Neuronal Uptake and Neuroprotective Effect of Curcumin-Loaded PLGA Nanoparticles on the Human SK-N-SH Cell Line
Bibliographic record
Abstract
Curcumin, a natural polyphenolic pigment present in the spice turmeric (Curcuma longa), is known to possess a pleiotropic activity such as antioxidant, anti-inflammatory, and anti-amyloid-β activities. However, these benefits of curcumin are limited by its poor aqueous solubility and oral bioavailability. In the present study, a polymer-based nanoparticle approach has been utilized to deliver drugs to neuronal cells. Curcumin was encapsulated in biodegradable poly (lactide-co-glycolide) (PLGA) based-nanoparticulate formulation (Nps-Cur). Dynamic laser light scattering and transmission electronic microscopy analysis indicated a particle diameter ranging from 80 to 120 nm. The entrapment efficiency was 31% with 15% drug-loading. In vitro release kinetics of curcumin from Nps-Cur revealed a biphasic pattern with an initial exponential phase followed by a slow release phase. Cellular internalization of Nps-Cur was confirmed by fluorescence and confocal microscopy with a wide distribution of the fluorescence in the cytoplasm and within the nucleus. The prepared nanoformulation was characterized for cellular toxicity and biological activity. Cytotoxicity assays showed that void PLGA-nanoparticles (Nps) and curcumin-loaded PLGA nanoparticles (Nps-Cur) were nontoxic to human neuroblastoma SK-N-SH cells. Moreover, Nps-Cur was able to protect SK-N-SH cells against H2O2 and prevent the elevation of reactive oxygen species and the consumption of glutathione induced by H2O2. Interestingly, Nps-Cur was also able to prevent the induction of the redox-sensitive transcription factor Nrf2 in the presence of H2O2. Taken together, these results suggest that Nps-Cur could be a promising drug delivery strategy to protect neurons against oxidative damage as observed in Alzheimer's disease.
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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".