Irinophore C, a Novel Nanoformulation of Irinotecan, Alters Tumor Vascular Function and Enhances the Distribution of 5-Fluorouracil and Doxorubicin
Bibliographic record
Abstract
PURPOSE: To examine the antitumor effects of Irinophore C, a nanopharmaceutical formulation of irinotecan, on the tissue morphology and function of tumor vasculature in HT-29 human colorectal tumors. EXPERIMENTAL DESIGN: Fluorescence microscopy was used to map and quantify changes in tissue density, tumor vasculature, hypoxia, and the distribution of Hoechst 33342, a perfusion marker, and the anticancer drug, doxorubicin. Noninvasive magnetic resonance imaging was used to quantify Ktrans, the volume transfer constant of a solute between the blood vessels and extracellular tissue compartment of the tumor, as a measure of vascular function. Following treatment with Irinophore C, 19F magnetic resonance spectroscopy was used to monitor the delivery of 5-fluorouracil (5-FU) to the tumor tissue, whereas scintigraphy was used to quantify the presence of bound [14C]5-FU. RESULTS: Irinophore C decreased cell density (P = 8.42 x 10(-5)), the overall number of endothelial cells in the entire section (P = 0.014), tumor hypoxia (P = 5.32 x 10(-9)), and K(trans) (P = 0.050). However, treatment increased the ratio of endothelial cells to cell density (P = 0.00024) and the accumulation of Hoechst 33342 (P = 0.022), doxorubicin (P = 0.243 x 10(-5)), and 5-FU (P = 0.0002) in the tumor. Vascular endothelial growth factor and interleukin-8, two proangiogenic factors, were down-regulated, whereas the antiangiogenic factor TIMP-1 was up-regulated in Irinophore C-treated tumors. CONCLUSIONS: Irinophore C treatment improves the vascular function of the tumor, thereby reducing tumor hypoxia and increasing the delivery and accumulation of a second drug. Reducing hypoxia would enhance radiotherapy, whereas improving delivery of a second drug to the tumor should result in higher cell kill.
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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) | 0.000 | 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".