Fluoxetine-induced alterations in human platelet serotonin transporter expression: serotonin transporter polymorphism effects
Bibliographic record
Abstract
OBJECTIVE: Long-term antidepressant drug exposure may regulate its target molecule - the serotonin transporter (SERT). This effect could be related to an individual's genotype for an SERT promoter polymorphism (human serotonin transporter coding [5-HTTLPR]). We aimed to determine the effects of fluoxetine exposure on human platelet SERT levels. METHOD: We harvested platelet samples from 21 healthy control subjects. The platelets were maintained alive ex vivo for 24 hours while being treated with 0.1 muM fluoxetine or vehicle. The effects on SERT immunoreactivity (IR) were then compared. Each individual's SERT promoter genotype was also determined to evaluate whether fluoxetine effects on SERT were related to genotype. RESULTS: Fluoxetine exposure replicably altered SERT IR within individuals. Both the magnitude and the direction of effect were related to a person's SERT genotype. People who were homozygous for the short gene (SS) displayed decreased SERT IR, whereas those who were homozygous for the long gene (LL) demonstrated increased SERT IR. A mechanistic experiment suggested that some individuals with the LL genotype might experience increased conversion of complexed SERT to primary SERT during treatment. CONCLUSIONS: These preliminary results suggest that antidepressant effects after longer-term use may include changes in SERT expression levels and that the type and degree of effect may be related to the 5-HTTLPR polymorphism.
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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) | 0.002 | 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".