RNAi mechanisms in Huntington’s disease therapy: siRNA versus shRNA
Bibliographic record
Abstract
Huntington's Disease (HD) is a genetically dominant trinucleotide repeat disorder resulting from CAG repeats within the Huntingtin (HTT) gene exceeding a normal range (> 36 CAGs). Symptoms of the disease manifest in middle age and include chorea, dystonia, and cognitive decline. Typical latency from diagnosis to death is 20 years. There are currently no disease-modifying therapies available to HD patients. RNAi is a potentially curative therapy for HD. A popular line of research employs siRNA or antisense oligonucleotides (ASO) to knock down mutant Huntingtin mRNA (mHTT). Unfortunately, this modality requires repeated dosing, commonly exhibit off target effects (OTEs), and exert renal and hepatic toxicity. In contrast, a single AAV-mediated short-hairpin RNA (shRNA) dose can last years with low toxicity. In addition, we highlight research indicating that shRNA elicits fewer OTEs than siRNA when tested head-to-head. Despite this promise, shRNA therapy has been held back by difficulties controlling expression (oversaturating cells with toxic levels of RNA construct). In this review, we compare RNAi modalities for HD and propose novel methods of optimizing shRNA expression and on-target fidelity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".