Innocuous Intracellular Ice Improves Survival of Frozen Cells
Bibliographic record
Abstract
Extensive efforts to avoid intracellular ice formation (IF) during freezing have been central to current methods used for the preservation and long-term storage of cells and tissues. In this study, we examined the effect of intracellular ice formation on the postthaw survival of V-79W fibroblast and MDCK epithelial cells using convection cryomicroscopy and controlled-rate freezing. V-79W and MDCK cells were cultured as single attached cells or as confluent cell monolayers. Postthaw cell survival was assessed using three different indices: the presence of an intact plasma membrane, the ability to reduce alamarBlue, and the capacity to form colonies in culture. Regulating the isothermal nucleation temperature was used to control the incidence of IIF in the model systems. We report that the presence of intracellular ice in confluent monolayers at high subzero temperatures does not adversely affect postthaw cell survival. Further, we show that in the absence of chemical cryoprotectants, the formation of intracellular ice alone improves the postthaw survival of cultured V-79W fibroblast and MDCK epithelial cells. Improved long-term storage of cells and tissues will result by incorporating innocuous intracellular ice formation into current strategies for cryopreservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".