Cranberry Phenolics: Effects on Oxidative Processes, Neuron Cell Death, and Tumor Cell Growth
Bibliographic record
Abstract
The North American cranberry ( Vaccinium macrocarpon ) is rich in natural phenolic compounds which contribute to its biological activity and potential therapeutic effect. Flavonols and anthocyanins from cranberry are potent radical scavengers which effectively protect low-density lipoproteins from oxidation in vitro . Whole cranberry extract was recently observed to exert neuroprotective effects in vitro under conditions of stroke. The incidence of hydrogen-peroxide-induced necrosis in rat brain neurons treated with whole cranberry extract was 48% lower than control and simulated ischemia-induced necrosis declined by 42%. Similar protective effects were observed for cranberry in rat brain neuron apoptosis assays. The relative contributions of each class of antioxidants is under investigation. Selective inhibition of tumor cell proliferation in a variety of tumor cell lines has been observed for triterpene phenolic esters and proanthocyanidin extracts isolated from whole cranberry. The nature and occurrence of these antitumor principles in cranberries is under further studies.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".