Neuronal Uptake and Neuroprotective Properties of Curcumin-Loaded Nanoparticles on SK-N-SH Cell Line: Role of Poly(lactide-<i>co</i>-glycolide) Polymeric Matrix Composition
Bibliographic record
Abstract
Curcumin, a neuroprotective agent with promising therapeutic approach has poor brain bioavailability. Herein, we demonstrate that curcumin-encapsulated poly(lactide-co-glycolide) (PLGA) 50:50 nanoparticles (NPs-Cur 50:50) are able to prevent the phosphorylation of Akt and Tau proteins in SK-N-SH cells induced by H2O2 and display higher anti-inflammatory and antioxidant activities than free curcumin. PLGA can display various physicochemical and degradation characteristics for controlled drug release applications according to the matrix used. We demonstrate that the release of curcumin entrapped into a PLGA 50:50 matrix (NPs-Cur 50:50) is faster than into PLGA 65:35. We have studied the effects of the PLGA matrix on the expression of some key antioxidant- and neuroprotective-related genes such as APOE, APOJ, TRX, GLRX, and REST. NPs-Cur induced the elevation of GLRX and TRX while decreasing APOJ mRNA levels and had no effect on APOE and REST expressions. In the presence of H2O2, both NPs-Cur matrices are more efficient than free curcumin to prevent the induction of these genes. Higher uptake was found with NPs-Cur 50:50 than NPs-Cur 65:35 or free curcumin. By using PLGA nanoparticles loaded with the fluorescent dye Lumogen Red, we demonstrated that PLGA nanoparticles are indeed taken up by neuronal cells. These data highlight the importance of polymer composition in the therapeutic properties of the nanodrug delivery systems. Our study demonstrated that NPs-Cur enhance the action of curcumin on several pathways implicated in the pathophysiology of Alzheimer's disease (AD). Overall, these results suggest that PLGA nanoparticles are a promising strategy for the brain delivery of drugs for the treatment of AD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".