Alkaline conditions better extract anti-inflammatory polysaccharides from winemaking by-products
Bibliographic record
Abstract
Winemaking generates large amounts of by-products, a well recognized source of phenolic compounds. However, less attention has been paid to the polysaccharide-rich fraction (PRF) and effects of fractionation techniques on its potential bioactivity. Therefore, PRFs from Syrah and Tempranillo winemaking by-products were extracted under aqueous (neutral pH conditions), acidic and alkaline conditions. PRFs were screened for their monosaccharide composition, uronic acid content, homogeneity and molecular weight. Anti-inflammatory activity of PRFs were evaluated on stimulated RAW 264.7 macrophages. PRF obtained in water and/or under acidic conditions showed heterogeneous profiles. As like as in the others, a heterogeneous and complex profile was detected in extracts procured under alkaline conditions. A high content of uronic acid was found in aqueous extracts, thus indicating the presence of pectin. Pectin and hemicellulose were present in PRFs procured under acidic conditions. Alkaline conditions rendered extracts containing a complex mixture of monosaccharides, mainly xylose. This latter PRF was the only one exhibiting anti-inflammatory potential (at 100 μg/mL) by reducing the release of TNF-α and activation of NF-κB in LPS-activated RAW 264.7 macrophages, with no effect on cell viability. Regardless of the grape variety, PRFs obtained under alkaline conditions were the best option to obtain bioactive polysaccharides with potential application as a source of anti-inflammatory compounds. A complex mixture of polymers may be responsible for the anti-inflammatory effects. Finally, according to results procured by NMR, it is possible to suggest that bioactive fractions are composed of a chain of α-L-Araf-(1 → 3) linked, β-D-Xylp- (1 → 4), α-D-Glcp-(1 → 4) linked, α-D-GalpA-(1 → 4), α-D-Gal-(1 → 2) forming possible RG I and RG II and xylan chains.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".