Enhancing cryopreservation of ex vivo 3D tumor models using vitrification strategies
Bibliographic record
Abstract
Abstract Microdissected tumor tissue explants (MDTs) are promising ex vivo models for oncology research but remain limited by poor preservation and the rapid loss of viability following resection. Here, we systematically optimized MDT cryopreservation using prostate-derived 49F and ovarian TOV112D tumor models. Before cryopreservation, we evaluated the effects of antioxidant supplementation, pre-cooling, and a short recovery period. MDTs were then preserved by either conventional slow-freezing or vitrification using ultra-rapid cooling. Following thawing, tissue morphology, apoptosis, and proliferative capacity were assessed relative to fresh controls. Vitrification improved morphological preservation and reduced apoptosis compared with slow-freezing, although recovery of proliferation differed between tumor models. Antioxidant supplementation enhanced post-thaw proliferation at optimal concentrations but induced toxicity at higher doses. Pre-cooling and a short pre-cryopreservation recovery period further improved post-thaw outcomes. Combining these parameters produced an optimized vitrification protocol that preserved up to 98% of the proliferative capacity of 49F MDTs and 69% of that of TOV112D MDTs relative to fresh controls. These findings establish optimized vitrification as a reproducible, high-yield strategy for preserving MDTs for downstream ex vivo oncology applications.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".