The Effects of Essential and Non-Essential Metal Toxicity in the Drosophila melanogaster Insect Model: A Review
Bibliographic record
Abstract
The biological effects of environmental metal contamination are important issues in an industrialized, resource-dependent world. Different metals have different roles in biology and can be classified as essential if they are required by a living organism (e.g., as cofactors), or as non-essential metals if they are not. While essential metal ions have been well studied in many eukaryotic species, less is known about the effects of non-essential metals, even though essential and non-essential metals are often chemically similar and can bind to the same biological ligands. Insects are often exposed to a variety of contaminated environments and associated essential and non-essential metal toxicity, but many questions regarding their response to toxicity remain unanswered. Drosophila melanogaster is an excellent insect model species in which to study the effects of toxic metal due to the extensive experimental and genetic resources available for this species. Here, we review the current understanding of the impact of a suite of essential and non-essential metals (Cu, Fe, Zn, Hg, Pb, Cd, and Ni) on the D. melanogaster metal response system, highlighting the knowledge gaps between essential and non-essential metals in D. melanogaster. This review emphasizes the need to use multiple metals, multiple genetic backgrounds, and both sexes in future studies to help guide future research towards better understanding the effects of metal contamination in general.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".