<i>Lactobacillus rhamnosus</i> GG inhibits neutrophil extracellular trap formation
Bibliographic record
Abstract
Alterations in the gut microbiota, referred to as dysbiosis, are strongly associated with chronic inflammatory bowel diseases. Neutrophils are one of the earliest cells to respond to an inflammatory stimulus and, in the inflamed intestine, constitute a significant proportion of cells that are recruited. Whereas probiotics have been shown to exert beneficial effects on gut homeostasis, their effect on neutrophil extracellular trap (NET) formation remains undefined. Therefore, the aim of this study was to determine the effects of the probiotic Lactobacillus rhamnosus GG (LGG) on the dynamics of NET formation. Bone marrow‐derived neutrophils (BMDN) were isolated from C57BL6 mice and co‐incubated with LGG (1 h, 37°C, 5% CO2) prior to stimulation with phorbol 12‐myristate 13‐acetate (PMA, 100 nM), which is an inducer of NET formation. Measurements of extracellular DNA (Sytox Green, 5 nM) and immunofluorescence microscopy staining for DNA, elastase and histones demonstrated that LGG mediated a concentration‐dependent reduction in NET‐osis. Given the proximity of recruited neutrophils to the lumen of the inflamed gut and the resident intestinal microbiota, it is important to define how neutrophil‐microbe interactions affect the local microenvironment. This work was funded by CIHR (operating grant MOP‐89894) .
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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".