The Effect of Paroxetine on 5-HT<sub>2A</sub> Receptors in Depression: An [<sup>18</sup>F]Setoperone PET Imaging Study
Bibliographic record
Abstract
OBJECTIVE: In the cortex of animals, serotonin (5-HT) levels increase after several weeks of treatment with selective serotonin reuptake inhibitors (SSRIs). Studies using an intrasubject design to examine the effects of SSRI treatment on 5-HT(2A) receptors in the cortex of drug-free depressed patients are needed. In theory, agonist stimulation of 5-HT(2A) receptors could be relevant to SSRI treatment by promoting neuronal growth and survival as well as direct elevation of mood. The objective of this study was to evaluate the effect of 6 weeks of paroxetine treatment on 5-HT(2A) receptors in depressed patients. METHOD: After a medication-free period of at least 3 months, 19 depressed patients were treated for 6 weeks with paroxetine, 20 mg/day. The authors used [(18)F]setoperone and positron emission tomography to assess 5-HT(2A) receptor binding potential in the patients before and after treatment and in 19 age-matched healthy subjects. RESULTS: 5-HT(2A) binding potential declined with age in all cortical regions in the depressed and healthy subjects. There was a significant interaction between age and treatment effect on 5-HT(2A) binding potential in all cortical regions. Subjects aged 20 to 30 years had a 10% decrease in 5-HT(2A) binding potential after treatment, whereas subjects aged 30 to 40 had no change. No regional differences in 5-HT(2A) binding potential between depressed and healthy subjects were found. CONCLUSIONS: 5-HT(2A) receptors down-regulate in young depressed subjects after treatment with paroxetine, but this down-regulation attenuates with age. This suggests that over 6 weeks paroxetine treatment increases 5-HT agonism on 5-HT(2A) receptors in the cortex of young patients with depression.
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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) | 0.001 | 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".