Curcumin inhibits tumor growth and angiogenesis in glioblastoma xenografts
Bibliographic record
Abstract
Among the natural products shown to possess chemopreventive and anticancer properties, curcumin is one of the most potent. In the current study, we investigated the effects of this natural product on the growth of human glioma U-87 cells xenografted into athymic mice. We show here that curcumin administration exerted significant anti-tumor effects on subcutaneous and intracerebral gliomas as demonstrated by the slower tumor growth rate and the increase of animal survival time. While investigating the mechanism of its action in vivo, we observed that curcumin decreased the gelatinolytic activities of matrix metalloproteinase-9. Furthermore, treatment with curcumin inhibited glioma-induced angiogenesis as indicated by the decrease of endothelial cell marker from newly formed vessels and by the diminution of the concentration of hemoglobin in curcumin-treated tumors. We also demonstrate, using an in vitro model of blood-brain barrier, that curcumin can cross the blood-brain barrier to a high level. These are the first results showing that curcumin suppresses tumor growth of gliomas in xenograft models. The mechanisms of the anti-tumor effects of curcumin were related, at least partly, to the inhibition of glioma-induced angiogenesis.
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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 | 0.001 |
| 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".