Optimizing Equilibration Time to Enhance Post-Thaw Viability of Cryopreserved Zebrafish ( <i>Danio rerio</i> ) Ovarian Fragments
Bibliographic record
Abstract
Abstract Cryopreservation enables the long-term storage of viable biological material at ultra-low temperatures and forms the foundation for germplasm cryobanks that maintain valuable genetic lines of model organisms such as zebrafish ( Danio rerio ). However, reliable and reproducible cryopreservation protocols for fish germline stem cells remain difficult to develop, partly because key steps such as equilibration are often overlooked or assigned arbitrarily. Here, we optimized equilibration time for cryopreservation of zebrafish ovarian tissue. Ovarian fragments were equilibrated in 2 M methanol + 0.1 M glucose + 10% egg yolk for varying durations (15-120 minutes) before controlled slow-cooling and storage in liquid nitrogen. Post-thaw viability was assessed using a Trypan Blue exclusion assay. A 60-minute equilibration yielded the highest viability of ovarian cells in Experiment 1 (55.68 ± 1.74%), whereas a 30-minute equilibration yielded the highest viability in Experiment 2 (75.86 ± 2.44%), but was not significantly different from the 60-minute equilibration in Experiment 2 (75.58 ± 2.04%) ( p = 0.9983). Equilibration alone accounted for a 48.71% increase in post-thaw viability relative to controls. The framework presented here provides a reproducible method for determining species-specific equilibration optima and supports the development of effective germplasm cryobanks for both model and endangered fish species.
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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) | 0.002 | 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".