<i>C. elegans</i> as an <i>in vivo</i> model system for the phenotypic drug discovery for treating paraquat poisoning
Bibliographic record
Abstract
Background Paraquat (PQ) is an effective and widely used herbicide and causes numerous fatalities by accidental or voluntary ingestion. However, neither the final cytotoxic mechanism nor effective treatments for PQ poisoning have been discovered. Phenotypic drug discovery (PDD), which does not rely on the molecular mechanism of the diseases, is having a renaissance in recent years owing to its potential to address the incompletely understood complexity of diseases. Herein, the C. elegans PDD model was established to pave the way for the future phenotypic discovery of potential agents for treating PQ poisoning. Methods C. elegans were treated with PQ-containing solid medium followed by statistical analysis of worm survival, pharyngeal pumping, and movement ability. Furthermore, coenzyme Q10 (CoQ10) was used to test the C. elegans model of PQ poisoning by measuring the levels of reactive oxygen species (ROS) and malondialdehyde (MDA), mitochondrial morphology, and worm survival rate. Additionally, we used the classic mice model of PQ intoxication to evaluate the validity of the C. elegans model of PQ poisoning by measuring the effect of CoQ10 as a potential antidote for PQ poisoning. Results In the C. elegans model of PQ poisoning, 5 mg/mL PQ increased the levels of ROS, MDA content, mitochondrial fragments, which significantly shortened the lifespan, while CoQ10 alleviated these phenotypes. In the mice model of PQ poisoning, CoQ10 increased the chance of survival in PQ poisoned mice while reducing ROS, MDA content in lung tissue and inhibiting PQ-induced lung edema. Moreover, CoQ10 alleviated the lung morphopathological changes induced by PQ. Conclusion Here we established a C. elegans model of PQ poisoning, whose validity was confirmed by the classic mice model of PQ intoxication.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".