Lowbush Blueberry Component Pterostilbene Inhibits the Matrix Metalloproteinase and the Urokinase Signaling Systems in Human Prostate Cancer Cells Via Alterations in Cellular Signal Transduction Pathways
Bibliographic record
Abstract
Wild blueberry extracts were shown to inhibit MMP-2 & MMP-9 activity in DU145 human prostate cancer cells in vitro. This study examined the effects of pterostilbene (PT) on MMP & uPA activities in DU145 cells. PT (30uM)(6h) inhibited MMP-2/-9 activity. PT decreased cellular viability ~10% post 6 h. exposure. PT treatment increased TIMP-1/-2 & decreased EMMPRIN & RECK expression. PT treatment decreased uPA & uPAR and increased PAI-1/-2 protein expression. PT treatment resulted in increased expression of pERK-1, pERK-2 & ERK-1. PT decreased p-p38 & p38 protein levels. No apparent change in either ERK-2, JNK-1 & JNK-2 or p-JNK-2 expression was noted. PT did increase p-JNK-1 protein levels. PT increased expression of p-Akt & P-I-3 kinase p85 with no apparent change in either Akt or P-I-3 kinase p110 protein expression levels. PT also increased JAK1, STAT3 & STAT4 expression & decreased JAK2 & STAT1 expression with no apparent change in either JAK3 or STAT2 expression. PT decreased cytosolic p65 and nuclear p50 & increased cytosolic p50 & increased IkB & c-fos and c-jun expression. PT can inhibit MMP-2/-9 & uPA activities by affecting cellular signalling. (Telus Motorcycle Ride for Dad (PEI Division) Prostate Cancer Research Fund)
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".