Endothelial Regulation of Drug Transport in a 3D Vascularized Tumor Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Drug discovery and efficacy in cancer treatments are limited by the inability of pre‐clinical models to predict successful outcomes in humans. Limitations remain partly due to their lack of a physiologic tumor microenvironment (TME), which plays a considerable role in drug delivery and tumor response to therapy. Chemotherapeutics and immunotherapies rely on transport through the vasculature, via the smallest capillaries and stroma to the tumor, where passive and active transport processes are at play. Here, a 3D vascularized tumor on‐chip is used to examine drug delivery in a relevant TME within a large bed of perfusable vasculature. This system demonstrates highly localized pathophysiological effects of two tumor spheroids (Skov3 and A549), which cause significant changes in vessel density and barrier function. Paclitaxel (Taxol) uptake is examined through diffusivity measurements, functional efflux assays, and accumulation of the fluorescent‐conjugated drug within the TME. Due to vascular and stromal contributions, differences in the response of vascularized tumors to Taxol (shrinkage and CD44 expression) are apparent compared with simpler models. This model specifically allows for examination of spatially resolved tumor‐associated endothelial dysfunction, likely improving the representation of in vivo drug distribution, and has potential for development into a more predictable model of drug delivery.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it