In vitro assessment of immunomodulatory and anti-Campylobacter activities of probiotic lactobacilli
Bibliographic record
Abstract
The present study was undertaken to assess the antimicrobial activity of Lactobacillus spp. (L. salivarius, L. johnsonii, L. reuteri, L. crispatus, and L. gasseri) against Campylobacter jejuni as well as their immunomodulatory capabilities. The results demonstrated that lactobacilli exhibit differential antagonistic effects against C. jejuni and vary in their ability to elicit innate responses in chicken macrophages. All lactobacilli exerted inhibitory effects on C. jejuni growth, abrogated the production of the quorum sensing molecule autoinducer-2 (AI-2) by C. jejuni and inhibited the invasion of C. jejuni in human intestinal epithelial cells. Additionally, all lactobacilli, except L. reuteri, significantly reduced the expression of virulence-related genes in C. jejuni, including genes responsible for motility (flaA, flaB, and flhA), invasion (ciaB), and AI-2 production (luxS). All lactobacilli enhanced C. jejuni phagocytosis by macrophages and increased the expression of interferon (IFN)-γ, interleukin (IL)-1β, IL-12p40, IL-10, and chemokine (CXCLi2) in macrophages. Furthermore, L. salivarius, L. reuteri, L. crispatus, and a mixture of all lactobacilli significantly increased expression of the co-stimulatory molecules CD40, CD80, and CD86 in macrophages. In conclusion, these findings demonstrate that lactobacilli possess anti-Campylobacter and immunomodulatory activities. Further studies are needed to assess their protective efficacy against intestinal colonization by C. jejuni in broiler 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".