Inhibiting nighttime melatonin and boosting cortisol increase patrolling monocytes, phagocytosis, and myelination in a murine model of multiple sclerosis
Bibliographic record
Abstract
Conflicting results on melatonin synthesis in multiple sclerosis (MS) have been reported due to variabilities in patient lifestyles, which are not considered when supplementing melatonin. Since melatonin acts through its receptors, we identified melatonin receptors in oligodendrocytes (OLs) in the corpus callosum, where demyelination occurs; the subventricular zone, where neural stem/progenitor cells (NSPCs) are located; and the choroid plexus, which functions as a blood-cerebrospinal fluid barrier. Moreover, using chimeric mice, resident macrophages were found to express melatonin receptors, whereas bone marrow-derived macrophages lost this expression in the demyelinated brain. Next, we showed that cuprizone-fed mice, which is an MS model, tended to have increased melatonin levels. While we used different approaches to alter the circadian rhythm of melatonin and cortisol, only the constant light approach increased NSPC proliferation and differentiation to oligodendrocyte precursor cells (OPCs), OPCs maturation to OLs and recruitment to the site of demyelination, the number of patrolling monocytes, and phagocytosis. In contrast, constant darkness and exogenous melatonin exacerbated these events and amplified monocyte infiltration. Therefore, melatonin should not be considered a universal remedy, as is currently claimed. Our data emphasize the importance of monitoring melatonin/cortisol oscillations in each MS patient by considering diet and lifestyle to avoid melatonin overdose.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".