Distinct modulatory roles of sigma receptor subtypes on glutamatergic responses in the dorsal hippocampus
Bibliographic record
Abstract
Sigma ligands have been previously shown to modulate the N-methyl-D-aspartate (NMDA) response in the dorsal hippocampus, such that low doses of sigma agonists dose-dependently potentiate the response. Recent studies with the sigma ligand 4-IBP found it to act differently from the sigma ligands (+)-pentazocine and DTG in the modulation of 5-HT firing activity in the dorsal raphe nucleus (DRN), as its effects were not blocked by the sigma antagonists which reversed those of (+)-pentazocine or DTG. Thus, this study set out to characterize 4-IBP's action at sigma receptors using the hippocampal paradigm of sigma ligand activity. Interestingly, we found that in 50% of the neurons recorded, 4-IBP (20 microg/kg i.v.) produced a potentiation of both NMDA- and quisqualate (QUIS)-induced responses. In the other 50% of neurons, 4-IBP produced an attenuation of both QUIS and NMDA responses. The sigma1 antagonist NE-100 blocked the reduction induced by 4-IBP, while the nonselective sigma antagonist haloperidol blocked all responses induced by 4-IBP. These data suggest that, in this model, 4-IBP may be acting as an agonist or inverse agonist of sigma receptors. Furthermore, the initial responses to NMDA and QUIS were higher in the group in which 4-IBP induced an attenuation of the firing activity. This suggests a modulatory role for 4-IBP on glutamatergic neurotransmission in the hippocampus, which appears to involve two distinct pathways, mediated by different sigma1 receptor subtypes, an NE-100 and haloperidol-sensitive sigma1 receptor, and an NE-100-insensitive, haloperidol-sensitive sigma1 receptor. This modulatory role has implications for disorders that involve glutamatergic transmission in the hippocampus.
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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".