Porcine Dendritic Cells as an In Vitro Model to Assess the Immunological Behaviour of Streptococcus suis Subunit Vaccine Formulations and the Polarizing Effect of Adjuvants
Bibliographic record
Abstract
An in vitro porcine bone marrow-derived dendritic cell (DC) culture was developed as a model for evaluating immune polarization induced by adjuvants when administered with immunogens that may become vaccine candidates if appropriately formulated. The swine pathogen Streptococcus suis was chosen as a prototype to evaluate proposed S. suis vaccine candidates in combination with the adjuvants Poly I:C, Quil A ®, Alhydrogel ®, TiterMax Gold ® and Stimune ®. The toll-like receptor ligand Poly I:C and the saponin Quil A ® polarized swine DC cytokines towards a type 1 phenotype, with preferential production of IL-12 and TNF-α. The water-in-oil adjuvants TiterMax Gold ® and Stimune ® favoured a type 2 profile as suggested by a marked IL-6 release. In contrast, Alhydrogel ® induced a type 1/type 2 mixed cytokine profile. The antigen type differently modified the magnitude of the adjuvant effect, but overall polarization was preserved. This is the first comparative report on swine DC immune activation by different adjuvants. Although further swine immunization studies would be required to better characterize the induced responses, the herein proposed in vitro model is a promising approach that helps assessing behaviour of the vaccine formulation rapidly at the pre-screening stage and will certainly reduce numbers of animals used while advancing vaccinology science.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".