On‐Chip Reconstitution of Uniformly Shear‐Sensing 3D Matrix‐Embedded Multicellular Blood Microvessel
Bibliographic record
Abstract
Abstract Preclinical human‐relevant modeling of organ‐specific vasculature offers a unique opportunity to recreate pathophysiological intercellular, tissue‐tissue, and cell‐matrix interactions for a broad range of applications. Here, this work presents a reliable, and simply reproducible process for constructing user‐controlled long rounded extracellular matrix (ECM) embedded vascular microlumens on‐chip for endothelization and co‐culture with stromal cells obtained from human lung. This work demonstrates the critical impact of microchannel cross‐sectional geometry and length on uniform distribution and magnitude of vascular wall shear stress, which is key when emulating in vivo observed blood flow biomechanics in health and disease. In addition, this study provides an optimization protocol for multicellular culture and functional validation of the system. Moreover, this study shows the ability to finely tune rheology of the three‐dimensional natural matrix surrounding the vascular microchannel to match pathophysiological stiffness. In summary, this work provides the scientific community with a matrix‐embedded microvasculature on‐chip populated with all‐primary human‐derived pulmonary endothelial cells and fibroblasts to recapitulate and interrogate lung parenchymal biology, physiological responses, vascular biomechanics, and disease biogenesis in vitro. Such a mix‐and‐match synthetic platform can be feasibly adapted to study blood vessels, matrix, and ECM‐embedded cells in other organs and be cellularized with additional stromal cells.
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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) | 0.001 | 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 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".