Supraphysiological expression of survival motor neuron protein from an adenovirus vector does not adversely affect cell function
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is the most common inherited neurodegenerative disease that leads to infant mortality. It is caused by mutations in the survival motor neuron (SMN) protein resulting in death of alpha motor neurons. Increasing evidence suggests that several other tissues are also affected in SMA, including skeletal and cardiac muscle, liver, and pancreas, indicating that systemic delivery of therapeutics may be necessary for true disease correction. Due to the natural biodistribution of therapeutics, a level of SMN several-fold above physiological levels can be achieved in some tissues. In this study, we address whether supraphysiological levels of SMN adversely affects cell function. Infection of a variety of cell types with an adenovirus (Ad) vector encoding SMN leads to very high expression, but the resulting protein correctly localizes within the cell, and associates with normal cellular partners. Although SMN affects transcription of certain target genes and can alter the splicing pattern of others, we did not observe any difference in select target gene splicing or expression in cells overexpressing SMN. However, normal human fibroblasts treated with Ad-SMN showed a slight reduction in growth rate, suggesting that certain cell types may be differently impacted by high levels of SMN.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".