Toxicity of representative mixture of five rare earth elements in juvenile rainbow trout (Oncorhynchus mykiss) juveniles
Bibliographic record
Abstract
Abstract Rare earth elements (REEs) are contaminants of increasing interest due to intense mining activities for commercial purposes and ultimately released in the environment. We exposed juvenile rainbow trout (Oncorhynchus mykiss) to a representative mixture of the five most abundant REEs for 96 h at concentrations similar found in lakes contaminated by mining activities at 0.1, 1, 10, and 100X whereas the 1x mixture contained cerium (Ce, 280 μg/L), lanthanum (La, 140 μg/L), neodymium (Nd, 120 μg/L), praseodymium (Pr, 28 μg/L), and samarium (Sm, 23 μg/L). We investigated the expression of 14 genes involved in oxidative stress, DNA repair, tissue growth/proliferation, protein chaperoning, xenobiotic biotransformation, and ammonia metabolism in the liver. In addition, DNA damage, oxidative stress (lipid peroxidation or LPO), inflammation (cyclooxygenase or COX activity), detoxification mechanisms (glutathione-S-transferase activity or GST), and labile zinc were determined in gills. The data revealed that genes involved in oxidative stress-catalase (cat), heat shock proteins 70 (hsp70), and glutamate dehydrogenase (glud) were upregulated while glutathione S-transferase (gst) and metallothionein (mt) gene expressions were downregulated. The mixture was genotoxic and increased labile Zn in gills of exposed trout. These changes occurred at concentrations 600 times lower than the LC50 for this mixture indicating effects below the 1X concentration. Based on principal component analysis and concentration-dependent reponses, the following sublethal effects were considered the most important/significant: DNA strand breaks (genotoxicity), labile Zn, cat, gst, hsp70, sparc, mt, and glud. These effects of fish juveniles are likely to occur in environments under the influence of mining activities.
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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".