Effects of Lactobacilli on Cytokine Expression by Chicken Spleen and Cecal Tonsil Cells
Bibliographic record
Abstract
Lactobacillus acidophilus, Lactobacillus reuteri, and Lactobacillus salivarius are all normal residents of the chicken gastrointestinal tract. Given the interest in using probiotic bacteria in chicken production and the important role of the microbiota in the development and regulation of the host immune system, the objective of the current study was to examine the differential effects of these bacteria on cytokine gene expression profiles of lymphoid tissue cells. Mononuclear cells isolated from cecal tonsils and spleens of chickens were cocultured with one of the three live bacteria, and gene expression was analyzed via real-time quantitative PCR. All three lactobacilli induced significantly more interleukin 1beta (IL-1beta) expression in spleen cells than in cecal tonsil cells, indicating a more inflammatory response in the spleen than in cecal tonsils. In cecal tonsil cells, substantial differences were found among strains in the capacity to induce IL-12p40, IL-10, IL-18, transforming growth factor beta4 (TGF-beta4), and gamma interferon (IFN-gamma). In conclusion, we demonstrated that L. acidophilus is more effective at inducing T-helper-1 cytokines while L. salivarius induces a more anti-inflammatory response.
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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".