<i>In Vitro</i> Dissolution, Cellular Membrane Permeability, and Anti-Inflammatory Response of Resveratrol-Encapsulated Mesoporous Silica Nanoparticles
Bibliographic record
Abstract
Sizing drugs down to the submicron and nanometer scale using nanoparticles has been extensively used in pharmaceutical industries to overcome the poor aqueous solubility of potential therapeutic agents. Here, we report the encapsulation and release of resveratrol, a promising anti-inflammatory and anticancer nutraceutical, from the mesopores of MCM-48-type silica nanospheres of various particle sizes, i.e., 90, 150, and 300 nm. Furthermore, the influence of the carrier pore size on drug solubility was also evaluated (3.5 vs 7 nm). From our results, it is observed that the saturated solubility could depend not only on the pore size but also on the particle size of the nanocarriers. Moreover, with our resveratrol-mesoporous silica nanoparticles formulation, we have observed that the permeability of resveratrol encapsulated in MCM-48 nanoparticles (90 nm) can be enhanced compared to a resveratrol suspension when tested through the human colon carcinoma cell monolayer (Caco-2). Using an in vitro NF-κB assay, we showed that resveratrol encapsulation did not alter its bioactivity and, at lower concentration, i.e., 5 μg mL –1, resveratrol encapsulation provided higher anti-inflammatory activity compared to both resveratrol suspension and solution. All combined, the reported results clearly highlight the potential of small size mesoporous silica nanoparticles as next generation nanocarriers for hydrophobic drugs and nutraceuticals.
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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".