Differential effects of lactobacilli on activation and maturation of mouse dendritic cells
Bibliographic record
Abstract
Lactic acid bacteria (LAB) are of interest because of their potential to modulate immune responses. The effects of LAB range from regulation to stimulation of the immune system. A series of studies were performed in vitro to study the effects of six lactic acid bacteria (LAB), Lactobacillus helveticus LH-2, Lactobacillus acidophilus La-5, La-115, La-116 and La-14, and Lactobacillus salivarius, on maturation and activation of mouse dendritic cells. Production of tumour necrosis factor (TNF)-?, interleukin (IL)-6 and IL-10 by dendritic cells (DCs) was determined after treating cells with live LAB. The expression of DC maturation markers, CD80 and CD40, was also measured using flow cytometry after stimulation with LAB. In addition, the expression of Toll-like receptors (TLRs) 2, 4 and 9 by DCs stimulated with LAB was measured. Our results revealed that LAB act differentially on pro-inflammatory and anti-inflammatory cytokine production and induction of co-stimulatory molecules by DCs. Specifically, L. salivarius was found to be the most effective LAB to induce pro-inflammatory cytokine production and expression of co-stimulatory molecules. Moreover, La-14, La-116 and La-5 induced moderate maturation and activation of DCs. On the other hand, LH-2 and La-115 were the least effective lactobacilli to induce DC responses. The present study also revealed that L. salivarius was able to induce the expression of TLR2, 4 and 9 by DCs. In conclusion, various strains and species of LAB can differentially regulate DC activation and maturation, providing further evidence that these bacteria may have the ability to influence and steer immune responses in vivo.
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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) | 0.000 | 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 | 0.001 |
| 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".