Dose-Specific Effects of Di-Isononyl Phthalate on the Endocannabinoid System and on Liver of Female Zebrafish
Bibliographic record
Abstract
Phthalates, used as plasticizers, have become a ubiquitous contaminant and have been reported for their potential to induce toxicity in living organisms. Among them, di-isononyl phthalate (DiNP) has been recently used to replace di(2-ethylhexyl) phthalate (DEHP). Nowadays, there is evidence that DiNP is an endocrine-disrupting chemical; however, little is known about its effects on the endocannabinoid system (ECS) and lipid metabolism. Hence, the aim of our study was to investigate the effects of DiNP on the ECS in zebrafish liver and brain and on hepatic lipid storage. To do so, adult female zebrafish were exposed to three concentrations (0.42 µg/L, 4.2 µg/L, and 42 µg/L) of DiNP via water for 3 weeks. Afterwards, we investigated transcript levels for genes involved in the ECS of the brain and liver as well as liver histology and image analysis, Fourier-transform infrared spectroscopy imaging, and measurement of endocannabinoid levels. Our results demonstrate that DiNP upregulates orexigenic signals and causes hepatosteatosis together with deregulation of the peripheral ECS and lipid metabolism. A decrease in the levels of ECS components at the central level was observed after exposure to the highest DiNP concentration tested. These findings suggest that replacement of DEHP with DiNP should be considered with caution because of observed adverse DiNP effects on aquatic organisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".