The effect of cell size distribution on predicted osmotic responses of cells.
Bibliographic record
Abstract
An understanding of the kinetics of the osmotic response of cells is important in understanding permeability properties of cell membranes and predicting cell responses during exposure to anisotonic conditions. Traditionally, a mathematical model of cell osmotic response is obtained by applying mass transport and Boyle-vant Hoff equations using numerical methods. In the usual application of these equations, it is assumed that all cells are the same size equal to the mean or mode of the population. However, biological cells (even if they had identical membranes and hence identical permeability characteristics--which they do not) have a distribution in cell size and will therefore shrink or swell at different rates when exposed to anisotonic conditions. A population of cells may therefore exhibit a different average osmotic response than that of a single cell. In this study, a mathematical model using mass transport and Boyle-van't Hoff equations was applied to measured size distributions of cells. Chinese hamster fibroblast cells (V-79W) and Madin-Darby canine kidney cells (MDCK), were placed in hypertonic solutions and the kinetics of cell shrinkage were monitored. Consistent with the theoretical predictions, the size distributions of these cells were found to change over time, therefore the selection of the measure of central tendency for the population may affect the calculated osmotic parameters. After examining three different average volumes (mean, median, and mode) using four different theoretical cell size distributions, it was determined that, for the assumptions used in this study, the mean or median were the best measures of central tendency to describe osmotic volume changes in cell suspensions.
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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 | 0.005 |
| 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 | 0.001 |
| 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".