Inhibiting ice recrystallization and optimization of cell viability after cryopreservation
Bibliographic record
Abstract
The ice recrystallization inhibition activity of various mono- and disaccharides has been correlated with their ability to cryopreserve human cell lines at various concentrations. Cell viabilities after cryopreservation were compared with control experiments where cells were cryopreserved with dimethylsulfoxide (DMSO). The most potent inhibitors of ice recrystallization were 220 mM solutions of disaccharides; however, the best cell viability was obtained when a 200 mM d-galactose solution was utilized. This solution was minimally cytotoxic at physiological temperature and effectively preserved cells during freeze-thaw. In fact, this carbohydrate was just as effective as a 5% DMSO solution. Further studies indicated that the cryoprotective benefit of d-galactose was a result of its internalization and its ability to mitigate osmotic stress, prevent intracellular ice formation and/or inhibit ice recrystallization. This study supports the hypothesis that the ability of a cryoprotectant to inhibit ice recrystallization is an important property to enhance cell viability post-freeze-thaw. This cryoprotective benefit is observed in three different human cell lines. Furthermore, we demonstrated that the ability of a potential cryoprotectant to inhibit ice recrystallation may be used as a predictor of its ability to preserve cells 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) | 0.001 | 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".