Protective Effects of Melatonin on Neurogenesis Impairment in Neurological Disorders and Its Relevant Molecular Mechanisms
Bibliographic record
Abstract
Neurogenesis is the process by which functional new neurons are generated from the neural stem cells (NSCs) or neural progenitor cells (NPCs). Increasing lines of evidence show that neurogenesis impairment is involved in different neurological illnesses, including mood disorders, neurogenerative diseases, and central nervous system (CNS) injuries. Since reversing neurogenesis impairment was found to improve neurological outcomes in the pathological conditions, it is speculated that modulating neurogenesis is a potential therapeutic strategy for neurological diseases. Among different modulators of neurogenesis, melatonin is a particularly interesting one. In traditional understanding, melatonin controls the circadian rhythm and sleep-wake cycle, although it is not directly involved in the proliferation and survival of neurons. In the last decade, it was reported that melatonin plays an important role in the regulation of neurogenesis, and thus it may be a potential treatment for neurogenesis-related disorders. The present review aims to summarize and discuss the recent findings regarding the protective effects of melatonin on the neurogenesis impairment in different neurological conditions. We also address the molecular mechanisms involved in the actions of melatonin in neurogenesis modulation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".