Development and Modeling of a Polymer Construct for Perfusion Imaging and Tissue Engineering.
Bibliographic record
Abstract
The physical and computational modeling of distributed fluid flow to vascular beds remains a challenging issue. The computational resources required, and the complexity of capillary networks makes modeling infeasible. The resolution limits of manufacturing techniques make physical models difficult to fabricate and manipulate under experimental conditions. As such, an in vitro polymer construct was developed with structural properties of small arteries and the bulk flow characteristics of capillary beds. Rapid prototyping and scaffolding techniques were used to fabricate vascular trees amendable to scaffold compartments. Several scaffold architectures were evaluated to achieve target fluid flow characteristics for implementation in a dynamic contrast-enhanced computed tomography (DCE-CT) imaging phantom and endothelial cell bioreactor, respectively. Experimental flow measurements were compared to measurements from computational simulations. In addition, the flow-induced shear stress across the construct was modeled to identify the optimal settings within the bioreactor. In addition, the cytocompatibility of the polymer construct was optimized. Vascular trees were reliably fabricated to achieve arteriole-like flow. Rapid prototyped polycaprolactone (PCL) scaffolds produced distinct differential flow ranges, marked by a decrease in flow rate across the network. The construct served as a viable dynamic flow phantom capable of generating signals typical of organs imaged with DCE-CT. Furthermore, simulations of the construct as a bioreactor provided guidance on the boundary conditions required for stimulatory shear stress within the scaffolds. Under static conditions, endothelial cells were cultured on PCL scaffolds modified with extra-cellular matrix mimicking biological and chemical agents. All surface modifications exhibited similar cell proliferation and function. However, the Arg-Gly-Asp (RGD) surface-modified constructs exhibited an optimal spatial distribution for future endothelial cell bioreactor investigations. This work demonstrates a method for modeling and physically simulating a bifurcating vascular tree adjoined to scaffold compartments with tunable flow, for application to perfusion imaging and in vitro tissue engineering (tissue and 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".