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Record W2144559222 · doi:10.1517/21678707.2015.1092868

Potential of antisense therapy for facioscapulohumeral muscular dystrophy

2015· article· en· W2144559222 on OpenAlexaff
Bo Bao, Toshifumi Yokota

Bibliographic record

VenueExpert Opinion on Orphan Drugs · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFacioscapulohumeral muscular dystrophyGene knockdownMuscular dystrophyHomeoboxMedicineGeneGenetic enhancementTranscription factorGeneticsBioinformaticsCancer researchBiology

Abstract

fetched live from OpenAlex

Introduction: Facioscapulohumeral muscular dystrophy (FSHD) is an autosomal dominant genetic disorder characterized by progressive muscle degeneration. Currently, no effective treatment exists for the disease. Although the causative gene for FSHD, the double homeobox protein 4 (DUX4) gene, was identified over recent years, little effort has been made to develop targeted therapies. This is due to a lack of understanding of the gene and pathways involved. However, with the recent discovery that overexpression of myopathic DUX4 gene causes FSHD pathogenesis, inhibition of DUX4 and its downstream molecule has emerged as a promising therapeutic strategy against FSHD.Areas covered: In this paper we will discuss and review the latest research in the area of antisense knockdown therapy for FSHD, as well as a variety of accompanying issues, including efficacy and potential of antisense oligonucleotides.Expert opinion: Very recently, an effective antisense knockdown therapy targeting the paired-like homeodomain transcription factor 1 (PITX1) gene has demonstrated success in a newly developed mouse model. Similar to DUX4, PITX1 is specifically up-regulated in FSHD affected muscles. As such, the same knockdown principle using oligonucleotide could be applied to suppress the aberrantly expressed DUX4 and PITX1 in FSHD patients.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.297
Teacher spread0.278 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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