The effect of chemotherapeutic agents on tumor vasculature in subcutaneous and orthotopic human tumor xenografts
Bibliographic record
Abstract
BACKGROUND: The growth of solid tumors and their regrowth after treatment is dependent upon functional tumor vasculature. Some chemotherapeutic agents have shown anti-angiogenic properties but there are limited studies of the effect of chemotherapy on tumor vasculature. Here we investigate the effect of paclitaxel, 5-fluorouracil (5-FU) and doxorubicin on tumor vasculature in subcutaneous and orthotopic xenografts in mice. METHODS: The vascular density and percentage of functional blood vessels were evaluated in subcutaneous A431 human vulvar cancer xenografts, and in subcutaneous and orthotopic MCF-7 human breast cancer xenografts, following single doses of paclitaxel, 5-FU or doxorubicin. RESULTS: There was no significant difference in total (CD31+) blood vessels between untreated ectopic and orthotopic MCF-7 tumors, but there was a significantly lower proportion of functional blood vessels in orthotopic tumors. After paclitaxel treatment, there was a decrease in functional tumor vasculature in A431 subcutaneous xenografts, followed by a subsequent rebound. There was a significant decrease in total vascular density on day 12 in A431 tumors following 5-FU or doxorubicin treatment, but no change in the percentage of functional vessels. An increase in functional blood vessels or percentage of functional vasculature was noted in MCF-7 subcutaneous and orthotopic xenografts following chemotherapy treatment. CONCLUSIONS: There are differences in the vasculature and microenvironment of ectopic and orthotopic xenografts in mice. Anti-tumor effects of chemotherapy may be due, in part, to effects on tumor vasculature and may vary in different tumor models.
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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 | 0.000 |
| 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".