Health Benefits of Microencapsulated Dietary Polyphenols: A Review
Bibliographic record
Abstract
Isolated dietary polyphenols are subjected to degradation under unfavorable conditions leading to the loss of their bioactivity. Microencapsulation has attained research interest for enhancing the stability, improved bioavailability of polyphenols, and targeted delivery. This review examines the effects of microencapsulation on health promotion compared to non-encapsulated polyphenol extracts as evidenced through in vitro, experimental animal, and human intervention studies. Most of the research confirms that microencapsulation enhances the extent of bioaccessibility and bioavailability of polyphenols. Also, encapsulated polyphenols have exhibited greater antidiabetic, anti-inflammatory, anticancer, and antimicrobial effects than non-encapsulated polyphenols. Interestingly microparticles are not found to create any cytotoxicity. Nevertheless, a few studies demonstrate either low or similar health effects of encapsulated form to their unprocessed form. Future research should mainly focus on human trials to better understand the contribution of microencapsulation to human health. Importantly, investigations should include non-encapsulated polyphenols as well to compare relative efficacy improvement by microencapsulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".