PCEP enhances IgA mucosal immune responses in mice following different immunization routes with influenza virus antigens
Bibliographic record
Abstract
BACKGROUND: We previously demonstrated that polyphosphazenes, particularly PCEP, enhance immune responses in mice immunized subcutaneously and intranasally. The objective of the present study was to investigate the efficacy of polyphosphazenes as adjuvants when delivered through different routes of vaccine administration. METHODS: BALB/c mice were immunized through intranasal, subcutaneous, oral and intrarectal delivery with vaccine formulations containing either influenza X:31 antigen alone or formulated in PCEP. Serum and mucosal washes were collected and assayed for antigen-specific antibody responses by ELISA, while splenocytes were assayed for antigen-specific cytokine production by ELISPOT. RESULTS: Intranasal immunization with PCEP+X:31 induced significantly higher IgA titers in all mucosal secretions (lung, nasal, and vaginal) compared to the other routes. Serum analysis showed that all mice given the PCEP+X:31 combination showed evidence of enhanced IgG2a titers in all administered routes, indicating that PCEP can be effective as an adjuvant in enhancing systemic immune responses when delivered via different routes of administration. CONCLUSIONS: We conclude that PCEP is a potent and versatile mucosal adjuvant that can be administered in a variety of routes and effectively enhances systemic and local immune responses. Furthermore, intranasal immunization was found to be the best administration route for enhancing IgA titers, providing further evidence for the potential of PCEP as a mucosal adjuvant.
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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) | 0.000 | 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 | 0.001 |
| 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".