H5N1 influenza virus-like particles produced by transient expression in mammalian cells induce humoral and cellular immune responses in mice
Bibliographic record
Abstract
Vaccination is an effective way to protect from influenza virus infection. Among the new candidates of influenza vaccines, influenza virus-like particles (VLPs) seem to be promising. Here, we generated 2 types of H5N1 influenza VLPs by co-expressing influenza virus Env (envelope protein) and murine leukemia virus (MLV) Gag-Pol. VLPs generated by co-transfection of pHCMV-wtH5 or pHCMV-mtH5 with pSV-Mo-MLVgagpol and pHCMV-N1 were named as wtH5N1 VLPs or mtH5N1 VLPs. The plasmid of pHCMV-wtH5 encoded the wild-type hemagglutinin (HA) (wtH5) from A/swine/Anhui/ca/2004 (H5N1) with a multibasic cleavage site, while pHCMV-mtH5 encoded the modified mutant-type (mtH5) with a monobasic cleavage site. Influenza virus HA VLPs were characterized and equal amounts of them were used to immunize mice subcutaneously, intraperitoneally, or intramuscularly. The levels of HA-specific IgG1, IFN-γ, and neutralization antibodies were significantly induced in mice immunized with wtH5N1 VLPs or mtH5N1 VLPs via all 3 routes, while HA-specific IgG2a was barely detectable. IL-4 secretion was detected in mice subcutaneously immunized with wtH5N1 VLPs or mtH5N1 VLPs, or intramuscularly immunized with mtH5N1 VLPs. Our results indicated that both H5N1 influenza VLPs could induce specific humoral and cellular immune responses in immunized mice. In conclusion, our study provides helpful information for designing new candidate vaccines against H5N1 influenza viruses.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".