Encapsulation of phytosterols and phytosterol esters in liposomes made with soy phospholipids by high pressure homogenization
Bibliographic record
Abstract
The objective of this work is to design liposomal vesicles for the encapsulation of phytosterols and phytosterol esters (PEs), and to understand the encapsulation mechanism of phytosterols and PE in these vesicles. A commercial blend of free phytosterols and phytosterol esters were incorporated into liposomes by microfluidization using native mixtures of soy phospholipids. The average diameter of the liposomes increased with increasing amounts of encapsulated phytosterols, especially with increasing free sterol content. The phytosterol content, liposome size, and phytosterol encapsulation efficiency started to plateau when liposomes were prepared with more than 4% commercial PE blend at soy phospholipid content of 50 mg/ml, suggesting a saturation of phytosterol encapsulation. We propose an encapsulation mechanism of free sterols and PEs in liposomes, where free sterols were mainly encapsulated within the lumen of these liposomes as crystals, and PEs and some free sterols were incorporated within the phospholipid bilayer of the liposomal membrane. Results from this work could provide the pharmaceutical and nutraceutical industries a practical method to produce loaded liposomes using inexpensive phospholipid mixtures for the delivery of bioactive ingredients.
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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".