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Record W4297249101 · doi:10.1002/cpz1.565

Modulating Myogenesis: An Optimized <i>In Vitro</i> Assay to Pharmacologically Influence Primary Myoblast Differentiation

2022· article· en· W4297249101 on OpenAlexaff
Cordell A. VanGenderen, Jules A Granet, Romina L. Filippelli, Yiyang Liu, Natasha C. Chang

Bibliographic record

VenueCurrent Protocols · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsMYF5MyoDMyogenesisMyogenic regulatory factorsMyosinMyogeninBiologyCellular differentiationMyocyteMyogenic contractionCell biologyMyoD ProteinGeneticsGeneEndocrinology

Abstract

fetched live from OpenAlex

The intentional pharmacological manipulation of myogenesis is an important technique for understanding the underlying mechanisms of muscle differentiation and disease etiology. Using the pharmacological agent metformin as an example molecule, we present a systematic approach to examine the impact of pharmacological agents on the myogenic program. This consists of optimizing the in vitro differentiation of primary myoblast cells followed by the generation of a dose-response curve for a respective pharmaceutical. To assess myogenic differentiation, we utilized three approaches (incorporating both transcriptional and protein techniques) to assess the effects of biologically active agents on the in vitro differentiation of primary myogenic progenitors. First, the immunofluorescent visualization of myosin heavy chain (MYHC), which is expressed in differentiated myofibers, is used to obtain the fusion index, a quantitative read-out of differentiation efficiency. Second, quantitative reverse transcription PCR (RT-qPCR) reveals the expression of myogenic factors (Pax7, Myf5, Myod, Myog, Myh2) at the transcript level. Third, western blotting is used to assess the protein expression levels of the myogenic markers (PAX7, MYF5, MYOD, MYOG, and MYHC). By monitoring the expression of these various myogenic factors during the differentiation process, the relative cellular state and differentiation status between samples can be determined. Combined, these approaches enable the successful assessment of the impact of pharmacological agents on myogenic differentiation. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Immunofluorescence assay for qualitative and quantitative assessment of pharmacological agents on in vitro myogenic differentiation Support Protocol 1: Evaluating myogenic gene expression by RT-qPCR Support Protocol 2: Evaluating myogenic protein expression by western blot.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.023
GPT teacher head0.327
Teacher spread0.304 · 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
GenreMethods

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

Citations11
Published2022
Admission routes1
Has abstractyes

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