Immune response in nasopharynx, lung, and blood elicited by experimental nasal pneumococcal vaccines containing live or heat-killed lactobacilli as mucosal adjuvants
Bibliographic record
Abstract
This work analyzes the humoral and cellular immune responses induced by live (LcV) and heat-killed (LcM) Lactobacillus casei associated with the pneumococcal antigen (P-Ag) at the nasopharynx level, considering nasal-associated lymphoid tissue (NALT) as the primary inductive site of the mucosal immune system, and lung and blood as effector sites. Levels of P-Ag IgA and IgG antibodies, main types of B and T cells, and cytokines in mucosal and systemic compartments were evaluated. The results showed that both LcM+P-Ag and LcV+P-Ag vaccines effectively induced IgA and IgG anti-P-Ag Abs in the upper and lower respiratory tract and plasma. These results correlated with increased IgA+ cells in NALT and lung that was induced by the experimental vaccines. Moreover, numbers of IgG+ cells increased in the blood. Profiles of inflammatory and regulatory cytokines were evaluated and their possible implications for the defense against pneumococci was assessed. Considering the overall results, the potential mechanisms of immune stimulation induced by LcM and LcV used as adjuvants are discussed. LcV and LcM showed similar effects on the immune system. Strain viability is not crucial for the stimulation of the antigen-specific immune response, and LcM is a convenient and effective 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) | 0.000 | 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 | 0.001 |
| 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".