Role of curcumin and its nanoformulations in the treatment of neurological diseases through the effects on stem cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it