Curcumin inhibits tumor growth and angiogenesis in glioblastoma xenografts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it