Bioactive molecules in the &lt;10kDa fraction of the <i>Lactobacillus helveticus</i>R0389 and <i>Lactobacillus rhamnosus R0011</i> secretomes down-regulate IL-8 production by HT-29 human intestinal epithelial cells induced by pro-inflammatory stimuli (MUC9P.752)
Bibliographic record
Abstract
Abstract The human gut microbiome has recently been associated with maintaining intestinal epithelial cell (IEC) homeostasis at the gastrointestinal interface by modulating host mucosal immune responses through varied mechanisms. The aim of this study was to compare the immunomodulatory activities of the L. helveticusR0389 and L. rhamosus R0011 secretomes on IEC. The human IEC line HT-29 was activated with a range of innate immune stimulants, to allow delineation of differential effects of the secretomes on Interleukin-8 (IL-8) production induced by varied stimuli. HT-29 IEC were co-incubated with either L. helveticusR0389 or L. rhamnosus R0011 secretomes and one of the following pro-inflammatory stimuli: interleukin 1-β (IL-1β), tumor necrosis factor-α (TNF-α); the toll-like receptor (TLR) agonists poly (I:C) (TLR3) or lipopolysaccharide (LPS) (TLR4), and alterations in IL-8 production were measured via enzyme-linked immunosorbant assay (ELISA). The L. helveticus R0389 and L. rhamnosus R0011 secretomes were also subjected to size fractionation to determine the size of the bioactive constituent of the secretome. Down-regulation of IL-8 was observed in HT-29 IEC co-incubated with the <10kDa fraction of the L. helveticusR0389 secretome or the L. rhamnosus R0011 secretome, for all of the innate immune stimulants tested. These results suggest that both Lactobacilli may secrete a low molecular weight bioactive molecule that modulates host immune activity by IEC.
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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".