Restoring SMN Expression: An Overview of the Therapeutic Developments for the Treatment of Spinal Muscular Atrophy
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) is an autosomal recessive neurodegenerative disorder and one of the most common genetic causes of infant death. It is characterized by progressive weakness of the muscles, loss of ambulation, and death from respiratory complications. SMA is caused by the homozygous deletion or mutations in the survival of the motor neuron 1 (SMN1) gene. Humans, however, have a nearly identical copy of SMN1 known as the SMN2 gene. The severity of the disease correlates inversely with the number of SMN2 copies present. SMN2 cannot completely compensate for the loss of SMN1 in SMA patients because it can produce only a fraction of functional SMN protein. SMN protein is ubiquitously expressed in the body and has a variety of roles ranging from assembling the spliceosomal machinery, autophagy, RNA metabolism, signal transduction, cellular homeostasis, DNA repair, and recombination. Motor neurons in the anterior horn of the spinal cord are extremely susceptible to the loss of SMN protein, with the reason still being unclear. Due to the ability of the SMN2 gene to produce small amounts of functional SMN, two FDA-approved treatment strategies, including an antisense oligonucleotide (AON) nusinersen and small-molecule risdiplam, target SMN2 to produce more functional SMN. On the other hand, Onasemnogene abeparvovec (brand name Zolgensma) is an FDA-approved adeno-associated vector 9-mediated gene replacement therapy that can deliver a copy of the human SMN1. In this review, we summarize the SMA etiology, the role of SMN, and discuss the challenges of the therapies that are approved for SMA treatment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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