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Record W2187211125 · doi:10.3791/53183

Analysis of Zebrafish Larvae Skeletal Muscle Integrity with Evans Blue Dye

2015· article· en· W2187211125 on OpenAlexafffund
Sarah J. Smith, Eric J. Horstick, Ann E. Davidson, James J. Dowling

Bibliographic record

VenueJournal of Visualized Experiments · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick Children
KeywordsZebrafishEvans BlueSkeletal muscleCell biologyBiologyAnatomyPathologyNeuroscienceMedicinePhysiologyBiochemistry

Abstract

fetched live from OpenAlex

The zebrafish model is an emerging system for the study of neuromuscular disorders. In the study of neuromuscular diseases, the integrity of the muscle membrane is a critical disease determinant. To date, numerous neuromuscular conditions display degenerating muscle fibers with abnormal membrane integrity; this is most commonly observed in muscular dystrophies. Evans Blue Dye (EBD) is a vital, cell permeable dye that is rapidly taken into degenerating, damaged, or apoptotic cells; in contrast, it is not taken up by cells with an intact membrane. EBD injection is commonly employed to ascertain muscle integrity in mouse models of neuromuscular diseases. However, such EBD experiments require muscle dissection and/or sectioning prior to analysis. In contrast, EBD uptake in zebrafish is visualized in live, intact preparations. Here, we demonstrate a simple and straightforward methodology for performing EBD injections and analysis in live zebrafish. In addition, we demonstrate a co-injection strategy to increase efficacy of EBD analysis. Overall, this video article provides an outline to perform EBD injection and characterization in zebrafish models of neuromuscular disease.

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 score0.047
Threshold uncertainty score0.440

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 head0.027
GPT teacher head0.387
Teacher spread0.360 · 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

Citations22
Published2015
Admission routes2
Has abstractyes

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