An Exploration into the Non-Photic Influence of Acetylcholinesterase Inhibitors on Circadian Rhythms
Bibliographic record
Abstract
While light is the dominant zeitgeber (“time giver”) for the circadian system, nonphotic cues, such as exercise and arousal, also affect circadian rhythmicity. Our lab has reported that cholinergic innervation of the suprachiasmatic nucleus arising from the basal forebrain is both necessary and sufficient for phase shifting circadian rhythms in a nonphotic manner. Therefore, the present study investigated a new avenue for modulating cholinergic activity in a less invasive manner by testing whether enhancing acetylcholine neurotransmission with acetylcholinesterase (AChE) inhibitors will cause nonphotic-like phase shifts of the circadian system. Three different AChE inhibitors were explored (Donepezil, Rivastigmine, and Tacrine) at varying doses. First, Syrian hamsters were housed in constant darkness (DD), and were administered an intraperitoneal (IP) injection of one of the AChE inhibitors or vehicle control in counterbalanced order six hours before their activity onset, a phase when nonphotic treatments elicit phase advances. The second hypothesis was that nonphotic phase shifts elicited by AChE inhibitor administration requires acetylcholine activity at the SCN. Hamsters were given a microinjection of either saline or the acetylcholine antagonist Atropine to the SCN 10 minutes prior to a CT6 injection of donepezil (10mg/kg). In experiment one, Donepezil at 10mg/kg had the most robust phase advances relative to the controls. In experiment two, the results were mixed with no significant differences were found between conditions, including the baseline measurement of Donepezil/control.
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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".