Protection Against Lethal Marburg Virus Infection Mediated by Lipid Encapsulated Small Interfering RNA
Bibliographic record
Abstract
BACKGROUND: Marburg virus (MARV) infection causes severe morbidity and mortality in humans and nonhuman primates. Currently, there are no licensed therapeutics available for treating MARV infection. Here, we present the in vitro development and in vivo evaluation of lipid-encapsulated small interfering RNA (siRNA) as a potential therapeutic for the treatment of MARV infection. METHODS: The activity of anti-MARV siRNAs was assessed using dual luciferase reporter assays followed by in vitro testing against live virus. Lead candidates were tested in lethal guinea pig models of 3 different MARV strains (Angola, Ci67, Ravn). RESULTS: Treatment resulted in 60%-100% survival of guinea pigs infected with MARV. Although treatment with siRNA targeting other MARV messenger RNA (mRNA) had a beneficial effect, targeting the MARV NP mRNA resulted in the highest survival rates. NP-718m siRNA in lipid nanoparticles provided 100% protection against MARV strains Angola and Ci67, and 60% against Ravn. A cocktail containing NP-718m and NP-143m provided 100% protection against MARV Ravn. CONCLUSIONS: These data show protective efficacy against the most pathogenic Angola strain of MARV. Further development of the lipid nanoparticle technology has the potential to yield effective treatments for MARV infection.
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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 | 0.000 |
| 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".