Role of angiopoietins and Tie2/TEK in astrocytoma angiogenesis
Bibliographic record
Abstract
Diffuse astrocytomas are the most common primary malignant brain tumor. Despite advances in surgical resection, radiation therapy and understanding of their molecular pathogenesis, their prognosis remains very poor. One of the most characteristic and distinguishing features of malignant progression in astrocytomas is proliferative vascularity and robust angiogenesis. This had led to much research being focused on identifying the molecular regulators of tumor angiogenesis in astrocytomas in hopes of identifying novel and effective therapeutic strategies to restrict growth of these highly aggressive tumors. Since the early 1970s when Judah Folkman, began the research campaign for tumor angiogenesis research there has been a tremendous increase in our understanding of the tumor angiogenic process, and astrocytoma angiogenesis has benefited from these advances. However, despite identifying key regulatory angiogenic factors that are critical in the vascular growth of astrocytomas, an effective therapeutic target has not yet been designed. This in part is due to the complexity of the cascade that regulates tumor angiogenesis. In order to improve our understanding of the orchestration involved between various angiogenic pathways, we have chosen to investigate the contribution of an endothelial cell specific pathway, Angiopoietins and their cognate receptor tyrosine kinase Tie2/TEK to the vascular growth of astrocytomas.
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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) | 0.001 | 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".