Agonists for glutamate, acetylcholine, and orexin cause non-photic phase shifts when applied to the intergeniculate leaflet
Bibliographic record
Abstract
The intergeniculate leaflet (IGL) and its neurotransmitter, Neuropeptide Y, are both necessary and sufficient inputs to the SCN to mediate non-photic phase shifting of circadian rhythms. In this study we examined what arousal inputs might participate in activation of the IGL during a non-photic manipulation. The ACh agonist carbachol caused non-photic phase shifts when applied to the IGL at CT6, but blocking ACh muscarinic receptors in the IGL with atropine did not attenuate phase shifts to a 3 h sleep deprivation (SD) procedure during the midday. Orexin, an important arousal neuropeptide, densely innervates the IGL. Pretreatment with the dual OX1/OX2 receptor antagonist MK-6096 did not attenuate phase shifts to 3 h midday SD. When injected into the IGL alone, orexin produced small and inconsistent phase shifts that overall did not differ significantly from vehicle control. The glutamate agonist NMDA caused non-photic-like phase shifts when applied to the IGL. While a cocktail of both carbachol and NMDA inhibited each other's phase shifting effects, a cocktail that included orexin, carbachol and NMDA reversed this inhibition and yielded the largest phase shifts of all. This suggests that the IGL is likely activated by numerous convergent arousal inputs during a phase-shifting non-photic manipulation.
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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 | 0.001 |
| 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".