Potential Vaccine or Antimicrobial Reagents: Simple Systems for Producing Lambda Display Particles (LDP) and Sheathed Lambda DNA Vaccine Particles (LDNAP)
Bibliographic record
Abstract
The focus of this study was to explore phage display systems employing bacteriophage lambda (λ) and gene fusions to its capsid decoration protein gpD as reagent tools for tackling disease, particularly when fused to cathelicidins or defensins retaining biological activity. We briefly review the formative studies for considering that phage representatives could serve as reagent tools helping to combat disease. This includes early ideas for “phage therapy” and the use of “display or vector” phages for vaccine generation and bioassays. We compare gene-fusion lytic display systems where the fusion display gene is integrated within the viral genome with a surrogate system that exogenously provides the fusion-display protein for addition to phage capsid. Finally, we discuss the potential for vaccine vector phage particles, which are essentially sheathed DNA vaccines encapsulated within an environmentally protective capsid. We show how it is easily possible to produce fully coated LDP serving as single epitope vaccines, or antimicrobials, or to produce partially coated LDP without any complex bacteriophage genetic engineering, making the system available to all. We show that multiple, single epitope LDP vaccine reagents can be generated in a single infection lysate. We provide a system whereby either intracellular phage-plasmid substitution recombination, or by cloning, can generate sheathed DNA vaccine particles, termed LDNAP that have the advantage of a high-level eukaryotic expression cassette without incorporating plasmid resistance elements or other genes.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".