A review of the current state of knowledge on sex differences in sleep and circadian phenotypes in rodents
Bibliographic record
Abstract
Sleep is a vital part of our lives as it is required to maintain health and optimal cognition. In humans, sex differences are relatively well-established for many sleep phenotypes. However, precise differences in sleep phenotypes between male and female rodents are less documented. The main goal of this article is to review sex differences in sleep architecture and electroencephalographic (EEG) activity during wakefulness and sleep in rodents. The effects of acute sleep deprivation on sleep duration and EEG activity in male and female rodents will also be covered, in addition to sex differences in specific circadian phenotypes. When possible, the contribution of the female estrous cycle to the observed differences between males and females will be described. In general, male rodents spend more time in non-rapid eye movement sleep (NREMS) in comparison to females, while other differences between sexes in sleep phenotypes are species- and estrous cycle phase-dependent. Altogether, the review illustrates the need for a sex-based perspective in basic sleep and circadian research, including the consideration of sex chromosomes and gonadal hormones in sleep and circadian phenotypes.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".