Interrupted cooling protocols in cryopreservation: A review of fundamentals, methods and experimental outcomes for cells in suspension
Bibliographic record
Abstract
Interrupted cooling protocols are effective techniques used in cryobiology to study how cells respond to the freeze/thaw processes. By interrupting either rapid or slow cooling processes within specific sub-zero temperature ranges, researchers can improve the viability of cells after cryopreservation. These methods also provide insights into the differences between slow and rapid cooling injuries. Interrupted cooling protocols help researchers optimize critical factors affecting post-thaw viability and function, such as cooling profiles, cryoprotectants, and plunge temperatures. Over more than 60 years, numerous studies have used interrupted cooling protocols to understand the nature of damage during cooling and warming, identify the sub-zero temperature ranges where most damage occurs, and select appropriate types and concentrations of cryoprotectants for specific cell types. This paper begins with a review of crucial cryobiology concepts that are important to understand interrupted cooling, such as slow cooling and rapid cooling injuries, different types of cryoprotectants and how each type protects cells during cooling and warming, and the damage caused by cryoprotectants. This paper continues with providing a comprehensive review of studies that used different interrupted cooling protocols and to highlight their important findings for a broad range of cell types.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".