Lactic acid bacteria and bifidobacteria attenuate the proinflammatory response in intestinal epithelial cells induced by <i>Salmonella enterica</i> serovar Typhimurium
Bibliographic record
Abstract
Inflammation is a physiological response to infections and tissue injury; however, abnormal immune responses can give rise to chronic inflammation and contribute to disease progression. Various dietary components, including probiotic lactic acid bacteria and prebiotics, have the potential to modulate intestinal inflammatory responses. One factor in particular, the chemokine interleukin-8 (IL-8, CXCL-8), is one of the major mediators of the inflammatory response. The purpose of this study was to investigate modulation of the inflammatory host response induced by Salmonella enterica serovar Typhimurium DT104 in the presence of selected probiotics and lactic acid bacteria (LAB) isolated from human sources, dairy products, and farm animals. IL-8 gene expression and protein production in HT-29 cells were evaluated by real-time PCR and ELISA, respectively. Pre-incubation of HT-29 cells with Lactobacillus kefir IM002, Bifidobacterium adolescentis FRP 61, Bifidobacterium longum FRP 68 and FRP 69, Bifidobacterium breve FRP 334, and Leuconostoc mesenteroides IM080 significantly inhibited IL-8 secretion induced by Salmonella Typhimurium DT104. Co-culture of selected probiotics and Salmonella Typhimurium DT104 reduced IL-8 production, while potential probiotics and LAB had no effect on IL-8 secretion in HT-29 cells preincubated with Salmonella Typhimurium DT104 prior to adding probiotics. Lactobacillus kefir IM002 supernatant also significantly reduced IL-8 production. In conclusion, our study suggests that probiotic bifidobacteria and LAB modulate cytokine induction and possess anti-inflammatory properties; however, the effectiveness is strain dependent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".