Evaluation of immunomodulatory effects of <i>Lactobacillus rhamnosus</i> R0011 fermented milk on production of pro-atherogenic cytokines: a comparison between THP-1 and U937 human monocyte cells (CCR4P.208)
Bibliographic record
Abstract
Abstract Atherosclerosis has been identified as an inflammatory disease involving both innate and adaptive immune activity. Fermented dairy product consumption has been associated with decreased cardiovascular disease incidence. The purpose of this study was to elucidate whether milk fermented with Lactobacillus rhamnosus R0011 influences production of pro-inflammatory cytokines by different human monocyte models (THP-1 and U937 human monocytes). THP-1 or U937 cells were stimulated with lipopolysaccharide (LPS) to incite an inflammatory response similar to that seen in atherosclerotic plaques. Monocytes were either co- or pre-incubated with milk fermented with L. rhamnosus R0011 or with milk controls and an LPS challenge and effects on the production of IL-8, sCD54, IL-1Ra were measured. sCD54 production by U937 monocytes was significantly reduced by co-incubation with the L. rhamnosus R0011 milk ferment or with acidified milk controls, which was not seen with THP-1 monocytes. IL-8 production was significantly increased in both U937 and THP-1 monocytes pre-incubated with unfermented milk controls followed by LPS stimulation. This increase in IL-8 production was not observed when U937 cells were pre-incubated with milk fermented with L. rhamnosus R0011 or with acidified controls. These results suggest that fermentation modifies milk components to influence production of IL-8 and sCD54 and that the effects of milk ferments on monocytes vary with the cell type used.
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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".