Pro-Angiogenic Character of Endothelial Cells and Gingival Fibroblasts Cocultures in Perfused Degradable Polyurethane Scaffolds
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it