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Record W4410876092 · doi:10.1093/sleepadvances/zpaf031

The effects of acute trazodone administration on sleep in mice

2025· article· en· W4410876092 on OpenAlexaff
Mayuko Arai, Brianne A. Kent

Bibliographic record

VenueSLEEP Advances · 2025
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTrazodoneAdministration (probate law)Sleep (system call)MedicineAnesthesiaInternal medicinePolitical scienceComputer scienceAntidepressant

Abstract

fetched live from OpenAlex

Study Objectives: Trazodone is an antidepressant with robust hypnotic effects, frequently prescribed off-label to treat insomnia. Trazodone has gained recent attention in the context of neurodegenerative diseases because sleep has been proposed as a novel target for disease-modifying therapeutics. Preclinical research in rodents examining the effects of trazodone on sleep is limited, so here we aimed to develop a translationally focused protocol to study the sleep-promoting effects of trazodone in mice. Methods: = 15; females = 6; age 10-13 months). Mice were dosed with trazodone for 6 consecutive nights, while being recorded with intracranially implanted 2-channel electroencephalogram (EEG) and electromyography (EMG). EEG/EMG recordings were analyzed for time spent in each vigilance state and power spectra. Results: A single dose of trazodone, administered prior to the onset of the 12-h rest phase, dose-dependently increased non-rapid eye movement (NREM) sleep and delta power during NREM sleep, at the expense of rapid eye movement (REM) sleep. These effects on sleep persisted after six consecutive days of dosing, albeit to a lesser extent. Conclusions: We have validated a novel voluntary oral administration protocol for trazodone use in mice and have shown that trazodone effectively promotes NREM in mice. Our novel protocol will be useful for future research investigating the effects of trazodone on sleep in mouse models of disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.318
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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