Challenges and future perspective of antisense therapy for spinal muscular atrophy: A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spinal muscular atrophy (SMA), the most common genetic cause of infantile death, is caused by a mutation in the survival of motor neuron 1 gene (SMN1), leading to the death of motor neurons and progressive muscle weakness. SMN1 normally produces an essential protein called SMN. Although humans possess a paralogous gene called SMN2, ∼90% of the SMN it produces is non-functional. This is due to a mutation in SMN2 that causes the skipping of a required exon during splicing of the pre-mRNA. The first treatment for SMA, nusinersen (brand name Spinraza), was approved by the FDA in 2016 and by the EMU in 2017. Nusinersen is an antisense oligonucleotide-based therapy that alters the splicing of SMN2 to make functional full-length SMN protein. Despite the recent advancements in antisense oligonucleotide therapy and SMA treatment development, nusinersen is faced with a multitude of challenges, such as intracellular and systemic delivery. In recent years, the use of peptide-conjugated phosphorodiamidate morpholino oligomers (PPMOs) in antisense therapy has gained interest. These are antisense oligonucleotides conjugated to cell-penetrating peptides such as Pips and DG9, and they have the potential to address the challenges associated with delivery. This review focuses on the historic milestones, development, current challenges, and future perspectives of antisense therapy for SMA.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it