Gemini nanoparticles as a co-delivery system for antigen – CpG oligodeoxynucleotide adjuvant combination
Bibliographic record
Abstract
Enhancement of antigen-specific immune responses could be achieved by co-delivery of antigens and adjuvants. CpG oligodeoxynucleotides (unmethylated cytosine-guanine tandems in a specific sequence; ODNs) increase innate and antigen-specific immune responses. Effective co-delivery approaches of CpG ODNs and antigens to antigen-presenting cells are needed to achieve more potent antigen-specific immune responses. We evaluated both cellular and humoural immune responses triggered by hen egg lysozyme (HEL), a model antigen, and CpG ODNs formulated in gemini surfactant and dioleoyl phosphatidylcholine-based nanoparticles as an intradermal and topical co-delivery system in a murine animal model. Overall, intradermal injection of HEL/CpG nanoparticles induced a more pronounced Th1 immune response compared with the HEL and HEL/CpG topical formulations, as evidenced by the shift in the Th2 response triggered by the antigen alone to a mixed Th1/Th2 immune response and increased the presence of interferon-gamma (IFN-γ) secreting cells in the spleen. However, in case of topical administration, the nanoparticle formulation of HEL produced enhanced immune response and immunomodulation even without the incorporation of CpG. The HEL-specific immune response and Th1 bias demonstrated the advantage of co-delivery of HEL/CpG ODNs by gemini nanoparticles intradermally, whereas, the adjuvant effect of the nanoparticle delivery system itself was more significant after topical treatment.
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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".