Modulation of cytokine gene expression by selected<i>Lactobacillus</i>isolates in the ileum, caecal tonsils and spleen of<i>Salmonella</i>-challenged broilers
Bibliographic record
Abstract
Probiotics have been used to control Salmonella colonization in the chicken intestine. Recently, we demonstrated that certain selected Lactobacillus isolates were able to reduce Salmonella infection in the chicken spleen and liver as well as down-regulated Salmonella pathogenicity island 1 virulence gene expression in the chicken caecum. To further understand the mechanisms through which Lactobacillus protected chickens from Salmonella infection, the present study has investigated the Lactobacillus isolate(s)-induced host immune response of chickens to Salmonella enterica serovar Typhimurium infection. A thorough examination of cytokine gene expression in the ileum, caecal tonsils, and spleen on days 1 and 3 post-Salmonella infection showed a dynamic spatial and temporal response to Salmonella infection and Lactobacillus treatments. In most instances, it was evident that treatment of chickens with Lactobacillus isolates could significantly attenuate Salmonella-induced changes in the gene expression profile. These included the genes encoding pro-inflammatory cytokines [lipopolysaccharide-induced TNF factor, interleukin (IL)-6, and IL-8], T helper 1 cytokines [IL-12 and interferon (IFN)-γ], and T helper 2 cytokines (IL-4 and IL-10). Another important observation from the present investigation was that the response induced by a combination of Lactobacillus isolates was generally more effective than that induced by a single Lactobacillus isolate. Our results show that administration of certain selected Lactobacillus isolates can effectively modulate Salmonella-induced cytokine gene expression, and thus help reduce Salmonella infection in chickens.
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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) | 0.000 | 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".