Microencapsulation Enhances the Biological Potential, Bioaccessibility, and Intracellular Oxidative Status of Guava Phenolic Extracts
Bibliographic record
Abstract
Guava fruit is widely consumed in tropical countries and beyond. The phenolic fraction of guava pulp and processing waste (a single fraction containing seeds, skins, and pulp residues) have been reported to carry in vitro biological activities, acting on biomarkers of metabolic diseases such as type 2 diabetes and obesity (enzymatic inhibition of α-glucosidase and pancreatic lipase), atherosclerosis (mitigation of LDL-cholesterol oxidation), and mutagenesis (suppression of DNA strand scission). However, such bioactivities may be compromised by the exposure of guava phenolics to the harsh conditions found along the human gastrointestinal (GI) tract. To overcome this limitation, guava phenolic extracts were microencapsulated with maltodextrin through freeze-drying. The effect of crude and microencapsulated extracts on biomarkers of metabolic diseases was compared before and after in vitro simulated GI digestion. Moreover, guava waste extracts were tested for their ability to interfere with the intracellular redox status of Caco-2 and HeLa cells incubated with free radicals. Microencapsulation considerably improved the bioaccessibility of guava phenolics across digestion stages, which reflected on the enhancement of most bioactivities measured, with the exception of pancreatic lipase inhibition (both pulp and waste extracts) and LDL oxidative protection (pulp extract). Meanwhile, microencapsulation accentuated intracellular antioxidant activity in Caco-2 cells induced by guava waste extract whereas a prooxidant effect in HeLa cells was intensified. This highlights the selectivity of the same extract toward different cell lines. Overall, microencapsulation was demonstrated as a promising tool for protecting and even enhancing the nutraceutical power of guava phenolics, reinforcing their relevance in the development of functional foods and nutraceutical products.
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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".