Effects of Lactic Acid Bacteria and Fermented Milks on Eicosanoid Production by Intestinal Epithelial Cells
Bibliographic record
Abstract
ABSTRACT: Fermented milk products produced with probiotic lactic acid bacteria (LAB) have attracted interest due to their potential health benefits. Probiotic bacteria have a range of immunomodulatory activity, interacting with a variety of cell types in the immune system. Interactions with intestinal epithelial cells (IEC) are an avenue through which probiotics and their fermented milks can influence production of key immunoregulatory molecules, including cytokines and eicosanoids. The eicosanoids, which include the prostaglandins (PGs), are lipid mediators implicated in both acute and chronic inflammatory processes. The primary objective of this study was to determine the ability of probiotic LAB and their ferments to interact with IEC and influence their eicosanoid production. Effects of LAB and their milk ferments on prostaglandin E 2 (PGE 2 ) and prostaglandin F 2α (PGF 2α ) production by human IEC lines were determined using a competitive enzyme immunoassay. LAB alone did not alter interleukin (IL)‐1β‐induced prostaglandin production by IEC. However, milk fermented with Lac‐tobacillus (L.) rhamnosus strain R0011 significantly suppressed IL‐1β‐induced levels of PGE 2 and PGF 2α , an effect which was counteracted by the addition of strain R0011. Milk ferments prepared withL. acidophilus strain R0052 were less effective in down‐regulation of PG production by IEC. Naltrexone, an opioid receptor antagonist, blocked the suppressive effects of L. rhamnosus R0011 milk ferments on PGF 2α production by IEC, suggesting that the bioactivity the ferments is opioid receptor‐mediated. These findings support immunomodulatory potential of fermented food components through interactions with intestinal epithelial cells.
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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 | 0.000 | 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) | 0.000 | 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".