Probiotic Lactobacilli Limit Avian Influenza Virus Subtype H9N2 Replication in Chicken Cecal Tonsil Mononuclear Cells
Bibliographic record
Abstract
Low pathogenic avian influenza virus (LPAIV) H9N2 poses significant threat to animal and human health. The growing interest in beneficial effects of probiotic bacteria on host immune system has led to research efforts studying their interaction with cells of host immune system. However, the role of lactobacilli in inducing antiviral responses in lymphoid tissue cells requires further investigation. The objective of the present study was to examine the antiviral and immunostimulatory effects of lactobacilli bacteria on chicken cecal tonsils (CT) cells against H9N2 LPAIV. CT mononuclear cells were stimulated with probiotic Lactobacillus spp mixture either alone or in combination with a Toll-like receptor (TLR)21 ligand, CpG oligodeoxynucleotides (CpG). Pre-treatment of CT cells with probiotic lactobacilli, alone or in combination with CpG, significantly reduced H9N2 LPAIV replication. Furthermore, lactobacilli alone elicited cytokine expression, including IL-2, IFN-γ, IL-1β, IL-6, and IL-12, and IL-10, while when combined with CpG, a significantly higher expression of (interferon-stimulated gene (viperin)), IL-12, IL-6, CXCLi2, and IL-1β was observed. However, none of these treatments induced significant changes in nitric oxide production by CT cells. In conclusion, probiotic lactobacilli demonstrated a modulatory effect on CT cells, and this correlated with enhanced antiviral immunity and reduced H9N2 LPAIV viral replication.
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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".