UV-Inactivated rVSV-M2e-Based Influenza Vaccine Protected against the H1N1 Influenza Challenge
Bibliographic record
Abstract
BACKGROUND: To investigate the immune responses and protection ability of ultraviolet light (UV)-inactivated recombinant vesicular stomatitis (rVSV)-based vectors that expressed a fusion protein consisting of four copies of the influenza matrix 2 protein ectodomain (tM2e) and the Dendritic Cell (DC)-targeting domain of the Ebola Glycoprotein (EΔM), (rVSV-EΔM-tM2e). METHOD: In our previous study, we demonstrated the effectiveness of rVSV-EΔM-tM2e to induce robust immune responses against influenza M2e and protect against lethal challenges from H1N1 and H3N2 strains. Here, we used UV to inactivate rVSV-EΔM-tM2e and tested its immunogenicity and protection in BALB/c mice from a mouse-adapted H1N1 influenza challenge. Using Enzyme-Linked Immunosorbent Assay (ELISA) and Antibody-Dependent Cellular Cytotoxicity (ADCC), the influenza anti-M2e immune responses specific to human, avian and swine influenza strains induced were characterized. Likewise, the specificity of the anti-M2e immune responses induced in recognizing M2e antigen on the surface of the cell was investigated using Fluorescence-Activated Cell Sorting (FACS) analysis. RESULTS: Like the live attenuated rVSV-EΔM-tM2e, the UV-inactivated rVSV-EΔM-tM2e was highly immunogenic against different influenza M2e from strains of different hosts, including human, swine, and avian, and protected against influenza H1N1 challenge in mice. The FACS analysis demonstrated that the induced immune responses can recognize influenza M2 antigens from human, swine and avian influenza strains. Moreover, the rVSV-EΔM-tM2e also induced ADCC activity against influenza M2e from different host strains. CONCLUSIONS: These findings suggest that UV-inactivated rVSV-EΔM-tM2e could be used as an inactivated vaccine against influenza viruses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".