SLEEP REGULATION IN THE STOP-NULL MOUSE MODEL OF SCHIZOPHRENIA
Bibliographic record
Abstract
Sleep disturbances are common in patients with schizophrenia. Mice lacking the cytoskeletal-associated protein Stable Tubule Only Polypeptide (STOP) display cognitive, behavioural and neurobiological deficits that mimic those seen in schizophrenia, but there is little evidence of sleep changes in these mice. To investigate their sleep patterns, electroencephalogram (EEG) and electromyogram were recorded under a 12:12 light:dark cycle in adult male STOP-null (KO; n=7) and wild-type (WT; n=8) mice, during a 24 h baseline period, followed by 6 h of sleep deprivation, and a 24 h recovery period. In the baseline period, KO mice spent more time awake and less time in non-rapid eye movement (NREM) and REM sleep compared to WT mice. Particularly in the dark phase, KO mice had more wake and NREM sleep episodes, and shorter NREM and REM sleep episodes relative to WT mice. Following sleep deprivation, during the first 12 h of recovery (i.e. dark phase), both groups showed similar increases in NREM and REM sleep amounts and NREM EEG delta power relative to corresponding baseline periods. These findings indicate that the STOP-null mice sleep less and their sleep is more fragmented compared to WT mice. These features are consistent with the sleep abnormalities found in individuals with schizophrenia.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".