β‐Sitosterol‐Enhanced Walnut Oil Liposomes: Optimization, Stability, and Applications
Bibliographic record
Abstract
ABSTRACT Cholesterol has limitations in food and nutraceutical applications due to health concerns. β‐Sitosterol (βS) has become an alternative to cholesterol due to its nutritional characteristics and stability. We optimized βS‐stabilized iron walnut oil (IWO) liposomes using a hybrid experimental design (Plackett–Burman and Box–Behnken methodologies), addressing emulsion stability and scalability gaps. Key parameters, including soy lecithin concentration (9 mg/mL), βS/lecithin ratio (1:6), IWO/lecithin ratio (1:4), and ultrasonication conditions (21% Tween‐80, 30 min, 425 W), were optimized. The resulting nanoliposomes achieved metrics: 92.51% encapsulation efficiency, 133.9 nm particle size (polydispersity Index [PDI] = 0.25), and −39.93 mV zeta potential, outperforming in thermal, centrifugal, and storage tests. βS synergizes with IWO's antioxidants, acting in dual roles as stabilizer and oxidation barrier, which is unachievable with conventional sterols. This innovation offers a safer and more effective option for food and nutraceutical applications. Practical Applications : This work delivers scalable strategies for food, nutraceutical applications, and pharmaceutical sectors. β‐Sitosterol enables plant‐based liposomes to encapsulate bioactive compounds (e.g., iron walnut oil's PUFAs), combining stabilization and oxidation resistance for shelf‐life extension without synthetic additives. Optimized parameters ensure reproducible production of stable nanocarriers (133.9 nm, 92.5% efficiency). Cholesterol replacement with phytosterols aligns with clean‐label trends, whereas valorizing underutilized IWO fosters economic growth in Southwest China. The technology bridges lab‐to‐industry gaps, offering cost‐effective encapsulation that addresses health priorities (heart‐healthy formulations) and environmental sustainability through resource‐efficient lipid protection.
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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".