Using engineering models to shorten cryoprotectant loading time for the vitrification of articular cartilage
Bibliographic record
Abstract
Osteochondral allograft transplantation can treat full thickness cartilage and bone lesions in the knee and other joints, but the lack of widespread articular cartilage banking limits the quantity of cartilage available for size and contour matching. To address the limited availability of cartilage, vitrification can be used to store harvested joint tissues indefinitely. Our group's reported vitrification protocol [Biomaterials 33 (2012) 6061-6068] takes 9.5 h to load cryoprotectants into intact articular cartilage on bone and achieves high cell viability, but further optimization is needed to shorten this protocol for clinical use. Herein, we use engineering models to calculate the spatial and temporal distributions of cryoprotectant concentration, solution vitrifiability, and freezing point for each step of the 9.5-h protocol. We then incorporate the following major design choices for developing a new shorter protocol: (i) all cryoprotectant loading solution concentrations are reduced, (ii) glycerol is removed as a cryoprotectant, and (iii) an equilibration step is introduced to flatten the final cryoprotectant concentration profiles. We also use a new criterion-the spatially and temporally resolved prediction of solution vitrifiability-to assess whether a protocol will be successful instead of requiring that each cryoprotectant individually reaches a certain concentration. A total cryoprotectant loading time of 7 h is targeted, and our new 7-h protocol is predicted to achieve a level of vitrifiability comparable to the proven 9.5-h protocol throughout the cartilage thickness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".