Apple Peel Polyphenols and Their Beneficial Actions on Oxidative Stress and Inflammation
Post-publication record
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Bibliographic record
Abstract
UNLABELLED: Since gastrointestinal mucosa is constantly exposed to reactive oxygen species from various sources, the presence of antioxidants may contribute to the body's natural defenses against inflammatory diseases. HYPOTHESIS: To define the polyphenols extracted from dried apple peels (DAPP) and determine their antioxidant and anti-inflammatory potential in the intestine. Caco-2/15 cells were used to study the role of DAPP preventive actions against oxidative stress (OxS) and inflammation induced by iron-ascorbate (Fe/Asc) and lipopolysaccharide (LPS), respectively. RESULTS: The combination of HPLC with fluorescence detection, HPLC-ESI-MS TOF and UPLC-ESI-MS/MS QQQ allowed us to characterize the phenolic compounds present in the DAPP (phenolic acids, flavonol glycosides, flavan-3-ols, procyanidins). The addition of Fe/Asc to Caco-2/15 cells induced OxS as demonstrated by the rise in malondialdehyde, depletion of n-3 polyunsaturated fatty acids, and alterations in the activity of endogenous antioxidants (SOD, GPx, G-Red). However, preincubation with DAPP prevented Fe/Asc-mediated lipid peroxidation and counteracted LPS-mediated inflammation as evidenced by the down-regulation of cytokines (TNF-α and IL-6), and prostaglandin E2. The mechanisms of action triggered by DAPP induced also a down-regulation of cyclooxygenase-2 and nuclear factor-κB, respectively. These actions were accompanied by the induction of Nrf2 (orchestrating cellular antioxidant defenses and maintaining redox homeostasis), and PGC-1α (the "master controller" of mitochondrial biogenesis). CONCLUSION: Our findings provide evidence of the capacity of DAPP to reduce OxS and inflammation, two pivotal processes involved in inflammatory bowel diseases.
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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) | 0.003 | 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".