Pre-clinical development of a vaccine against Lassa fever
Bibliographic record
Abstract
Lassa virus (LASV) is a persistent global health threat that causes about half a million cases of Lassa fever each year in Western Africa. Although most cases are mild, the disease can cause significant morbidity and results in as many as 5,000 deaths per year. Since 2015, Nigeria has been experiencing a severe and extended outbreak of Lassa fever, raising concerns that it could spill over into other countries and reach a magnitude similar to the West African Ebola outbreak of 2013-2016. Despite the burden that Lassa fever places on public health, both in Africa and around the world, there are still no clinically-approved therapeutics or vaccines to treat or prevent it. Nevertheless, a number of promising candidate vaccines have been developed over the last several years, and there is a growing political and social determination to drive at least one of these candidates towards licensure. This paper describes a LASV vaccine candidate that is being developed at Canada's National Microbiology Laboratory. Based on the same live attenuated vesicular stomatitis virus (VSV) vaccine platform that was used to produce the successful Ebola virus vaccine, the VSV-based LASV vaccine has been shown to elicit a potent and protective immune response against LASV. The vaccine shows 100% protection in the "gold-standard" nonhuman primate model of Lassa fever, inducing both humoral and cellular immune responses. Moreover, studies have shown that a single vaccination may offer universal protection against numerous different strains of the virus, and additional studies have shown that immunization with the VSV platform appears to be unaffected by pre-existing immunity to VSV. The next step in the development of the VSV-based LASV vaccine is phase I human clinical trials to assess vaccine safety and dosage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".