Induction of mucosal immunity through systemic immunization: Phantom or reality?
Bibliographic record
Abstract
Generation of protective immunity at mucosal surfaces can greatly assist the host defense against pathogens which either cause disease at the mucosal epithelial barriers or enter the host through these surfaces. Although mucosal routes of immunization, such as intranasal and oral, are being intensely explored and appear promising for eliciting protective mucosal immunity in mammals, their application in clinical practice has been limited due to technical and safety related challenges. Most of the currently approved human vaccines are administered via systemic (such as intramuscular and subcutaneous) routes. Whereas these routes are acknowledged as being capable to elicit antigen-specific systemic humoral and cell-mediated immune responses, they are generally perceived as incapable of generating IgA responses or protective mucosal immunity. Nevertheless, currently licensed systemic vaccines do provide effective protection against mucosal pathogens such as influenza viruses and Streptococcus pneumoniae. However, whether systemic immunization induces protective mucosal immunity remains a controversial topic. Here we reviewed the current literature and discussed the potential of systemic routes of immunization for the induction of mucosal immunity.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".