Silibinin suppresses in vivo growth of human prostate carcinoma PC-3 tumor xenograft
Bibliographic record
Abstract
Chemoprevention is an upcoming approach to control cancer including prostate cancer (PCa). Here, we studied the efficacy and associated mechanisms of a chemopreventive agent silibinin against ectopically growing and established advanced human prostate carcinoma PC-3 tumor xenografts in athymic nude mice. Dietary silibinin (0.5%, w/w) did not show any adverse health effect in mice. In first protocol, silibinin started 1 week prior to xenograft implantation and continued for 60 additional days, whereas in the second protocol, silibinin treatment was started after 25 days of established tumors for 4, 8 and 16 days. Silibinin inhibited tumor growth rate in both protocols showing up to 35% (P = 0.010) and 18-56% (P = 0.002 to <0.001) decrease in tumor volume per mouse and 27% (P < 0.01) and 44% (P = 0.014) decrease in tumor weight per mouse, respectively. In first protocol, silibinin decreased (P < 0.001) tumor cell proliferation and microvessel density but increased (P < 0.001) apoptosis. An increase in insulin-like growth factor-binding protein-3 (IGFBP-3) expression with a concomitant decrease in vascular endothelial growth factor (VEGF) expression was noted. Silibinin strongly increased phospho-extracellular signal-regulated kinase 1/2 (ERK1/2), Cip1/p21 and Kip1/p27 (cyclin-dependent kinase inhibitors) levels but moderately decreased Bcl-2 and survivin levels. In established tumors, similar biomarkers and molecular changes were observed due to silibinin corresponding to its antitumor efficacy. These findings identified in vivo antitumor efficacy of silibinin against PC-3 human PCa in both intervention protocols accompanied with its anti-proliferative, pro-apoptotic and anti-angiogenic activities. At molecular level, silibinin increased IGFBP-3, Cip1/p21, Kip1/p27 levels and ERK1/2 activation and decreased Bcl-2, survivin and VEGF levels in tumors.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".