Engineering 3D perfusion platforms for recapitulating immune responses in vascularized models
Bibliographic record
Abstract
The vascular system, responsible for the transport of nutrients, oxygen, and waste removal, overcomes the limitations of oxygen diffusion in solid tissues through blood perfusion, thereby preventing necrosis. The mechanical stimuli from blood flow are pivotal for vascular development and engineering, influencing endothelial cell morphology and vessel remodeling via mechanosensing. Current organ-on-chip systems, while successful in applying dynamic flow to endothelial cells, have limitations, including dependency on pumps and confinement to closed microfluidic channels. Additionally, the interaction between immune cells and these systems under long-term recirculating flow conditions has not been adequately demonstrated. This thesis introduces a novel biofabrication and device manufacturing technique that utilizes a flexible, patternable sacrificial material on a 2D surface. This material morphs in response to an aqueous hydrogel and then degrades, forming perfusable vascular networks within a natural hydrogel matrix. We achieved perfusion using a rocker mechanism that periodically changes tilt direction, while the open-well design facilitates the visualization of perfusable tubular tissues via clinical ultrasound imaging and the construction of complex, vascularized hepatic tissues embedded in gel matrices (Chapter 2). To mimic the unidirectional recirculating flow characteristic of blood vessels, we created the UniPlate platform, combining injection molding with 3D printing (Chapter 3). This innovation allows for the perfusion and recirculation of monocytes through vascular channels without compromising cell viability or eliciting an inflammatory response. Furthermore, by integrating cancer spheroids into the vascular tissues on UniPlate, we developed a vascularized cancer spheroid model that exhibited temporally dependent and tissue-specific macrophage recruitment toward tumor sites with continuous monocyte recirculation (Chapter 4). Collectively, this series of research work introduces a versatile and robust platform capable of replicating vascular functions and immune responses, offering a substantial advancement in the investigation of vascular biology and the mechanism of disease progression.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".