Micronucleus Test Reveals Genotoxic Effects in Bats Associated with Agricultural Activity
Bibliographic record
Abstract
Bats play a vital role in our ecosystems and economies as natural pest-control agents, seed dispersers, and pollinators. Agricultural intensification, however, can impact bats foraging near crops, affecting the ecosystem services they provide. Exposure to pesticides, for example, may induce chromosome breakage or missegregation that can result in micronucleus formation. Detection of micronuclei is a simple, inexpensive, and relatively minimally invasive technique commonly used to evaluate chemical genotoxicity but rarely applied to assess wildlife genotoxic effects. We evaluated the suitability of the micronucleus test as a biomarker of genotoxicity for biomonitoring field studies in bats. We collected blood samples from insectivorous bats roosting in caves surrounded by different levels of disturbance (agriculture, human settlements) in Colima and Jalisco, west central Mexico. Then, we examined the frequency of micronucleus inclusions in erythrocytes using differentially stained blood smears. Bats from caves surrounded by proportionately more (53%) land used for agriculture and irrigated year-round had higher micronucleus frequency than bats from a less disturbed site (15% agriculture). We conclude that the micronucleus test is a sensitive method to evaluate genotoxic effects in free-ranging bats and could provide a useful biomarker for evaluating risk of exposure in wild populations. Environ Toxicol Chem 2021;40:202-207. © 2020 SETAC.
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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 | 0.001 | 0.001 |
| 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".