Impact of nucleation temperature and hydroxyethyl starch on ice crystal growth: Implications for cell viability during extreme temperature fluctuations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it