Application of an M-cell-targeting ligand for oral vaccination induces efficient systemic and mucosal immune responses against a viral antigen
Bibliographic record
Abstract
Oral mucosal vaccination is an alternative method to overcome the pitfalls of current injection-based vaccines, such as pain and high cost of vaccination. It is a feasible and economic vaccine application, especially in developing countries. However, achieving effective antigen delivery into mucosal lymphoid organs and efficient immune stimulation are prerequisites to successful oral mucosal vaccination. One promising approach for oral mucosal vaccine development is exploring the potential of M cells via M-cell-targeting ligands that have the potential to deliver ligand-conjugated antigens into mucosal lymphoid organs and evoke conjugated-antigen-specific systemic and mucosal immune responses. Here, we investigated the M-cell-targeting ligand, Co1, in inducing specific immune responses against a pathogenic viral antigen, envelope domain III (EDIII) of dengue virus, to provide the foundation for oral mucosal vaccine development against the pathogen. After oral administration of Co1-conjugated EDIII antigens, we observed efficient antigen delivery into Peyer's patches. We also report the elicitation of EDIII-specific immunity in systemic and mucosal compartments by Co1 ligand (located in the C-terminus of EDIII). Furthermore, the antibodies induced by the ligand-conjugated EDIII antigen showed effective virus-neutralizing activity. The results of this study suggest that the M-cell-targeting strategy using Co1 ligand as a mucosal adjuvant may be applicable for developing oral vaccine candidates against pathogenic viral antigen.
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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".