<i>In vitro</i>gastrointestinal digestibility of phytosterol oleogels: influence of self-assembled microstructures on emulsification efficiency and lipase activity
Bibliographic record
Abstract
The objective of this study was to investigate the influence of self-assembled microstructure on lipid digestibility in phytosterol (γ-oryzanol and β-sitosterol) oleogels. Different molar ratios of γ-oryzanol and β-sitosterol yielded a variety of crystal morphologies; the resulting gels were tested for their lipid emulsification efficiency, release rate of free fatty acids (FFAs) during lipolysis, and their effect on lipase behavior. Results indicated that oleogels were harder to emulsify when compared to oil samples. The emulsification efficiencly was affected by both the gel strength and crystal morphology of the self-assembled structures within phytosterol oleogels. In oil emulsions, intestinal digestion resulted in more extensive lipid droplet coalescence with increased particle size when compared to oleogel emulsions. The FFA release rate suggested that the extent of lipid digestion was correlated to the emulsification efficiency. The interfacial binding of lipase indicated that the amount of lipase adsorption was positively correlated to the interface area created during the emulsification process. Finally, isothermal titration calorimetry results indicated that self-assembled structures within these oleogels physically obstructed the interaction between lipase and lipid. Ultimately, this led to lower reaction rate during gastrointestinal digestion. Collectively, these results may have important implications in designing oleogel systems with controlled lipid digestibility as well as controlling the bioavailability of delivered lipid-soluble bioactive compounds.
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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) | 0.000 | 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".