Effect of basic fibroblast growth factor on the cellular repopulation of decellularized anterior cruciate ligament allografts
Bibliographic record
Abstract
The use of decellularized anterior cruciate ligament (ACL) allografts in ACL replacement surgery may allow for the native structure of the ligament to be retained, thereby recapturing the function of the ligament post-injury. Our previous work has focused on repopulating decellularized allograft ACL tissue with ACL fibroblasts in order to prevent destructive remodelling of the implanted tissue by extrinsic host cells. In this study, the use of basic fibroblast growth factor (bFGF) to improve the cellular repopulation of decellularized ACL tissue was assessed. A concentration of 6 ng/ml bFGF was demonstrated to be effective in increasing cellular growth in the absence of tissue; however, this concentration, as well as reduced and increased levels of bFGF (0.1 and 60 ng/ml, respectively), failed to increase cellular repopulation of ACL fibroblast-seeded decellularized tissue after 28 days of culture. Mean repopulation levels of 11-19% of fresh tissue [3200-5300 cells/mg dry weight (dwt) tissue] were achieved after 28 days in culture. Qualitative observation of histological samples suggested that different repopulation characteristics exist at various concentrations of bFGF and, in particular, that bFGF may be stimulating a catabolic pathway resulting in matrix destruction. Significant differences in the effects of bFGF observed between cell-only and cell-and-tissue studies serve to reinforce the concept that cells respond to stimuli in a different manner, depending on the surrounding environment. As a result, caution should be used when information obtained from studies utilizing cells alone is applied to the development of tissue-engineered constructs.
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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) | 0.001 | 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".