A Novel Polymer Nanoparticle Polydimethyl Diallyl Ammonium Chloride as An Adjuvant Enhances the Immune Response of SARS‐CoV‐2 Subunit Vaccine
Bibliographic record
Abstract
Abstract The Coronavirus Disease 2019 (COVID‐19) pandemic caused by SARS‐CoV‐2 has a significant impact on global health and the economy. It has underscored the urgent need for a stable, easily produced and effective vaccine. This study presents a novel approach using SARS‐CoV‐2 spike (S) protein‐conjugated nanoparticles (NPs) in combination with cyclic GMP‐AMP (cGAMP) (S‐NPs‐cGAMP) as a subunit vaccine. When mice are immunized, the antiserum of S‐NPs‐cGAMP group exhibits a 16‐fold increase in neutralizing activity against a pseudovirus, compared to S protein group. Additionally, S‐NPs‐cGAMP induces even higher levels of neutralizing antibodies. Remarkably, the vaccine also triggers a robust humoral immune response, as evidenced by a notable elevation in virus‐specific IgG and IgM antibodies. Furthermore, after 42 days of immunization, there is an observed increase in specific immune cell populations in the spleen. CD3+CD4+ and CD3+CD8+T lymphocytes, as well as B220+CD19+ and CD3−CD49b+ NK lymphocytes, show an upward trend, indicating a positive cellular immune response. Moreover, the S‐NPs‐cGAMP demonstrates promising results against the Delta strain and exhibits good cross‐neutralization potential against other variants. These findings suggest that pDMDAAC NPs is potential adjuvant and could serve as a versatile platform for future vaccine development.
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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".