A Bioglass‐Poly(lactic<i>‐co‐</i>glycolic Acid) Scaffold@Fibrin Hydrogel Construct to Support Endochondral Bone Formation
Bibliographic record
Abstract
Bone tissue engineering using stem cells to build bone directly on a scaffold matrix often fails due to lack of oxygen at the injury site. This may be avoided by following the endochondral ossification route; herein, a cartilage template is promoted first, which can survive hypoxic environments, followed by its hypertrophy and ossification. However, hypertrophy is so far only achieved using biological factors. This work introduces a Bioglass-Poly(lactic-co-glycolic acid@fibrin (Bg-PLGA@fibrin) construct where a fibrin hydrogel infiltrates and encapsulates a porous Bg-PLGA. The hypothesis is that mesenchymal stem cells (MSCs) loaded in the fibrin gel and induced into chondrogenesis degrade the gel and become hypertrophic upon reaching the stiffer, bioactive Bg-PLGA core, without external induction factors. Results show that Bg-PLGA@fibrin induces hypertrophy, as well as matrix mineralization and osteogenesis; it also promotes a change in morphology of the MSCs at the gel/scaffold interface, possibly a sign of osteoblast-like differentiation of hypertrophic chondrocytes. Thus, the Bg-PLGA@fibrin construct can sequentially support the different phases of endochondral ossification purely based on material cues. This may facilitate clinical translation by decreasing in-vitro cell culture time pre-implantation and the complexity associated with the use of external induction factors.
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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".