Investigating the Effects of Cranberry Proanthocyanidin and its Microbial Metabolites on the Human Intestinal MiRNome In Vitro
Bibliographic record
Abstract
The molecular basis underlying the known anti-inflammatory and anti-carcinogenic properties of cranberries is incompletely understood. The objectives of this project were to determine microRNA signatures of Caco-2BBe1 cells in response to a cranberry proanthocyanidin-enriched extract and two of its gut microbial metabolites, 3,4-dihydroxyphenylacetic acid and 3-(4- hydroxyphenyl)-propionic acid, evaluate if they were maintained in an inflammatory environment, and assess if the treatments mitigated inflammatory microRNA signatures. Each treatment generated a distinct microRNA signature, but the metabolites shared a “core” microRNA response. Treatment signatures were disrupted following IL-1β challenge, but the extract and 3,4- dihydroxyphenylacetic acid partially reversed microRNAs upregulated by IL-1β. Gene targets of microRNAs altered by the metabolites, but not the extract, were significantly enriched in many pathways relating to cell growth and development and pathways in cancer. Conclusively, the health effects of cranberry could be mediated via host microRNA and the gut microbiome may be indispensable to its bioactivity.
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) | 0.000 | 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) | 0.002 | 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".