Muramyl dipeptide and mononuclear cell supernatant induce Langhans-type cells from human monocytes
Bibliographic record
Abstract
Muramyl dipeptide (MDP) in bacterial cell walls reportedly evokes epithelioid cell granulomas. We examined its effects on multinucleated-giant-cell (MGC) formation from monocytes. Supernatant of concanavalin A-stimulated peripheral blood mononuclear cells (conditioned medium) generated MGCs from monocytes. MDP significantly increased the fusion index of Langhans-type MGCs (LGCs) but did not affect total MGCs. N-Acetylmuramyl-L-alanyl-L-isoglutamine, an MDP analogue, had no effect on MGC formation. MGCs were produced by conditioned medium from CD14(++)/CD16(-) monocytes. MDP enhanced the LGC fusion index from CD14(++)/CD16(-) monocytes. MGCs were not produced from CD14(+)/CD16(+) monocytes or immature dendritic cells induced by granulocyte macrophage-colony stimulating factor (GM-CSF) and interleukin (IL) 4 and only weakly produced from macrophage (M)-CSF- or GM-CSF-induced macrophages. Added MDP did not generate MGCs from CD14(+)/CD16(+) monocytes or dendritic cells but enhanced LGC formation from macrophages. Because IFN-gamma, IL-3, and GM-CSF reportedly are important in LGC induction, we added anti-IFN-gamma, anti-IL-3, or anti-GM-CSF monoclonal antibody (mAb) concomitantly to the monocyte culture treated with conditioned medium alone or plus MDP. Anti-IFN-gamma mAb completely abrogated MGC generation, whereas anti-GM-CSF and anti-IL-3 mAbs significantly inhibited LGCs. These findings suggest that CD14(++)/CD16(-) monocytes are fused to form LGCs by MDP derived from granulomatous-disease-causing pathogens with inflammatory mediators such as IFN-gamma, IL-3, and GM-CSF.
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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 | 0.001 |
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".