Ancestral Fluoxetine Exposure Sensitizes Zebrafish to Venlafaxine-Induced Reductions in Cortisol and Spawning
Bibliographic record
Abstract
Owing to the prevalence of depression during childbearing, mothers can be prescribed multiple antidepressants; however, little is known about the risk and consequences to the offspring or subsequent generations. Fluoxetine (FLX) is usually the first-line of pharmacological treatment for affective disorders in pregnant women, with venlafaxine (VEN) used as secondary treatment. Given that FLX and VEN readily cross the placenta, a fetus from a treated pregnant woman is potentially at risk of the endocrine disruptive effects of these chemicals. Pharmaceutical agents, including FLX and VEN, reach aquatic ecosystems through sewage release; thus, fish could also be inadvertently affected. We report the results from a 6-day FLX exposure during early zebrafish development to an environmentally relevant level (0.54 µg/L in water) and a concentration detected in the cord blood of FLX-treated pregnant women (54 µg/L in water). The FLX exposure reduced the stress response (arithmetic difference between the stress-induced and unstressed whole-body cortisol levels) in the adult female and male zebrafish, an effect that persisted for four generations. To model the possibility of a second antidepressant exposure, filial generation 4 was exposed to VEN (5 µg/L). We found that FLX exposure sensitized these descendants to VEN. VEN treatment further suppressed cortisol production in females and decreased spawning rates in adult pairs. This is an important demonstration that in an animal model, a brief ancestral exposure of great-great-grandparents to the selective serotonin reuptake inhibitor FLX will shape the physiological responses of future generations to the serotonin and norepinephrine reuptake inhibitor VEN.
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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 | 0.001 |
| 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".