Probiotic <i>Lactobacillus rhamnosus</i> Inhibits the Formation of Neutrophil Extracellular Traps
Bibliographic record
Abstract
Neutrophil extracellular traps (NETs) are an essential component of the antimicrobial repertoire and represent an effective means by which neutrophils capture, contain, and kill microorganisms. However, the uncontrolled or excessive liberation of NETs also damages surrounding cells and can contribute to disease pathophysiology. Alterations in the gut microbiota, as well as the presence of local and systemic markers of inflammation, are strongly associated with the manifestation of a spectrum of intestinal disorders, including chronic inflammatory bowel disease. Although probiotics exert beneficial effects on gut homeostasis, their direct effect on neutrophils, which are abundant in the setting of intestinal inflammation, remains unclear. In this study, we investigated the effects of nonpathogenic, enteropathogenic, and probiotic bacteria on the dynamics of NET formation. Using murine bone marrow-derived neutrophils and the neutrophil-differentiated human myeloid cell line d.HL-60, we demonstrate for the first time, to our knowledge, that probiotic Lactobacillus rhamnosus strain GG inhibits both PMA- and Staphylococcus aureus-induced formation of NETs. Moreover, probiotic L. rhamnosus strain GG had potent antioxidative activity: dampening reactive oxygen species production and phagocytic capacity of the neutrophils while protecting against cell cytotoxicity. Within the milieu of the gut, this represents a novel mechanism by which probiotics can locally dampen innate immune responses and confer desensitization toward luminal Ags.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".