Characterization of the electrophysiological properties of triple reuptake inhibitors on monoaminergic neurons
Bibliographic record
Abstract
Triple reuptake inhibitors represent a potential new class of antidepressant drugs that block norepinephrine (NE), dopamine (DA) and serotonin [5-hydroxytryptamine (5-HT)] transporters. The present in-vivo electrophysiological study was undertaken to determine the effects of the triple reuptake inhibitors SEP-225289 and DOV216303 on the neuronal activities of locus coeruleus (LC) NE, ventral tegmental area (VTA) DA and dorsal raphe (DR) 5-HT neurons. Administered acutely, SEP-225289 and DOV216303 dose-dependently decreased the spontaneous firing rate of LC NE, VTA DA and DR 5-HT neurons through the activation of α₂, D₂ and 5-HT(₁A) autoreceptors, respectively. Both compounds predominantly inhibited the firing rate of LC NE neurons while producing only a partial decrease in VTA DA and DR 5-HT neuronal discharge. SEP-225289 was equipotent at inhibiting 5-HT and NE transporters since it prolonged to the same extent the time required for a 50% recovery (RT₅₀) of the firing activity of dorsal hippocampus CA3 pyramidal neurons from the inhibition induced by microiontophoretic application of 5-HT and NE. Finally, in the presence of WAY100635, a 5-HT(₁A) receptor antagonist, SEP-225289 activated 5-HT neurons at doses that normally did not inhibit them. Taken together, the present results indicate that reciprocal interactions among NE, DA and 5-HT inputs need to be considered to anticipate the net effect of triple reuptake inhibitors on the enhancement of brain monoamine transmission. The results also suggest that the therapeutic action of triple reuptake inhibitors may be potentiated by antagonizing the cell body 5-HT(₁A) autoreceptors.
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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".