Challenges associated with curcumin therapy in Alzheimer disease
Bibliographic record
Abstract
Curcumin, the phytochemical agent in the spice turmeric, which gives Indian curry its yellow colour, is also a traditional Indian medicine. It has been used for millennia as a wound-healing agent and for treating a variety of ailments. The antioxidant, anti-inflammatory, antiproliferative and other properties of curcumin have only recently gained the attention of modern pharmacology. The mechanism of action of curcumin is complex and multifaceted. In part, curcumin acts by activating various cytoprotective proteins that are components of the phase II response. Over the past decade, research with curcumin has increased significantly. In vitro and in vivo studies have demonstrated that curcumin could target pathways involved in the pathophysiology of Alzheimer disease (AD), such as the β-amyloid cascade, tau phosphorylation, neuroinflammation or oxidative stress. These findings suggest that curcumin might be a promising compound for the development of AD therapy. However, its insolubility in water and poor bioavailability have limited clinical trials and its therapeutic applications. To be effective as a drug therapy, curcumin must be combined with other drugs, or new delivery strategies need to be developed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".