Effect of Ice Nucleation and Cryoprotectants during High Subzero-Preservation in Endothelialized Microchannels
Bibliographic record
Abstract
Cryopreservation is of significance in areas including tissue engineering, regenerative medicine, and organ transplantation. We investigated endothelial cell attachment and membrane integrity in a microvasculature model at high subzero temperatures in the presence of extracellular ice. The results show that in the presence of heterogeneous extracellular ice formation induced by ice nucleating bacteria, endothelial cells showed improved attachment at temperature minimums of −6 °C. However, as temperatures decreased below −6 °C, endothelial cells required additional cryoprotectants. The glucose analog, 3- O -methyl- d -glucose (3-OMG), rescued cell attachment optimally at 100 mM (cells/lane was 34, as compared to 36 for controls), while 2% and 5% polyethylene glycol (PEG) were equally effective at −10 °C (88% and 86.4% intact membranes). Finally, endothelialized microchannels were stored for 72 h at −10 °C in a preservation solution consisting of the University of Wisconsin (UW) solution, Snomax, 3-OMG, PEG, glycerol, and trehalose, whereby cell attachment was not significantly different from unfrozen controls, although membrane integrity was compromised. These findings enrich our knowledge about the direct impact of extracellular ice on endothelial cells. Specifically, we show that, by controlling the ice nucleation temperature and uniformity, we can preserve cell attachment and membrane integrity. Further, we demonstrate the strength of leveraging endothelialized microchannels to fuel discoveries in cryopreservation of thick tissues and solid organs.
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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) | 0.000 | 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 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".