The Effects of Two Phthalate Esters on Toxicity and Gene Expression After an Acute Exposure of Oryzias latipes
Bibliographic record
Abstract
Phthalates are industrial chemicals used primarily to increase flexibility of high molecular-weight polymers. These compounds are not covalently bound to the polymeric matrix and are easily able to leach out and contaminate the environment. It is important to learn more about how these toxicants are affecting the environment and vertebrate health. Diisoheptyl phthalate (DIHepP) and Diisononyl phthalate (DINP) are among the substances being assessed by Environment Canada’s Chemical Management Plan, which assesses and manages chemicals that are used in commerce in Canada. This study examines the effect of DIHepP and DINP in the fish Oryzias latipes. At 1 day-post-hatching, fish were exposed to a range of environmentally relevant low concentrations of DIHepP (37-300 ppb ) and DINP (12-100 ppb DINP). Fish were exposed to phthalates under semi-static conditions for 7 days. None of the treatments significantly induced mortality or malformation. Gene expression analysis was performed in order to assess possible endocrine disruption of these compounds in O. latipes. The transcripts of interest include thyroid hormone-related genes (deiodinases, thyroid receptors alpha and beta), and steroid-related genes (cholesterol transporter, 5-reductases, androgen receptor, aromatase, and estrogen receptor). Data on mortality, malformation, and gene expression will be presented.
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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".