Current research for a vaccine against Lassa hemorrhagic fever virus
Bibliographic record
Abstract
Lassa virus (LASV) is a rodent-borne arenavirus endemic to several West African countries that causes Lassa fever (LF). LF is typically mild but it can cause severe disease characterized by hemorrhagic fever and multi-organ failure. A current outbreak of LASV in Nigeria has seen greater than 300 cases with a case fatality rate of 22%. Currently, there are limited treatment options and no vaccine candidates are approved to prevent LASV infection. The Coalition for Epidemic Preparedness Innovations has identified LASV as an emerging pathogen of high consequence and this has resulted in a push for several preclinical vaccine candidates to be advanced toward clinical trials. Here, we discuss several important aspects of LASV infection including immunobiology, immune evasion, and correlates of protection against LF, which have been identified through animal models and human infections. In addition, we discuss several vaccine candidates that have shown efficacy in animal models that could be advanced toward clinical trials. The increased fatality rate seen in the recent LASV outbreak in Nigeria highlights the importance of developing effective treatment and prevention strategies against LF. The spike in LASV cases seen in West Africa has the potential for increased mortality and human-to-human transmission, making the development and testing of effective vaccines for LASV critical.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".