Utilization of solid lipid nanoparticles for enhanced delivery of curcumin in cocultures of HT29-MTX and Caco-2 cells
Bibliographic record
Abstract
Solid lipid nanoparticles (SLN) have shown potential for encapsulation, protection and delivery of lipophilic functional components. In this study, we have investigated the capabilities of SLN to deliver a hydrophobic polyphenol compound, curcumin, in a coculture system of absorptive Caco-2 and mucus secreting HT29-MTX cells. The cells were grown on transport filters to mimic the human intestinal epithelium. Because of the hydrophobic nature of curcumin, its delivery to the basolateral compartment is expected to take place via a paracellular route. The changes in curcumin concentration in various compartments (i.e., apical, basolateral, mucus, and cell lysates) were evaluated using fluorescence spectroscopy. Two SLN systems were prepared with different emulsifying agents. The encapsulation of curcumin in SLN caused enhanced delivery compared to unencapsulated curcumin. In addition, SLN showed enhanced delivery compared to emulsion droplets containing liquid soy oil. The SLN were retained on the apical mucosal layer to a greater extent than emulsion droplets. The presence of SLN did not affect the integrity of the cellular junctions, as indicated by the TEER values, and the route of transport of the solid particles was simple diffusion, with permeability rates of about 7 × 10(-6) cm s(-1). Approximately 1% of total curcumin was delivered to the basolateral compartment, suggesting that most of the curcumin was absorbed and metabolized by the cell.
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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) | 0.001 | 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 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".