Small RNA drugs for prion disease: a new frontier
Bibliographic record
Abstract
INTRODUCTION: Prion diseases, also known as transmissible spongiform encephalopathies, are a group of neurodegenerative diseases that are invariably incurable. In fact, intense laboratory and clinical research have failed to discover effective treatments, to date, which delay the onset or progression of any neurodegenerative conditions, including those caused by infectious prions. It has become clear that profound changes in the brains of patients are evident long before clinical signs and it is at this stage that the disease is reversible and presents 'druggable' targets. However, research is beginning to uncover the molecular underpinnings involved in the early stages of disease pathogenesis. Targeting key genes and pathways using short non-coding RNA is a new avenue of exploratory research for the treatment of prion disease that holds much promise for the future. AREAS COVERED: This article reviews the novel approach of using RNA-based drugs as a therapeutic opportunity for prion disease. Furthermore, it discusses the challenges that currently exist in the development of these therapies and highlights the future opportunities in this area. EXPERT OPINION: Numerous challenges exist before this therapeutic option can be translated into effective treatments. First, the crucial genes and pathways targeted must be identified from the multitude of temporally and spatially altered genetic processes that occur during the disease. Second, patients must be before irreversible neuronal degeneration, that accompanies prion replication, has progressed. Finally, these small RNAs must be delivered to the affected region of the brain over long periods of time and without significant side effects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".