Pharmaceuticals as Neuroendocrine Disruptors: Lessons Learned from Fish on Prozac
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it