Benefits of curcumin in brain disorders
Bibliographic record
Abstract
Curcumin is widely consumed in Asia either as turmeric directly or as one of the culinary ingredients in food recipes. The benefits of curcumin in different organ systems have been reported extensively in several neurological diseases and cancer. Curcumin has got its global recognition because of its strong antioxidant, anti-inflammatory, anti-cancer, and antimicrobial activities. Additionally, it is used in diabetes and arthritis as well as in hepatic, renal, and cardiovascular diseases. Recently, there is growing attention on usage of curcumin to prevent or delay the onset of neurodegenerative diseases. This review summarizes available data from several recent studies on curcumin in various neurological diseases such as Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, Huntington's disease, Prions disease, stroke, Down's syndrome, autism, Amyotrophic lateral sclerosis, anxiety, depression, and aging. Recent advancements toward increasing the therapeutic efficacy of curcuma/curcumin formulation and the novel delivery strategies employed to overcome its minimal bioavailability and toxicity studies have also been discussed. This review also summarizes the ongoing clinical trials on curcumin for different neurodegenerative diseases and patent details of curcuma/curcumin in India.
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 | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".