Antiangiogenic Strategies on Defense: On the Possibility of Blocking Rebounds by the Tumor Vasculature after Chemotherapy
Bibliographic record
Abstract
Rapid or accelerated tumor cell repopulation after significant tumor cell killing induced by various cytotoxic agents often compromises the expected therapeutic benefit of such tumor responses. Here, we discuss the concept that tumor cell repopulation after certain cytotoxic therapies, using vascular disrupting agents as an example, may be aided by a reactive, systemic host response involving the mobilization of bone marrow-derived circulating cells, including endothelial progenitor cells, which subsequently home to the vasculature of treated tumors and promote tumor neovascularization. These vasculogenic "rebounds" can be blocked, at least in some cases, by treatment with an antiangiogenic drug. There is limited preliminary evidence that maximum tolerated dose chemotherapy causes a similar effect. This could constitute one way by which antiangiogenic therapy could increase the efficacy of conventional cytotoxic chemotherapy regimens; it also raises the specter of new molecular targets for systemic cancer therapies which are involved in therapy-induced bone marrow-derived cell mobilization, homing to tumors, and tumor retention.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".