Pharmaceuticals as Neuroendocrine Disruptors: Lessons Learned from Fish on Prozac
Bibliographic record
Abstract
Pharmaceuticals are increasingly detected in a variety of aquatic systems. One of the most prevalent environmental pharmaceuticals in North America and Europe is the antidepressant fluoxetine, a selective serotonin reuptake inhibitor (SSRI) and the active ingredient of Prozac. Usually detected in the range below 1 μg/L, fluoxetine and its active metabolite norfluoxetine are found to bioaccumulate in wild-caught fish, particularly in the brain. This has raised concerns over potential disruptive effects of neuroendocrine function in teleost fish, because of the known role of serotonin (5-HT) in the modulation of diverse physiological processes such as reproduction, food intake and growth, stress and multiple behaviors. This review describes the evolutionary conservation of the 5-HT transporter (the therapeutic target of SSRIs) and reviews the disruptive effects of fluoxetine on several physiological endpoints, including involvement of neuroendocrine mechanisms. Studies on the goldfish, Carassius auratus, whose neuroendocrine regulation of reproduction and food intake are well characterized, are described and represent a reliable model to study neuroendocrine disruption. In addition, fish studies investigating the effects of fluoxetine, not only on reproduction and food intake, but also on stress and behavior, are discussed to complement the emerging picture of neuroendocrine disruption of physiological systems in fish exposed to fluoxetine. Environmental relevance and key lessons learned from the effects of the antidepressant fluoxetine on fish are highlighted and may be helpful in designing targeted approaches for future risk assessments of pharmaceuticals disrupting the neuroendocrine system 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".