Role of curcumin and its nanoformulations in the treatment of neurological diseases through the effects on stem cells
Bibliographic record
Abstract
Curcumin from turmeric is a natural phenolic compound with a promising potential to regulate fundamental processes involved in neurological diseases, including inflammation, oxidative stress, protein aggregation, and apoptosis at the molecular level. In this regard, employing nanoformulation can improve curcumin efficiency by reducing its limitations, such as low bioavailability. Besides curcumin, growing data suggest that stem cells are a noteworthy candidate for neurodegenerative disorders therapy due to their anti-inflammatory, anti-oxidative, and neuronal-differentiation properties, which result in neuroprotection. Curcumin and stem cells have similar neurogenic features and can be co-administered in a cell-drug delivery system to achieve better combination therapeutic outcomes for neurological diseases. Based on the evidence, curcumin can induce the neuroprotective activity of stem cells by modulating their related signalling pathways. The present review is about the role of curcumin and its nanoformulations in the improvement of neurological diseases alone and through the effect on different categories of stem cells by discussing the underlying mechanisms to provide a roadmap for future investigations.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".