Impact of nucleation temperature and hydroxyethyl starch on ice crystal growth: Implications for cell viability during extreme temperature fluctuations
Bibliographic record
Abstract
Extreme temperature fluctuations during routine handling and shipping of cryopreserved cell products significantly compromise product quality in ways that extend beyond the duration and peak temperature of the fluctuation. The type of cryoprotectant used and the initial ice nucleation temperature influence ice crystal growth during rewarming events, in turn impacting cell survival. Using a cryomicroscope together with temperature profiles recorded in cord-blood units, ice crystal growth was tracked through five transient-warming events (TWEs) that peaked at -30 °C, -20 °C, or -10 °C. Initial freezing conditions were modified either by adding 6 % (w/v) hydroxyethyl starch (HES) or by lowering the ice-nucleation temperature by 10 °C. Across five TWEs, ice-crystal area saw the greatest increase when the peak rewarming temperature was -10 °C. Although adding HES further accelerated this recrystallization, it still protected Jurkat cells after a single TWE. Lowering the nucleation temperature also improved viability in samples warmed to -20 °C, regardless of HES supplementation. These findings show that ice crystal growth is not the sole cause of injury during transient rewarming; other temperature-dependent stresses also play a role. Importantly, careful optimisation of cryoprotectant composition and nucleation temperature can bolster cellular resilience to temperature excursions, potentially reducing quality losses during the storage and transport of cryopreserved therapeutics.
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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) | 0.000 | 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 | 0.000 |
| 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".