Protective Roles of Melatonin in Alzheimer's Disease: A Review of Experimental and Clinical Research
Bibliographic record
Abstract
: Alzheimer's disease (AD) stands as the most prevalent neurodegenerative disorder, marked by neuronal loss, synaptic dysfunction, atrophy in various brain regions, cognitive decline, dementia, the production of β-amyloid (Aβ) peptide, and the presence of neurofibrillary tangles. Melatonin, also known as N-acetyl 5-methoxy tryptamine, is a hormone regulated by circadian rhythms and plays a crucial role in certain neurodegenerative conditions, including AD. In individuals with AD, alterations have been observed in the pineal gland hormone melatonin (MLT), the activity of enzymes associated with MLT synthesis, and the density of MT1 receptors in the suprachiasmatic nucleus (SCN) of the hypothalamus. The growing body of literature indicates a rising interest in utilizing MLT for AD intervention. Melatonin has shown several potential benefits in AD, such as mitigating mitochondrial dysfunction, reducing Aβ toxicity, scavenging free radicals, and even ameliorating circadian dysregulation, which includes addressing issues like sundowning and sleep disturbances. Recent studies suggest that MLT might serve as a potential biomarker for assessing the severity and progression of AD. This paper aimed to provide an overview of recent research on three key aspects: (1) MLT physiology, (2) the role of MLT in the learning and memory processes, and (3) an exploration of studies investigating the role of MLT in AD.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".