Naturally Occurring Antioxidant Therapy in Alzheimer’s Disease
Bibliographic record
Abstract
It is estimated that the prevalence rate of Alzheimer's disease (AD) will double by the year 2040. Although currently available treatments help with symptom management, they do not prevent, delay the progression of, or cure the disease. Interestingly, a shared characteristic of AD and other neurodegenerative diseases and disorders is oxidative stress. Despite profound evidence supporting the role of oxidative stress in the pathogenesis and progression of AD, none of the currently available treatment options address oxidative stress. Recently, attention has been placed on the use of antioxidants to mitigate the effects of oxidative stress in the central nervous system. In preclinical studies utilizing cellular and animal models, natural antioxidants showed therapeutic promise when administered alone or in combination with other compounds. More recently, the concept of combination antioxidant therapy has been explored as a novel approach to preventing and treating neurodegenerative conditions that present with oxidative stress as a contributing factor. In this review, the relationship between oxidative stress and AD pathology and the neuroprotective role of natural antioxidants from natural sources are discussed. Additionally, the therapeutic potential of natural antioxidants as preventatives and/or treatment for AD is examined, with special attention paid to natural antioxidant combinations and conjugates that are currently being investigated in human clinical trials.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".