Real-Time Visualization and Quantitation of Vascular Permeability In Vivo: Implications for Drug Delivery
Bibliographic record
Abstract
The leaky, heterogeneous vasculature of human tumors prevents the even distribution of systemic drugs within cancer tissues. However, techniques for studying vascular delivery systems in vivo often require complex mammalian models and time-consuming, surgical protocols. The developing chicken embryo is a well-established model for human cancer that is easily accessible for tumor imaging. To assess this model for the in vivo analysis of tumor permeability, human tumors were grown on the chorioallantoic membrane (CAM), a thin vascular membrane which overlays the growing chick embryo. The real-time movement of small fluorescent dextrans through the tumor vasculature and surrounding tissues were used to measure vascular leak within tumor xenografts. Dextran extravasation within tumor sites was selectively enhanced an interleukin-2 (IL-2) peptide fragment or vascular endothelial growth factor (VEGF). VEGF treatment increased vascular leak in the tumor core relative to surrounding normal tissue and increased doxorubicin uptake in human tumor xenografts. This new system easily visualizes vascular permeability changes in vivo and suggests that vascular permeability may be manipulated to improve chemotherapeutic targeting to tumors.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".