High-Throughput Evaluation of Cryoprotective Agents for Mixture Effects That Reduce Toxicity
Bibliographic record
Abstract
Vitrification is a promising approach for cryopreserving complex biological structures such as organs. However, to prevent ice formation, high concentrations of cell-permeable cryoprotective agents (CPAs) are required, which can be highly toxic. The current reliance on a small number of CPAs limits optimization of low-toxicity compositions for vitrification. To address this, there is growing interest in uncovering novel chemicals with analogous protective qualities. This may not only enhance vitrification efficacy but also mitigate toxic effects. In the current study, we employed a high throughput method to assess the toxicity of 21 compounds at room temperature, both individually and in binary combinations. Our analysis revealed that toxicity increases with both exposure duration and concentration, and that several CPA combinations result in reduced overall toxicity. Notably, among all tested mixtures, four binary combinations-formamide/glycerol, dimethyl sulfoxide/1,3-propanediol, 1,2-propanediol/diethylene glycol, and 1,3-propanediol/diethylene glycol-produced a statistically significant decrease in toxicity, resulting in significantly higher viability for the 6 mol/kg mixture than both corresponding 6 mol/kg single CPA solutions. The high-throughput approach presented here will aid in building a comprehensive CPA toxicity database, which will improve our understanding of toxicity mechanisms and support the development of predictive models for identifying novel CPA mixtures with low toxicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".