Vaccination Strategies Based on Bacterial Self-Assembling Proteins as Antigen Delivery Nanoscaffolds
Bibliographic record
Abstract
Vaccination has saved billions of human lives and has considerably reduced the economic burden associated with pandemic and endemic infectious diseases. Notwithstanding major advancements in recent decades, multitude diseases remain with no available effective vaccine. While subunit-based vaccines have shown great potential to address the safety concerns of live-attenuated vaccines, their limited immunogenicity remains a major drawback that still needs to be addressed for their use fighting infectious illnesses, autoimmune disorders, and/or cancer. Among the adjuvants and delivery systems for antigens, bacterial proteinaceous supramolecular structures have recently received considerable attention. The use of bacterial proteins with self-assembling properties to deliver antigens offers several advantages, including biocompatibility, stability, molecular specificity, symmetrical organization, and multivalency. Bacterial protein nanoassemblies closely simulate most invading pathogens, acting as an alarm signal for the immune system to mount an effective adaptive immune response. Their nanoscale architecture can be precisely controlled at the atomic level to produce a variety of nanostructures, allowing for infinite possibilities of organized antigen display. For the bottom-up design of the proteinaceous antigen delivery scaffolds, it is essential to understand how the structural and physicochemical properties of the nanoassemblies modulate the strength and polarization of the immune responses. The present review first describes the relationships between structure and the generated immune responses, before discussing potential and current clinical applications.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".