Potential of antisense therapy for facioscapulohumeral muscular dystrophy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".