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Record W4406717235 · doi:10.3389/fcell.2024.1526635

Multitasking muscle: engineering iPSC-derived myogenic progenitors to do more

2025· article· en· W4406717235 on OpenAlexafffund
Mark Stephen Hamer, Fábio Rossi

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsProgenitor cellBiologyRegeneration (biology)MyogenesisInduced pluripotent stem cellStem cellNeuroscienceRegenerative medicineTissue engineeringSkeletal muscleCell biologyMyocyteAnatomyEmbryonic stem cellGeneGenetics

Abstract

fetched live from OpenAlex

tissue regeneration has long been a goal in the skeletal muscle community. Today, protocols enable the production of potent, albeit immature, iMPs that resemble Pax7+ adult muscle stem cells. While muscular dystrophies are often the primary therapeutic target for these cells, an underexplored application is their use in treating traumatic muscle injuries. Notably absent from recent reviews on iMPs is the concept of engineering these cells to perform functions post-transplantation that non-transgenic cells cannot. Here, we highlight protocols to enhance the generation, purification, and maturation of iMPs, and introduce the idea of engineering these cells to perform functions beyond their normal capacities, envisioning novel therapeutic applications.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.003
GPT teacher head0.213
Teacher spread0.209 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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