Vascularized organoid engineered by modular assembly enables blood perfusion
Bibliographic record
Abstract
Tissue engineering is one approach to address the donor-organ shortage, but to attain clinically significant viable cell densities in thick tissues, laboratory-constructed tissues must have an internal vascular supply. We have adopted a biomimetic approach and assembled microscale modular components, consisting of submillimeter-sized collagen gel rods seeded with endothelial cells (ECs) into a (micro)vascularized tissue; in some prototypes the gel contained HepG2 cells to illustrate the possibilities. The EC-covered modules then were assembled into a larger tube and perfused with medium or whole blood. The interstitial spaces among the modules formed interconnected channels that enabled this perfusion. Viable cell densities were high, within an order of magnitude of cell densities within tissues, and the percolating nature of the flow through the construct was evident in microcomputed tomography and Doppler ultrasound measurements. Most importantly, the ECs retained their nonthrombogenic phenotype and delayed clotting times and inhibited the loss of platelets associated with perfusion of whole blood through the construct. Unlike the conventional scaffold and cell-seeding paradigm of other tissue-engineering approaches, this modular construct has the potential to be scalable, uniform, and perfusable with whole blood, circumventing the limitations of other approaches.
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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) | 0.000 | 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 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".