Heavy Metal Exposure Disrupts Electrotactic Behavior in <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
ABSTRACT Environmental toxicants such as heavy metals can profoundly impact organismal physiology, yet sensitive and rapid behavioral assays to quantify such effects are limited. Here, we employed a microfluidic-based electrotaxis assay to systematically evaluate the impact of chronic exposure to metal salts on the electrotactic swimming behavior of the nematode Caenorhabditis elegans . We report that exposure to Ag, Hg, MeHg, Cu, Mn, and Pb significantly alters electrotaxis speed. Notably, Cu required higher concentrations to induce phenotypes, whereas Ag and Hg disrupted behavior at lower doses. Three other metals (Ni, Fe, and Cd) did not elicit marked electrotaxis defects. Bioaccumulation analysis of Cu, Ag, Hg, and MeHg via ICP-MS revealed that MeHg was present in highest amount in worms. Consistent with this, MeHg showed the highest toxicity. We also found dopaminergic neurodegeneration in most metal-exposed animals, correlating with behavioral impairments. Although heat shock factor-1 (HSF-1) and its downstream target hsp-16.2 were induced by some metals, their expression did not consistently correlate with electrotaxis defects. Moreover, metal-induced impairments persisted even after recovery on toxin-free media, indicating possible irreversible damage. Our findings establish electrotaxis in C. elegans as a robust, non-invasive assay to assess neurobehavioral toxicity and demonstrate its utility for detecting sub-lethal impacts of environmental metals.
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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".