Peptostreptococcus micros cell wall elicits a pro-inflammatory response in human macrophages
Bibliographic record
Abstract
Peptostreptococcus micros is a Gram-positive anaerobic bacterium associated with periodontitis, a chronic inflammatory disease affecting tooth-supporting tissues. In the present study, we investigated the response of human macrophages to stimulation with a cell wall preparation from P. micros. In addition, the effect of the preparation on the phosphorylation of macrophage kinases was studied. The preparation, which was non-toxic for macrophages, significantly increased the secretion of the pro-inflammatory cytokines TNF-alpha, IL-1beta and IL-6. It also increased the secretion of two potent chemokines IL-8 and, to a lesser extent, RANTES. Lastly, stimulation of macrophages by the P. micros cell wall preparation induced a significant increase in MMP-9 secretion but had no effect on the production of prostaglandin E2. The phosphorylation of macrophage kinases, including cAMP-dependent protein-serine kinase (PKA) catalytic subunit beta, G protein-coupled receptor-serine kinase 2, mitogen-activated protein-serine kinase p38 alpha (p38a MAPK), extracellular regulated protein-serine kinase 2 (ERK2) and Jun N-terminus protein-serine kinases (JNK), increased following stimulation with cell wall. In summary, our study showed that the P. micros cell wall preparation induced intracellular signaling pathways, leading to an increased production of pro-inflammatory cytokines, chemokines and MMP-9 by macrophages.
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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".