Freeze-thaw treatment effects on the dynamic mechanical properties of articular cartilage
Bibliographic record
Abstract
BACKGROUND: As a relatively non-regenerative tissue, articular cartilage has been targeted for cryopreservation as a method of mitigating a lack of donor tissue availability for transplant surgeries. In addition, subzero storage of articular cartilage has long been used in biomedical studies using various storage temperatures. The current investigation studies the potential for freeze-thaw to affect the mechanical properties of articular cartilage through direct comparison of various subzero storage temperatures. METHODS: Both subzero storage temperature as well as freezing rate were compared using control samples (4°C) and samples stored at either -20°C or -80°C as well as samples first snap frozen in liquid nitrogen (-196°C) prior to storage at -80°C. All samples were thawed at 37.5°C to testing temperature (22°C). Complex stiffness and hysteresis characterized load resistance and damping properties using a non-destructive, low force magnitude, dynamic indentation protocol spanning a broad loading rate range to identify the dynamic viscoelastic properties of cartilage. RESULTS: Stiffness levels remained unchanged with exposure to the various subzero temperatures. Hysteresis increased in samples snap frozen at -196°C and stored at -80°C, though remained unchanged with exposure to the other storage temperatures. CONCLUSIONS: Mechanical changes shown are likely due to ice lens creation, where frost heave effects may have caused collagen damage. That storage to -20°C and -80°C did not alter the mechanical properties of articular cartilage shows that when combined with a rapid thawing protocol to 37.5°C, the tissue may successfully be stored at subzero temperatures.
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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) | 0.000 | 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) | 0.003 | 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".