Effect of basic fibroblast growth factor on the cellular repopulation of decellularized anterior cruciate ligament allografts
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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