Investigating vascular-targeting strategies with three-dimensional power Doppler ultrasound.
Bibliographic record
Abstract
Vascular targeting agents have been recently combined strategically with existing cancer therapies to potentially enhance tumor response. Our aim was thus to investigate the role of blood vessels in radiation response and how blood vessels can be targeted to enhance treatments. Breast cancer MDA-MB-231 xenografts were treated with single radiation doses of 0–16 Gy alone, or in combination with Sutent, an antiangiogenic agent. 3-D ultrasound tumor data were acquired before and 24 h after treatment using a 25-MHz transducer and a VEVO770. The vascularity index (VI) was used to quantify blood from power Doppler data, while quantitative ultrasound spectroscopy (QUS) was used to monitor tumor cell death. Staining using TUNEL and CD31 of tumor sections was used to measure cell death and tumor vasculature distributions. Preliminary results indicated a VI decrease of up to 50% when tumors were irradiated with 16 Gy. Sutent-radiation combination treatments showed an increase in the VI, which may be associated with a vascular normalization effect. Analyses with QUS and TUNEL staining indicate an enhanced dose-dependent increase in tumor cell death when radiation was combined with Sutent. These results indicate that Sutent treatment may radiosensitize tumors by altering the tumor microenvironment. [Work funded by the Canadian Breast Cancer Foundation.]
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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 | 0.001 |
| 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".