Bibliographic record
Abstract
A number of cancer therapies including radiation, chemotherapy, and anti-vascular treatments act by causing endothelial cell death, leading to microvascular destruction within tumours. We have been using high-frequency (20–40 MHz) power Doppler micro-ultrasound to study the effects of a variety of anti-cancer therapies on tumor blood micro-vessels. Using breast and prostate xenograft tumors in mice with radiation alone, large doses above 8 Gy appeared to diminish the vascular by up to 40%–60%. Radiation given in the presence of a basic fibroblast growth factor did not have this effect. The anti-angiogenic Sutent suppressed vascular growth and enhanced radiation effect, but tumor vasculature exhibited a rebound effect when therapy was stopped. In contrast, novel anti-vascular microbubble based therapies induced a greater synergistic tumor cell kill due to vascular disruption. Tumor cell kill increased when microbubbles were used in conjunction with radiation, with increases of cell kill from 5% to over 50% with combined single treatments linked to destruction of the micro-vasculature. In summary, power Doppler micro-ultrasound can be used quantitatively to assess the effects of anti-cancer therapies on tumor micro-vasculature. Effects of anti-angiogenic agents and newer anti-vascular therapies and their rebound effects can be monitored non-invasively and longitudinally in preclinical models.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".