Permeability and Osmotic Parameters of Human Umbilical Vein Endothelial Cells and H9C2 Cells under Non-ideal Thermodynamic Assumptions: A Novel Iterative Fitting Method
Bibliographic record
Abstract
Cryopreservation is the use of very low subzero temperatures to preserve cells and tissues for later use. This is achieved by controlled cooling in the presence of cryoprotectants that moderate the amount of ice formed. Mathematical modeling of the cryopreservation process is a useful tool to investigate the different variables that affect the results of this process. The changing cell volume during cryopreservation can be modeled using cell membrane water and cryoprotectant permeabilities and the osmotically inactive fraction of the intracellular contents. These three cell-specific parameters have been found previously for different cell types under ideal and dilute assumptions, but biological solutions at subzero temperatures are far from ideal and dilute, especially when cryoprotectants are included. In this work, the osmotic virial equation is used to model the changing cell volume under non-ideal assumptions, and the intracellular environment is described using the grouped solute, which consists of all impermeant intracellular solutes grouped together, leading to two additional cell-specific parameters, the second and third osmotic virial coefficients of the grouped solute. Herein, we present a novel fitting method to efficiently determine these five cell-specific parameters by fitting kinetic cell volume data under non-ideal assumptions and report the results of applying this method to obtain the parameters for two cell types: human umbilical vein endothelial cells and H9C2 rat myoblasts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".