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Record W2500647738 · doi:10.5582/irdr.2016.01056

Targeting mRNA for the treatment of facioscapulohumeral muscular dystrophy

2016· review· en· W2500647738 on OpenAlexafffund
Bo Bao, Rika Maruyama, Toshifumi Yokota

Bibliographic record

VenueIntractable & Rare Diseases Research · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMuscular Dystrophy CanadaUniversity of Alberta
FundersCanadian Institutes of Health ResearchMuscular Dystrophy CanadaNational Institutes of HealthUniversity of AlbertaWomen and Children's Health Research InstituteChildren's Health Research Institute
KeywordsFacioscapulohumeral muscular dystrophyGene knockdownMedicineMuscular dystrophyPathogenesisHomeoboxAntisense therapyGeneGeneticsTranscription factorBioinformaticsCancer researchPathologyOligonucleotideBiologyInternal medicine

Abstract

fetched live from OpenAlex

Facioscapulohumeral muscular dystrophy (FSHD) is an inherited autosomal dominant disorder characterized clinically by progressive muscle degeneration. Currently, no curative treatment for this disorder exists. FSHD patients are managed through physiotherapy to improve function and quality of life. Over the last two decades, FSHD has been better understood as a disease genetically characterized by a pathogenic contraction of a subset of macrosatellite repeats on chromosome 4. Specifically, several studies support an FSHD pathogenesis model involving the aberrant expression of the double homeobox protein 4 (DUX4) gene. Hence, potential therapies revolving around inhibition of DUX4 have been explored. One of the potential treatment options is the use of effective antisense oligonucleotides (AOs) to knockdown expression of the myopathic DUX4 gene and its downstream molecules including paired-like homeodomain transcription factor 1 (PITX1). Success in the suppression of PITX1 expression has already been demonstrated systemically in vivo in recent studies. In this article, we will review the pathogenesis of FSHD and the latest research involving the use of antisense knockdown therapy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

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

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 head0.053
GPT teacher head0.399
Teacher spread0.347 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2016
Admission routes2
Has abstractyes

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