Food-Borne Exposure of Juvenile Rainbow Trout (<i>Oncorhynchus mykiss</i>) to Benzotriazole Ultraviolet Stabilizers Alone and in Mixture Induces Specific Transcriptional Changes
Bibliographic record
Abstract
Abstract Benzotriazole ultraviolet-stabilizers (BZT-UVs) are commonly used as additives to protect from light-induced degradation in a variety of consumer goods. Despite their widespread presence in aquatic ecosystems, information on the effects of these compounds remains largely unknown. The objectives of the present study were to evaluate the chronic effects of 2 BZT-UVs alone and in a mixture, 2-(2H-benzotriazol-2-yl)-4,6-bis(1-methyl-1-phenylethyl)phenol (UV-234) and 2-(2H-benzotriazol-2-yl)-4,6-di-tert-pentylphenol (UV-328), in juvenile rainbow trout (Oncorhynchus mykiss) chronically exposed (for 28 d) through the diet. Chemical analyses of livers from exposed trout suggested liver accumulation and potential metabolism of the 2 compounds. Hepatic RNA-sequencing analyses revealed specific effects of each compound on gene transcription profiles; UV-234 affected mainly genes involved in cellular metabolism, whereas UV-328 induced the transcription of ribosomal proteins and downregulated genes involved in immune responses. Both compounds regulated iron homeostasis genes in an opposite manner. The mixture of both BZT-UVs did not produce significant evidence of additive or synergistic effects. Environ Toxicol Chem 2020;39:852–862. © 2020 Her Majesty the Queen in Right of Canada. Environmental Toxicology and Chemistry © 2020 SETAC Abstract Benzotriazole ultraviolet (UV) stabilizers UV-234 and UV-328 induce transcriptional changes in juvenile rainbow trout exposed chronically through the diet, mainly affecting genes involved in iron homeostasis. ALB1 = serum albumin; AMBP = α-1-microglobulin; Fe = iron; HPX = hemopexin; TRF = transferrin.
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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".