Characterization and Inhibition of Fibrin Hydrogel–Degrading Enzymes During Development of Tissue Engineering Scaffolds
Bibliographic record
Abstract
The goal of articular cartilage tissue engineering is to provide cartilaginous constructs to replace abnormal cartilage. We have evaluated the chondroprogenitor clonal cell line RCJ3.1C5.18 (C5.18) as a model to guide the development of appropriate scaffolds for tissue engineering. Rapid degradation of fibrin hydrogels was observed after encapsulation of C5.18 cells. The enzymes responsible for this fibrin gel breakdown were characterized to control their activity and regulate gel stability. Western blotting, confirming zymography, revealed bands due to matrix metalloproteinases (MMP-2, MMP-3) that are secreted concomitantly with fibrin hydrogels breakdown. High plasmin activity was detected in conditioned media during hydrogel breakdown but not in the confluent cells before encapsulation. Reverse transcriptase polymerase chain reaction indicated the expression of MMP-2, -3, and -9 and plasminogen in the cells. MMP-9 was 100 times higher at day 1, whereas MMP-2 started to increase and reached its maximum level by day 7. Aprotinin, a known serine protease inhibitor, and galardin (GM6001), a potent MMP inhibitor, in combination or separately, prevented the breakdown of fibrin-C5.18 hydrogels, whereas only the combination of both promoted the accumulation of extracellular matrix. These findings suggest that plasmin and MMPs contribute independently to fibrin hydrogel breakdown, but that either enzyme can achieve extracellular matrix breakdown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".