MétaCan
Menu
Back to cohort

Fluorescence as an alternative to light-scatter gating strategies to identify frozen–thawed cells with flow cytometry

2014· article· en· W2059323604 on OpenAlexafffund
Anthony Reardon, Janet A.W. Elliott, Locksley E. McGann

Bibliographic record

VenueCryobiology · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsFlow cytometryCytometryFluorescenceGatingBiophysicsOpticsBiologyPhysicsMolecular biology

Abstract

fetched live from OpenAlex

Flow cytometry is a key instrument in biological studies, used to identify and analyze cells in suspension. The identification of cells from debris is commonly based on light scatter properties as it has been shown that there is a relationship between forward scattered light and cell volume and this has become common practice in flow cytometry. Cryobiological conditions induce changes in cells that alter their light scatter properties. Cells with membrane damage from freeze-thaw stress produce lower forward scatter signals and may fall below standard forward scatter thresholds. In contrast to light scatter properties that cannot identify damaged cells from debris, fluorescent dyes used in membrane integrity and mitochondrial polarization assays are capable of labeling and discriminating all cells in suspension. Under cryobiological conditions, isolating cell populations is more effectively accomplished by gating on fluorescence rather than light scatter properties. This study shows the limitations of using forward scatter thresholds in flow cytometry to identify and gate cells after exposure to a freeze-thaw protocol and demonstrates the use of fluorescence as an alternative means of identifying and analyzing cells.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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 designBench or experimental
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

Citations26
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueCryobiologySame topicMicrofluidic and Bio-sensing TechnologiesFrench-language works237,207