Systemic Effects of Ingested Lactobacillus Rhamnosus: Inhibition of Mast Cell Membrane Potassium (IKCa) Current and Degranulation
Bibliographic record
Abstract
Exposure of the intestine to certain strains lactobacillus can have systemic immune effects that include the attenuation of allergic responses. Despite the central role of mast cells in allergic disease little is known about the effect of lactobacilli on the function of these cells. To address this we assessed changes in rat mast cell activation following oral treatment with a strain of Lactobacillus known to attenuate allergic responses in animal models. Sprague Dawley rats were fed with L. rhamnosus JB-1 (1×10(9)) or vehicle control for 9 days. Mediator release from peritoneal mast cells (RPMC) was determined in response to a range of stimuli. Passive cutaneous anaphylaxis (PCA) was used to assess mast cell responses in vivo. The Ca(2+) activated K(+) channel (KCa3.1) current, identified as critical to mast cell degranulation, was monitored by whole cell patch-clamp. L. rhamnosus JB-1 treatment lead to significant inhibition of mast cell mediator release in response to a range of stimuli including IgE mediated activation. Furthermore, the PCA response was significantly reduced in treated rats. Patch-clamp studies revealed that RPMC from treated animals were much less responsive to the KCa3.1 opener, DCEBIO. These studies demonstrate that Ingestion of L. rhamnosus JB-1 leads to mast cell stabilization in rats and identify KCa3.1 as an immunomodulatory target for certain lactobacilli. Thus the systemic effects of certain candidate probiotics may include mast cell stabilization and such actions could contribute to the beneficial effect of these organisms in allergic and other inflammatory disorders.
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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) | 0.001 | 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".