Neuronal Uptake and Neuroprotective Effect of Curcumin-Loaded PLGA Nanoparticles on the Human SK-N-SH Cell Line
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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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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) | 0.000 | 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) | 0.000 | 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