Marburg virus infection in nonhuman primates: Therapeutic treatment by lipid-encapsulated siRNA
Bibliographic record
Abstract
Marburg virus (MARV) and the closely related filovirus Ebola virus cause severe and often fatal hemorrhagic fever (HF) in humans and nonhuman primates with mortality rates up to 90%. There are no vaccines or drugs approved for human use, and no postexposure treatment has completely protected nonhuman primates against MARV-Angola, the strain associated with the highest rate of mortality in naturally occurring human outbreaks. Studies performed with other MARV strains assessed candidate treatments at times shortly after virus exposure, before signs of disease are detectable. We assessed the efficacy of lipid nanoparticle (LNP) delivery of anti-MARV nucleoprotein (NP)-targeting small interfering RNA (siRNA) at several time points after virus exposure, including after the onset of detectable disease in a uniformly lethal nonhuman primate model of MARV-Angola HF. Twenty-one rhesus monkeys were challenged with a lethal dose of MARV-Angola. Sixteen of these animals were treated with LNP containing anti-MARV NP siRNA beginning at 30 to 45 min, 1 day, 2 days, or 3 days after virus challenge. All 16 macaques that received LNP-encapsulated anti-MARV NP siRNA survived infection, whereas the untreated or mock-treated control subjects succumbed to disease between days 7 and 9 after infection. These results represent the successful demonstration of therapeutic anti-MARV-Angola efficacy in nonhuman primates and highlight the substantial impact of an LNP-delivered siRNA therapeutic as a countermeasure against this highly lethal human disease.
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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".