Combination of Modified NS1 and NS3 as a Novel Vaccine Strategy against Dengue Virus Infection
Bibliographic record
Abstract
Abstract Dengue virus (DENV) causes a range of illness, including dengue fever, dengue hemorrhagic fever, and dengue shock syndrome. DENV nonstructural protein (NS) 1 has been considered to be a desirable vaccine candidate for its ability to induce Ab and complement-dependent cytolysis of DENV-infected cells as well as to block the pathogenic effects of NS1. However a potential drawback of NS1 as a vaccine is that anti-DENV NS1 Abs can lead to endothelial cell damage and platelet dysfunction by antigenic cross-reactivity. Therefore, we modified the DENV NS1 by replacing the C-terminal cross-reactive epitopes with the corresponding region of Japanese encephalitis virus NS1 to generate a chimeric DJ NS1 protein. Active immunization with DJ NS1 induced a strong Ab response. To enhance cellular immunity, we further combined DJ NS1 with DENV NS3 to immunize mice and showed activation of Ag-specific CD4+ and CD8+ T cells in addition to Ab responses. We further detected NS3-specific CTL activities as well as CD107a expression of effector cells. Importantly, the protective effects attributed by DJ NS1 and NS3 immunization were demonstrated in a DENV-infected mouse model by reduced viral titers, soluble NS1 levels, mouse tail bleeding time, and vascular leakage at skin injection sites. Collectively, the results from this study reveal the humoral and cellular immune responses and the protective effects conferred by DJ NS1 and NS3 immunization in the mouse model of DENV infection and provide a potential strategy for dengue vaccine design.
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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".