SARS‐CoV‐2 Protein Nanoparticle Vaccines Formed In Situ From Lyophilized Lipids
Bibliographic record
Abstract
The receptor binding domain (RBD) of the SARS-CoV-2 Spike (S) glycoprotein is an appealing immunogen, but associated vaccine approaches must overcome the hapten-like nature of the compact protein and adapt to emerging variants with evolving RBD sequences. Here, a vaccine manufacturing methodology is proposed comprising a sterile-filtered freeze-dried lipid cake formulation that can be reconstituted with liquid proteins to instantaneously form liposome-displayed protein nanoparticles. Mannitol is used as a bulking agent and a small amount of Tween-80 surfactant is required to achieve reconstituted submicron particles that do not precipitate prior to usage. The lipid particles include an E. coli-derived monophosphoryl lipid A (EcML) for immunogenicity, and cobalt porphyrin-phospholipid (CoPoP) for antigen display. Reconstitution of the lipid cake with aqueous protein results in rapid conversion of the RBD into intact liposome-bound format prior to injection. Protein particles can readily be formed with sequent-divergent RBD proteins derived from the ancestral or Omicron strains. Immunization of mice elicits antibodies that neutralize respective viral strains. When K18-hACE2 transgenic mice are immunized and challenged with ancestral SARS-CoV-2 or the Omicron BA.5 variant, both liquid liposomes displaying the RBD and rapid reconstituted particles protect mice from infection, as measured by the viral load in the lungs and nasal turbinates.
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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".