Polyphenols from grape and apple skin: A study on non-conventional extractions and biological activity on endothelial cell cultures
Bibliographic record
Abstract
Grape and apple skins and seeds are rich in natural antioxidant compounds known as polyphenols. Polyphenols exhibit a wide range of beneficial biological properties acting as antioxidants and antiinflammatory that can be exploited for vascular diseases. The polyphenol content in grapes and apples depends on cultivar and growing conditions. For this study, four different cultivars of apples (Golden Delicious, Jonagold, Renetta Canada and Raventze) and three of grapes (Fumin, Premetta and Petit Rouge), typical of Aosta Valley (Italy), were harvested at commercial maturity. Skins were collected and dried. Powdered samples were extracted with methanol using microwave assisted extraction (MAE, 110 °C, 60 min) and high pressure and temperature extraction (HPTE, 150 °C, 150 min). The non-conventional methodologies were compared with the classic solid-liquid extraction (25 °C, 19 h). The extracts were characterized in terms of total polyphenols (TP) content and their antiradical power. HPLC analysis was also performed to quantify main single phenolic compounds. For grape and apple skins, the higher TP yields were obtained by HPTE using Jonagold and Premetta cultivars, respectively. In general, extraction yields of HPTE have reached values higher than 30 and 10 mg of Gallic Acid Equivalent per g of Dry Material for grape and apple skins, respectively. Moreover, the biological vaso-protective activity of apple extracts was investigated by evaluating the expression of endothelial activation markers in an in vitro model of endothelial dysfunction induced by the pro-inflammatory cytokine TNFα.
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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) | 0.001 | 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".