Contribution of transcriptional and translational mechanisms to the recovery aspect of sleep regulation
Bibliographic record
Abstract
Sleep parallels brain functioning and mental health. Neuronal activity during wakefulness leads to a subsequent increase in sleep intensity as measured using electroencephalographic slow-wave activity (SWA; index of neuronal synchrony in the low-frequency range). Wakefulness, and particularly prolonged wakefulness, also drives important changes in brain gene expression and changes in protein regulation. The role of these two cellular mechanisms in sleep-wake regulation has typically been studied independently, and their exact contribution to SWA remains poorly defined. In this review, we highlight that many transcriptional pathways driven by sleep deprivation are associated to protein regulation. We first describe the relationship between cytokines, clock genes, and markers of sleep need with an emphasis on transcriptional processes. Observations regarding the role of protein metabolism in sleep-wake regulation are then depicted while presenting interconnections between transcriptional and translational responses driven by sleep loss. Lastly, a manner by which this integrated response can feed back on neuronal network activity to determine sleep intensity is proposed. Overall, the literature supports that a complex cross-talk between transcriptional and translational regulation during prolonged wakefulness drives the changes in sleep intensity as a function of the sleep/wake history.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".