Interpenetrating Alginate-Collagen Polymer Network Microspheres for Modular Tissue Engineering
Bibliographic record
Abstract
The lack of vascularization limits the creation of engineered tissue constructs with clinically relevant sizes. We pioneered a bottom-up process (modular tissue engineering) in which constructs with intrinsic vasculature were assembled from endothelialized building blocks. In this study, we prepared an interpenetrating polymer network (IPN) hydrogel from a collagen-alginate blend and evaluated its use as microspheres in modular tissue engineering. Ionotropic gelation of alginate was combined with collagen fibrillogenesis, and the resulting hydrogel was stiffer and had greater resistance to enzymatic degradation relative to that of collagen alone; the viability of embedded mesenchymal stromal cells (adMSC) was unaltered. IPN microspheres were fabricated by a coaxial air-flow technique, and an additional step of collagen coating was required to have human umbilical vein endothelial cells (HUVEC) attach and proliferate. When implanted subcutaneously in SCID/bg mice, adMSC-HUVEC microspheres promoted more blood vessels at day 7 relative to microspheres without adMSC but coated with HUVEC. Perfusion studies confirmed that these vessels were connected to the host vasculature. Fewer vessels were detected in both groups at day 21, but in adMSC-HUVEC explants, more smooth muscle cells had wrapped around vessels, and CLARITY processing of whole explants revealed a restricted leakage of blood. The capacity for rapid gelation and high throughput production are promising features for the use of these microspheres in modular tissue engineering.
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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".