.1371/journal.pntd.0005502

The novel nematicide wact-86 interacts with aldicarb to kill nematodes

2017· article· en· W2605340136 on OpenAlexafffund
Andrew R. Burns, Rachel Bagg, May Yeo, Genna M. Luciani, Michael R Schertzberg, Andy G. Fraser, Peter J. Roy

Bibliographic record

VenuePLoS neglected tropical diseases · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of TorontoUniversity of Minnesota
KeywordsAldicarbBiologyToxicologyCaenorhabditis elegansNematodePesticidePharmacologyBiochemistryEcology

Abstract

fetched live from OpenAlex

Parasitic nematodes negatively impact human and animal health worldwide. The market withdrawal of nematicidal agents due to unfavourable toxicities has limited the available treatment options. In principle, co-administering nematicides at lower doses along with molecules that potentiate their activity could mitigate adverse toxicities without compromising efficacy. Here, we screened for new small molecules that interact with aldicarb, which is a highly effective treatment for plant-parasitic nematodes whose toxicity hampers its utility. From our collection of 638 worm-bioactive compounds, we identified 20 molecules that interact positively with aldicarb to either kill or arrest the growth of the model nematode Caenorhabditis elegans. We investigated the mechanism of interaction between aldicarb and one of these novel nematicides called wact-86. We found that the carboxylesterase enzyme GES-1 hydrolyzes wact-86, and that the interaction is manifested by aldicarb's inhibition of wact-86's metabolism by GES-1. This work demonstrates the utility of C. elegans as a platform to search for new molecules that can positively interact with industrial nematicides, and provides proof-of-concept for prospective discovery efforts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.228
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2017
Admission routes2
Has abstractyes

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