Designing Vitamin D3 Formulations: An In Vitro Investigation Using a Novel Micellar Delivery System
Bibliographic record
Abstract
Vitamin D is an essential nutrient with important immunomodulatory properties. As a fat-soluble compound, Vitamin D (and its D3 form) is immiscible with water, which presents challenges to absorption. In an in vitro setting, the current study characterizes novel micellar formulations of Vitamin D3 designed to improve absorption. Techniques used to evaluate and compare the micellar formulations against a non-micellar formula include the following: cryo-SEM to determine morphology; laser diffraction to determine particle size and distribution; zeta potential to determine stability of the particles; solubility assays to determine solubility in water and gastrointestinal media; and Caco-2 cell monolayers to determine intestinal permeability. Results show advantageous features (particle size range in the low micrometres with an average zeta potential of −51.56 ± 2.76 mV), as well as significant improvements in intestinal permeability, in one optimized micellar formula (LipoMicel®). When introduced to Caco-2 cells, LipoMicel’s permeability was significantly better than the control (p < 0.01; ANOVA). Findings of this study suggest that the novel micellar form of Vitamin D3 (LipoMicel) has the potential to promote absorption of Vitamin D3. Thus, it can serve as a promising candidate for follow-up in vivo studies in humans.
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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 | 0.001 | 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".