Role of Glia in the Regulation of Sleep in Health and Disease
Bibliographic record
Abstract
Abstract Sleep is a naturally occurring physiological state that is required to sustain physical and mental health. Traditionally viewed as strictly regulated by top‐down control mechanisms, sleep is now known to also originate locally. Glial cells are emerging as important contributors to the regulation of sleep‐wake cycles, locally and among dedicated neural circuits. A few pioneering studies revealed that astrocytes and microglia may influence sleep pressure, duration as well as intensity, but the precise involvement of these two glial cells in the regulation of sleep remains to be fully addressed, across contexts of health and disease. In this overview article, we will first summarize the literature pertaining to the role of astrocytes and microglia in the regulation of sleep under normal physiological conditions. Afterward, we will discuss the beneficial and deleterious consequences of glia‐mediated neuroinflammation, whether it is acute, or chronic and associated with brain diseases, on the regulation of sleep. Sleep disturbances are a main comorbidity in neurodegenerative diseases, and in several brain diseases that include pain, epilepsy, and cancer. Identifying the relationships between glia‐mediated neuroinflammation, sleep‐wake rhythm disruption and brain diseases may have important implications for the treatment of several disorders. © 2020 American Physiological Society. Compr Physiol 10:687‐712, 2020.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".