Analytical Strategies to Analyze the Oxidation Products of Phytosterols, and Formulation-Based Approaches to Reduce Their Generation
Bibliographic record
Abstract
Phytosterols are a class of lipid molecules present in plants that are structurally similar to cholesterol and have been widely utilized as cholesterol-lowering agents. However, the susceptibility of phytosterols to oxidation has led to concerns regarding their safety and tolerability. Phytosterol oxidation products (POPs) present in a variety of enriched and non-enriched foods can show pro-atherogenic and pro-inflammatory properties. Therefore, it is crucial to screen and analyze various phytosterol-containing products for the presence of POPs and ultimately design or modify phytosterols in such a way that prevents the generation of POPs and yet maintains their pharmacological activity. The main approaches for the analysis of POPs include the use of mass spectrometry (MS) linked to a suitable separation technique, notably gas chromatography (GC). However, liquid chromatography (LC)-MS has the potential to simplify the analysis due to the elimination of any derivatization step, usually required for GC-MS. To reduce the transformation of phytosterols to their oxidized counterparts, formulation strategies can theoretically be adopted, including the use of microemulsions, microcapsules, micelles, nanoparticles, and liposomes. In addition, co-formulation with antioxidants, such as tocopherols, may prove useful in substantially preventing POP generation. The main objectives of this review article are to evaluate the various analytical strategies that have been adopted for analyzing them. In addition, formulation approaches that can prevent the generation of these oxidation products are proposed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".