Selective Serotonin Reuptake Inhibitors Potentiate the Rapid Antidepressant-Like Effects of Serotonin4 Receptor Agonists in the Rat
Bibliographic record
Abstract
BACKGROUND: We have recently reported that serotonin(4) (5-HT(4)) receptor agonists have a promising potential as fast-acting antidepressants. Here, we assess the extent to which this property may be optimized by the concomitant use of conventional antidepressants. METHODOLOGY/PRINCIPAL FINDINGS: We found that, in acute conditions, the 5-HT(4) agonist prucalopride was able to counteract the inhibitory effect of the selective serotonin reuptake inhibitors (SSRI) fluvoxamine and citalopram on 5-HT neuron impulse flow, in Dorsal Raphé Nucleus (DRN) cells selected for their high (>1.8 Hz) basal discharge. The co-administration of both prucalopride and RS 67333 with citalopram for 3 days elicited an enhancement of DRN 5-HT neuron average firing rate, very similar to what was observed with either 5-HT(4) agonist alone. At the postsynaptic level, this translated into the manifestation of a tonus on hippocampal postsynaptic 5-HT(1A) receptors, that was two to three times stronger when the 5-HT(4) agonist was combined with citalopram. Similarly, co-administration of citalopram synergistically potentiated the enhancing effect of RS 67333 on CREB protein phosphorylation within the hippocampus. Finally, in the Forced Swimming Test, the combination of RS 67333 with various SSRIs (fluvoxamine, citalopram and fluoxetine) was more effective to reduce time of immobility than the separate administration of each compound. CONCLUSIONS/SIGNIFICANCE: These findings strongly suggest that the adjunction of an SSRI to a 5-HT(4) agonist may help to optimize the fast-acting antidepressant efficacy of the latter.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".