Milk fermented with Lactobacillus rhamnosus R0011 induces a regulatory cytokine profile in LPS-challenged U937 and THP-1 macrophages
Bibliographic record
Abstract
Fermented dairy products have become attractive functional foods for the delivery of probiotics and their biologically active metabolites. The aim of this study was to examine the immunomodulatory activity of milk fermented with the probiotic lactic acid bacterium Lactobacillus rhamnosus R0011 (LrF) on macrophages challenged with lipopolysaccharide (LPS), a potent pro-inflammatory stimulus. To this end, human THP-1 or U937 monocytes were differentiated into resting macrophages then stimulated with LPS and co-incubated with the LrF or with milk controls. Levels of pro-inflammatory and immunoregulatory cytokines were determined by enzyme-linked immunosorbent assays. Culturing of LPS-stimulated U937 macrophages with either the whole or filtered LrF resulted in an increase in Interleukin (IL)-1Ra production relative to the negative control. THP-1 macrophages cultured with the LrF demonstrated an increase in LPS-induced IL-10 and IL-1β production, while production of LPS-induced IL-6, sCD54, IL-8, IL-1β, TNF-α, IL-12p70 and Transforming Growth Factor-β (TGF-β) was unaffected. Further, the LrF induced the expression of DC-SIGN and CD206, markers of immunoregulatory M2 macrophage polarization, in LPS-challenged THP-1 macrophages. Taken together, milk fermented with L. rhamnosus R0011 increased regulatory cytokine production from LPS-challenged U937 and THP-1 macrophages, while simultaneously up-regulating the production of IL-1β and expression of DC-SIGN and CD206, a profile characteristic of polarization into the immunoregulatory M2 macrophage phenotype.
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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".