Circadian control of pain and neuroinflammation
Bibliographic record
Abstract
The importance of a neuroinflammatory response to the development and maintenance of inflammatory and neuropathic pain have been highlighted in recent years. Inflammatory cells contributing to this response include circulating immune cells such as monocytes, T and B lymphocytes, and neutrophils, as well as microglia in the central nervous system. Pain signals are transmitted via sensory neurons in the peripheral nervous system, which express various receptors and channels that respond to mediators secreted from these inflammatory cells. Chronobiological rhythms, which include the 24-hr circadian cycle, have recently been shown to regulate both nervous and immune cell activity and function. This review examines the current literature on chronobiological control of neuroinflammatory processes, with a focus on inflammatory and neuropathic pain states. While the majority of this work has stemmed from observational studies in humans, recent advances in using animal models have highlighted distinct mechanisms underlying these interactions. Better understanding interactions between the circadian and neuroimmune systems can help guide the development of new treatments and provide improved care for patients suffering from acute and chronic pain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".