Fibrin‐filled scaffolds for bone‐tissue engineering: An <i>in vivo</i> study
Bibliographic record
Abstract
Recently, fibrin sealants that typically contain supra physiological concentrations of fibrinogen and thrombin have been investigated as matrices to facilitate the delivery of cells within biodegradable scaffolds for tissue engineering applications. It is well known from in vitro experiments that the thrombin concentration present during fibrin polymerization influences the structural properties of fibrin, and these can affect cell invasion. This study was conducted to determine whether the structural properties of fibrin can affect bony wound healing in vivo. Drill hole defects were created in the distal femurs of 20 rats. Four experimental groups were used: nontreated defects, scaffolds alone, and scaffolds filled with fibrin polymerized with either a low thrombin concentration [fibrin(low T)] or a high thrombin concentration [fibrin(high T)]. The area of bone formed at 2, 5, and 11 days after implantation was determined histomorphometrically. After 5 days, scaffolds filled with fibrin(high T) were infiltrated with less bone than empty scaffolds (p < 0.05), but no statistical difference was found between the empty scaffolds and the scaffolds filled with fibrin(low T). After 11 days, both fibrin-filled scaffolds significantly delayed bony wound healing (p < 0.004). Reducing sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis of the two fibrin formulations showed no difference in gamma-gamma crosslink formation. This work demonstrates that fibrin sealants in their present state are not ideal for enhancing bone-tissue invasion into scaffolds, and that the structural properties of fibrin matrices may be an important design parameter for maximizing host tissue invasion during wound healing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".