Curcumin and neurodegenerative diseases
Bibliographic record
Abstract
Over the last 10 years curcumin has been reported to be effective against a wide variety of diseases and is characterized as having anticarcinogenic, hepatoprotective, thrombosuppressive, cardioprotective, antiarthritic, and anti-infectious properties. Recent studies performed in both vertebrate and invertebrate models have been conducted to determine whether curcumin was also neuroprotective. The efficacy of curcumin in several preclinical trials for neurodegenerative diseases has created considerable excitement mainly because of its lack of toxicity and low cost. This suggests that curcumin could be a worthy candidate for nutraceutical intervention. As aging is a common risk factor for neurodegenerative diseases, it is possible that some compounds that target aging mechanisms could also prevent these kinds of diseases. One potential mechanism to explain several of the general health benefits associated with curcumin is that it may prevent aging-associated changes in cellular proteins that lead to protein insolubility and aggregation. This loss in protein homeostasis is associated with several age-related diseases. Recently, curcumin has been found to help maintain protein homeostasis and extend lifespan in the model invertebrate Caenorhabditis elegans. Here, we review the evidence from several animal models that curcumin improves healthspan by preventing or delaying the onset of various neurodegenerative diseases.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".