Quantitative Analysis of the Efficacy and Potency of Novel Small Molecule Ice Recrystallization Inhibitors
Bibliographic record
Abstract
The effects of ice recrystallization are well-recognized throughout the literature. This phenomenon is the major cause for cellular damage during thawing of cells, ultimately reducing post-thaw viability and function. In this paper, we describe a method for quantifying the inhibitory effect on ice recrystallization of novel small molecules that are cryoprotectants for red blood cells. The method is ideally suited to the splat-cooling assay, where high-ice volume fractions are present. Using our method, we have derived first-order rate constants for the increase in the average crystal size based upon a “binning” approach of ice crystals as a function of size and time. Using this reliable metric, dose–response curves were constructed to obtain IC 50 values. Two very effective inhibitors of ice recrystallization, p -methoxyphenyl β- d -glucopyranoside (PMP-Glc) and p -bromophenyl β- d -glucopyranoside (pBrPh-Glc), had IC 50 values of 16.3 and 14.8 mM, respectively. Interestingly, the Hill slopes from these dose–response curves were 5.12 ± 0.81 for PMP-Glc and 3.12 ± 0.62 for pBrPh-Glc, suggesting that an element of cooperativity may be involved in the mechanism by which these compounds inhibit ice recrystallization. This is particularly interesting, as unlike antifreeze (glyco)proteins, these small molecules do not bind to the ice surface.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".