Rapid response to an emerging infectious disease – Lessons learned from development of a synthetic DNA vaccine targeting Zika virus
Bibliographic record
Abstract
Vaccines are considered one of the greatest advances in modern medicine. The global burden of numerous infectious diseases has been significantly reduced, and in some cases, effectively eradicated through the deployment of specific vaccines. However, efforts to develop effective new vaccines against infectious pathogens such as influenza, Human immunodeficiency virus (HIV), dengue virus (DENV), chikungunya virus (CHIKV), Ebola virus, and Zika virus (ZIKV) have proven challenging. Zika virus is a mosquito-vectored flavivirus responsible for periodic outbreaks of disease in Africa, Southeast Asia, and the Pacific Islands dating back over 50 years. Over this period, ZIKV infections were subclinical in most infected individuals and resulted in mild cases of fever, arthralgia, and rash in others. Concerns about ZIKV changed over the past two years, however, as outbreaks in Brazil, Central American countries, and Caribbean islands revealed novel aspects of infection including vertical and sexual transmission modes. Cases have been reported showing dramatic neurological pathologies including microcephaly and other neurodevelopmental problems in babies born to ZIKV infected mothers, as well as an increased risk of Guillain-Barre syndrome in adults. These findings prompted the World Health Organization to declare ZIKV a public health emergency in 2016, which resulted in expanded efforts to develop ZIKV vaccines and immunotherapeutics. Several ZIKV vaccine candidates that are immunogenic and effective at blocking ZIKV infection in animal models have since been developed, with some of these now being evaluated in the clinic. Additional therapeutics under investigation include anti-ZIKV monoclonal antibodies (mAbs) that have been shown to neutralize infection in vitro as well as protect against morbidity in mouse models of ZIKV infection. In this review, we summarize the current understanding of ZIKV biology and describe our efforts to rapidly develop a vaccine against ZIKV.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".