A standalone perfusion platform for drug testing and target validation in micro-vessel networks
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
Studying the effects of pharmacological agents on human endothelium includes the routine use of cell monolayers cultivated in multi-well plates. This configuration fails to recapitulate the complex architecture of vascular networks in vivo and does not capture the relationship between shear stress (i.e. flow) experienced by the cells and dose of the applied pharmacological agents. Microfluidic platforms have been applied extensively to create vascular systems in vitro; however, they rely on bulky external hardware to operate, which hinders the wide application of microfluidic chips by non-microfluidic experts. Here, we have developed a standalone perfusion platform where multiple devices were perfused at a time with a single miniaturized peristaltic pump. Using the platform, multiple micro-vessel networks, that contained three levels of branching structures, were created by culturing endothelial cells within circular micro-channel networks mimicking the geometrical configuration of natural blood vessels. To demonstrate the feasibility of our platform for drug testing and validation assays, a drug induced nitric oxide assay was performed on the engineered micro-vessel network using a panel of vaso-active drugs (acetylcholine, phenylephrine, atorvastatin, and sildenafil), showing both flow and drug dose dependent responses. The interactive effects between flow and drug dose for sildenafil could not be captured by a simple straight rectangular channel coated with endothelial cells, but it was captured in a more physiological branching circular network. A monocyte adhesion assay was also demonstrated with and without stimulation by an inflammatory cytokine, tumor necrosis factor-α.
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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 | 0.001 | 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 it