SARS-CoV-2 spike-based virus-like particles incorporate influenza H1/N1 antigens and induce dual immunity in mice
Bibliographic record
Abstract
A vaccine effective against both SARS-CoV-2 and influenza A (IAV) viruses could represent a cost-effective strategy to reduce their combined public health burden as well as potential complications arising from co-infection. Based on previous findings that full-length SARS-CoV-2 spike (S) expression can induce high-level, enveloped VLP (eVLP) production in CHO cells, we tested whether IAV H1N1 hemagglutinin (H1) and neuraminidase (N1) could also be displayed on these particles. We found that co-incorporation of the IAV surface antigens in spike VLPs (S-VLPs) was highly efficient: upon transient co-expression of S + H1 or S + H1 + N1 in CHO cells, the resulting VLPs contained similar amounts of the SARS-CoV-2 S and IAV antigens. The self-assembled bivalent (S/H1) and trivalent (S/H1/N1) VLPs released into the culture media were purified by single-step chromatography using a S-VLP affinity resin. Western blot analysis and immuno‑gold labeling transmission electron microscopy (TEM) of purified VLPs confirmed the coexistence of S, H1 and N1 antigens in the same particles. Finally, we demonstrated that two doses of adjuvanted bivalent and trivalent VLPs elicit specific functional antibodies and cellular immunity in a mouse model, suggesting potential for combined SARS-CoV-2/IAV vaccine development.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".