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Effects of different cryopreservation parameters on the differences between trypan blue and fluorescent SYTO 13/GelRed assays

2024· article· en· W4392514369 on OpenAlexafffund
Elham Ashrafi, Dominic Sauvageau, Janet A.W. Elliott

Bibliographic record

VenueCryobiology · 2024
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsCryoprotectantCryopreservationTrypan blueViability assayHemocytometerBiologyCellContext (archaeology)ChemistryMolecular biologyBiochemistryCell biologyEmbryo

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.274
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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