Effects of different cryopreservation parameters on the differences between trypan blue and fluorescent SYTO 13/GelRed assays
Bibliographic record
Abstract
Post-thaw cell viability assessment is very important in cryopreservation because it is the main assessment method used to optimize cryopreservation protocols for each cell type; hence, having standardized accurate, quick, and reliable assays for post-thaw cell viability measurements is of utmost importance. The trypan blue exclusion assay and nucleic-acid-binding fluorescence-based assays are two different methods for cell viability assessment. Both assays identify cells with damaged membranes by whether they let a compound enter the cell. In this study, these two assays are compared in the context of cryopreservation and the impacts of important cryopreservation parameters on the differences in measurements are investigated. H9c2 myoblasts were cryopreserved with different freezing protocols. Cell membrane integrities were measured immediately after thaw as well as after cryoprotectant removal by a hemocytometer-based trypan blue dye exclusion assay and a dual fluorometric SYTO 13/GelRed assay; and the results were compared. This study quantifies how (i) the absence or presence of different cryoprotectants, (ii) different cell-cryoprotectant incubation conditions, and (iii) the presence or removal of cryoprotectants after thaw affect the differences between these two viability assays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".