Anti-angiogenic Therapy and Induction of Blood Vessel Normalization in the Treatment of Ovarian Cancer
Bibliographic record
Abstract
Angiogenesis describes the sprouting of new blood vessels from preexisting vascular networks. A tightly regulated balance between pro- and antiangiogenic factors regulates this process, which can be disrupted under pathological conditions. Tumor development is characterized by an “angiogenic switch”, which results in an increase in expression of pro-angiogenic factors such as vascular endothelial growth factor (VEGF) and concomitant repression of angiogenesis inhibitors such as thrombospondin- 1 (TSP-1). Overexpression of pro-angiogenic factors promotes the rapid formation of tumor vasculature, which typically is hyperpermeable, convoluted, and lacks normal perivascular cell interactions. This disorganized vasculature results in reduced tumor perfusion, which can significantly impair drug delivery to the tumor. Ovarian cancer is typically not detected until late stage and can present a therapeutic challenge, due to the presence of a large, poorly-vascularized tumor. The prevalence of chemoresistance in ovarian cancer has spurred recent research into alternative therapeutic targets for this disease. Various anti-angiogenic approaches have been studied, including targeting VEGF expression and restoring expression of anti-angiogenic factors such as TSP-1. Studies using anti-angiogenic treatments have demonstrated the preferential destruction of immature, dysfunctional blood vessels, with the preservation of healthy, parental vasculature. The vessel normalization induced by anti-angiogenic therapy has been found to improve tumor perfusion, facilitate uptake of chemotherapeutic compounds and is associated with tumor regression even in advanced stage ovarian cancer. The purpose of this review is to examine the pathological angiogenic processes that characterize tumor growth, evaluate the efficacy of anti-angiogenic therapies, and discuss the process and therapeutic implications of vessel normalization in the context of antiangiogenic therapies for advanced stage ovarian cancer. Keywords: Angiogenesis inhibitors, thrombospondin, anti-cancer therapy, combination therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".