GPI-GM-CSF protein transferred onto H5 influenza VLPs remains stably expressed and functionally active (P6168)
Bibliographic record
Abstract
Abstract Pathogenic H5N1, a lipid-enveloped influenza virus, is a pandemic threat. The use of virus-like particles (VLPs) as an alternative to current influenza vaccines is highly promising. VLPs are similar in structure to their live viral counterparts but do not contain viral genome that is required for replication; hence VLPs provide for a safe and immunogenic vaccine. Although the particulate nature of VLPs allows them to be highly immunogenic, the need for protection against heterotypic viruses still remains. Therefore, inclusion of immunostimulatory molecules (ISMs) onto the VLP surface can help to induce cross-protection against strains and provide for stronger immunity. We were able to show that GPI-anchored-GM-CSF can incorporate stably onto H5 influenza VLP surfaces after a simple and quick protein transfer method. This method allows for the incorporation of the GPI-anchor onto the surface of lipid-bilayered VLPs within a matter of hours and allows for the incorporation of multiple GPI-ISMs in a concentration-dependent manner. Furthermore, protein transferred VLPs were functional in leading to bone marrow derived cell proliferation compared to unmodified VLPs, and incorporated GM-CSF was as functional as equal concentrations of commercially available recombinant soluble GM-CSF. Therefore, VLPs expressing GPI-GM-CSF could lead to enhanced immunogenicity and antiviral immune responses compared to unmodified VLPs.
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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) | 0.000 | 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".