Dopaminergic Regulation of Sleep and Cataplexy in a Murine Model of Narcolepsy
Bibliographic record
Abstract
STUDY OBJECTIVES: To determine if the dopaminergic system modulates cataplexy, sleep attacks and sleep-wake behavior in narcoleptic mice. DESIGN: Hypocretin/orexin knockout (i.e., narcoleptic) and wild-type mice were administered amphetamine and specific dopamine receptor modulators to determine their effects on sleep, cataplexy and sleep attacks. PATIENTS OR PARTICIPANTS: Hypocretin knockout (n = 17) and wild-type mice (n = 21). INTERVENTIONS: Cataplexy, sleep attacks and sleep-wake behavior were identified using electroencephalogram, electromyogram and videography. These behaviors were monitored for 4 hours after an i.p. injection of saline, amphetamine and specific dopamine receptor modulators (D1- and D2-like receptor modulators). MEASUREMENTS AND RESULTS: Amphetamine (2 mg/kg), which increases brain dopamine levels, decreased sleep attacks and cataplexy by 61% and 67%, suggesting that dopamine transmission modulates such behaviors. Dopamine receptor modulation also had powerful effects on sleep attacks and cataplexy. Activation (SKF 38393; 20 mg/kg) and blockade (SCH 23390; 1 mg/kg) of D1-like receptors decreased and increased sleep attacks by 77% and 88%, without affecting cataplexy. Pharmacological activation of D2-like receptors (quinpirole; 0.5 mg/kg) increased cataplectic attacks by 172% and blockade of these receptors (eticlopride; 1 mg/kg) potently suppressed them by 97%. Manipulation of D2-like receptors did not affect sleep attacks. CONCLUSIONS: We show that the dopaminergic system plays a role in regulating both cataplexy and sleep attacks in narcoleptic mice. We found that cataplexy is modulated by a D2-like receptor mechanism, whereas dopamine modulates sleep attacks by a D1-like receptor mechanism. These results support a role for the dopamine system in regulating sleep attacks and cataplexy in a murine model of narcolepsy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".