The Mechanisms of Action of Curcumin in Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder of the elderly. As the prevalence of AD rises in the 21st century, there is an urgent need for the development of effective pharmacotherapies. Currently, drug treatments target the symptoms of the disease and do not modify or halt the disease progress. Thus, natural compounds have been investigated for their ability to treat AD. This review examines the efficacy of curcumin, a polyphenol derived from turmeric herb, to treat AD. We summarize the in vivo and in vitro research describing the mechanisms of action in which curcumin modifies AD pathology: curcumin inhibits the formation and promotes the disaggregation of amyloid-β plaques, attenuates the hyperphosphorylation of tau and enhances its clearance, binds copper, lowers cholesterol, modifies microglial activity, inhibits acetylcholinesterase, mediates the insulin signaling pathway, and is an antioxidant. In conclusion, curcumin has the potential to be more efficacious than current treatments. However, its usefulness as a therapeutic agent may be hindered by its low bioavailability. If the challenge of low bioavailability is overcome, curcumin-based medications for AD may be in the horizon.
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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 | 0.002 |
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".