Pro-Angiogenic Character of Endothelial Cells and Gingival Fibroblasts Cocultures in Perfused Degradable Polyurethane Scaffolds
Bibliographic record
Abstract
Gingival atrophy manifests as exposure of the tooth root surface because of recession of the gingiva, a condition that affects >20% of adults and leads to increased root sensitivity and ultimately, tooth loss. Tissue engineering approaches that employ novel synthetic polymeric scaffolds are being considered for rebuilding the gingival lamina propria lost in the atrophic process. Specifically, polyurethane hydrogels (degradable/polar/hydrophobic/ionic polyurethane [D-PHI]) can enhance the proliferation of human gingival fibroblasts (HGFs) and collagen production in a perfusion system. However, few studies have assessed the potential of synthetic block copolyurethanes to initiate blood vessel formation in an in vitro bioreactor system. As the gingival lamina propria is highly vascular, a coculture system of human umbilical vein endothelial cells (HUVECs) with HGFs was used in perfused D-PHI scaffolds to determine the feasibility of initiating vascularization. Culture conditions were optimized for driving cocultures toward the desired tissue-engineered construct. HUVEC-HGF coculture in perfused D-PHI scaffolds with a cell seeding density of at least 80,000 cells/scaffold in a 50/50 mix of HUVEC and HGF media (by volume) exhibited enhanced cell growth and increased vascular endothelial growth factor and fibroblast growth factor (FGF)-2 production, as well as reduced myofibroblast differentiation. A greater fibroblast proportion (seeding ratio of 1:2) in the coculture resulted in HUVEC cluster formations and increased transforming growth factor-β1 and FGF-2 production. The combined pro-angiogenic effects provided by these culture conditions are anticipated to be important in the development of a highly vascularized tissue-engineered construct for regenerating the gingival lamina propria and possibly other soft tissues.
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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".