Characterization of bioactive cranberry fractions by mass spectrometry
Bibliographic record
Abstract
Increasing evidence indicates that fruits contain functional bio-active compounds that have several preventive and therapeutic health benefits. Our group has recently conducted studies to assess the potential effects of cranberry polyphenolic fractions on intestinal Caco-2/15 epithelial cells and a substantial reduction in oxidative stress and inflammation was observed. The aim of the present work was to determine the polyphenolic species most likely responsible for the observed biological activity. Low, medium, and high molecular weight cranberry fractions were generated with a Sephadex LH-20 column by elution with 60% MeOH, 100% MeOH, and 70% acetone, respectively. The total phenolic content in these fractions was determined by the Folin–Ciocalteu method. A combination of LC–MS and MALDI-TOF methods were used to characterize the nature of polyphenolic compounds in the cranberry extracts. High resolution mass spectrometry was used to generate empirical formulae for the detected species. The low molecular weight fraction was essentially constituted of small phenolic acids (hydroxycinnamic and hydroxybenzoic acids). The medium fraction was mostly composed of anthocyanin, flavonols (quercetin, myricetin, isorhamnetin, kaempferol), procyanidins monomers (epicatechin, catechin), dimers, and few small oligomers. The heavy fraction was devoid of phenolic acids and anthocyanins, and it contained for the most part oligomers and polymers of procyanidins. Proanthocyanidins oligomers up to n = 22 were detected, which is, to our knowledge, the largest individual polymers reported to date. With this approach, it was also possible to distinguish between the A-type and B-type linkages.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".