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Record W2063334334 · doi:10.1371/journal.pone.0028628

A Method for the Direct Identification of Differentiating Muscle Cells by a Fluorescent Mitochondrial Dye

2011· article· en· W2063334334 on OpenAlexafffund
Tetsuaki Miyake, John C. McDermott, Anthony O. Gramolini

Bibliographic record

VenuePLoS ONE · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsTed Rogers Centre for Heart ResearchYork UniversityUniversity of Toronto
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsGreen fluorescent proteinCell biologyTransfectionMyocyteFluorescence microscopeFluorescenceBiologyStainingCell cultureCellMuscle tissueMolecular biologyBiochemistryAnatomyGeneGenetics

Abstract

fetched live from OpenAlex

Identification of differentiating muscle cells generally requires fixation, antibodies directed against muscle specific proteins, and lengthy staining processes or, alternatively, transfection of muscle specific reporter genes driving GFP expression. In this study, we examined the possibility of using the robust mitochondrial network seen in maturing muscle cells as a marker of cellular differentiation. The mitochondrial fluorescent tracking dye, MitoTracker, which is a cell-permeable, low toxicity, fluorescent dye, allowed us to distinguish and track living differentiating muscle cells visually by epi-fluorescence microscopy. MitoTracker staining provides a robust and simple detection strategy for living differentiating cells in culture without the need for fixation or biochemical processing.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.031
GPT teacher head0.243
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2011
Admission routes2
Has abstractyes

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Same venuePLoS ONESame topicMuscle Physiology and DisordersFrench-language works237,207