Oral vaccination stimulates neutrophil functionality and exerts protection in a Mycobacterium avium subsp. paratuberculosis infection model
Bibliographic record
Abstract
Mycobacterium avium subsp. paratuberculosis (Map) causes paratuberculosis (PTB), a granulomatous enteritis in ruminants that exerts high economic impact on the dairy industry worldwide. Current vaccines have shown to be cost-effective against Map and in some cases confer beneficial non-specific effects against other pathogens suggesting the existence of trained immunity. Although Map infection is mainly transmitted by the fecal-oral route, oral vaccination has not been deeply studied. Therefore, the aim of this study was to compare the oral route with a set of mycobacterial and non-mycobacterial vaccines with a subcutaneously administered commercially available vaccine. Training effects on polymorphonuclear neutrophils (PMNs) and homologous and heterologous in vivo protection against Map were investigated in the rabbit infection model. Oral vaccination with inactivated or live vaccines was able to activate mucosal immunity as seen by elevation of serum IgA and the expression of IL4 in peripheral blood mononuclear cells (PBMCs). In addition, peripheral PMN phagocytosis against Map was enhanced by vaccination and extracellular trap release against Map and non-related pathogens was modified by both, vaccination and Map-challenge, indicating trained immunity. Finally, PBMCs from vaccinated animals stimulated in vitro with Map antigens showed a rapid innate activation cytokine profile. In conclusion, our data show that oral vaccination against PTB can stimulate neutrophil activity and both innate and adaptive immune responses that correlate with protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".