Effect of vitrification on mechanical properties of porcine articular cartilage
Bibliographic record
Abstract
Articular cartilage (AC) injuries do not heal primarily and large lesions progress to degenerative osteoarthritis. Osteochondral allograft transplantation is an effective surgical treatment but is limited by the lack of donor tissue availability. Fresh allografts can be stored hypothermically up to 28–45 days after which the tissue is no longer viable for transplantation. Vitrification is a method of cryopreservation with the potential to extend the storage time of AC. A specific protocol has been demonstrated to preserve high chondrocyte viability; however, its effect on various mechanical properties of the extracellular matrix (ECM) remains unknown and is the focus of this initial study. Porcine AC was subject to a defined vitrification protocol, using fresh and frozen samples as positive and negative controls, respectively; n = 20 for all three groups. Unconfined compression testing was used to assess mechanical properties of the tissue under rapid load, stress relaxation, and equilibrium conditions. The stress relaxation time constants (modeled with a 2-term Prony series) τ 1 and τ 2 were significantly lower for frozen ( p = 0.014, p < 0.001) and vitrified ( p = 0.009, p = 0.003) tissue compared to fresh, with no differences between frozen and vitrified samples ( p = 0.848 and 0.105 for τ 1 and τ 2 , respectively). These values indicate that frozen and vitrified samples relaxed more rapidly than fresh, which may suggest altered matrix composition and permeability post-treatment. These results represent the initial study in our experimental path to evaluate differences in mechanical properties of vitrified tissues.
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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 | 0.001 |
| 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".