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Quantification of dynamic mitochondrial morphologies in myoblasts

2010· article· en· W3166905416 on OpenAlexafffund
Sobia Iqbal, David A. Hood

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOrganelleMyocyteC2C12MitochondrionReticular connective tissueSkeletal muscleCell biologyBiophysicsChemistryBiologyAnatomyMyogenesis

Abstract

fetched live from OpenAlex

Mitochondria are vital organelles, critical for energy supply and cell survival. Such functional versatility is paralleled by the structural complexity of the organelle. Mitochondria can compensate for alterations in energy requirements by adjusting their size and distribution within skeletal muscle. To quantify this in living cells, we used C2C12 myoblasts transfected with fluorescently labeled pEYFP‐Mito in order to track mitochondrial dynamics. We captured mitochondrial movements at 2 second intervals for an observation time of 5 minutes using real‐time imaging. Approximately 60% of the mitochondria localized in the periphery of myoblasts were punctate spheres less than 0.5μm 2 in area, while 19% of mitochondria were present as elongated reticular structures greater than 1.0μm 2 in area. These dynamic organelles also varied in their displacement, moving an average of 3.4μm from the origin. However, the total path length travelled by these organelles was approximately 4–5 fold greater, averaging 15μm within the 5 minutes time frame. Thus, mitochondrial morphology in myoblasts is under dynamic control. Understanding the changes in mitochondrial morphology will allow us to elucidate the underlying basis for mitochondrial reticular formation in skeletal muscle, and the effects of contractile activity. Supported by NSERC.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.260
Teacher spread0.247 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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