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Record W2173989824 · doi:10.1172/jci85002

Memory or amnesia: the dilemma of stem cell therapy in muscular dystrophies

2015· letter· en· W2173989824 on OpenAlexaff
Marco Sandri

Bibliographic record

VenueJournal of Clinical Investigation · 2015
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsInduced pluripotent stem cellMuscular dystrophyGenetic enhancementDuchenne muscular dystrophyBioinformaticsBiologyStem cellGene deliveryMyocyteNeuroscienceMedicineGeneGeneticsCell biologyEmbryonic stem cell

Abstract

fetched live from OpenAlex

Muscular dystrophies are monogenetic diseases that are often characterized by the degeneration of both cardiac and skeletal muscle. Gene therapy to correct the mutated gene has shown promise in both animal models and clinical trials; however, current gene delivery strategies are limited to the introduction of the corrected gene into only one tissue. Strategies to target multiple striated muscle types would provide a much-needed improvement for the treatment of muscular dystrophies. In this issue of the JCI, Quattrocelli and colleagues demonstrate that induced pluripotent stem cells (iPSCs) with a myogenic propensity are able to engraft into both cardiac and skeletal muscles. The authors also identified a novel pool of mesodermal iPSC-derived progenitors (MiPs). Moreover, the authors show that these MiPs are amenable to gene correction and can restore function in murine dystrophic models. Together, the results of this study provide an important advance in improving gene delivery to treat patients with muscular dystrophy.

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)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.022
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.005
Scholarly communication0.0020.005
Open science0.0020.001
Research integrity0.0220.025
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 head0.109
GPT teacher head0.362
Teacher spread0.253 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2015
Admission routes1
Has abstractyes

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