Effect of hypoxia on angiogenesis related factors in glioblastoma cells
Bibliographic record
Abstract
Pathological angiogenesis is a characteristic feature of glioblastoma multiforme (GBM) where the balance between pro-angiogenic and anti-angiogenic factors are shifted towards the pro-angiogenic phenotype. In this study we sought to determine whether angiostatins are expressed by GBM cells and whether their expression along with other related factors [matrix metalloproteinase (MMP)-2, MMP-9, and collagen type I α1 (COLIA1)] are altered by hypoxia and/or correlated with the levels of cancer stem cell marker CD133. Using qRT-PCR, western blotting, and gelatin zymography, we examined the expression of angiostatins, MMP-2, MMP-9, COLIA1 and CD133 in GBM cell lines cultured under aerobic conditions and hypoxia. Expression levels of MMP-2 and MMP-9 were significantly induced by hypoxia. Angiostatins were detected in all GBM cell lines and were increased by hypoxia while the angiostatin isoform of 38-kDa was the most abundant in GBM cells under aerobic and hypoxic conditions. COLIA1 and CD133 were significantly increased in several GBM cell lines under hypoxia. Despite expression and upregulation of anti-angiogenic factors (e.g. angiostatins) in GBM cells, they are overwhelmed by the overexpression of a larger number of angiogenic factors that shift the angiogenic balance towards the pro-angiogenic phenotype. Thus, an exogenous administration of anti-angiogenic factors may be required to improve the treatment of GBM tumors.
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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 | 0.000 |
| 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".