Low Density Granulocytes in ANCA Vasculitis Are Heterogenous and Hypo-Responsive to Anti-Myeloperoxidase Antibodies
Bibliographic record
Abstract
Low Density Granulocytes (LDGs), which appear in the peripheral blood mononuclear cell layer of density-separated blood, are seen in cancer, sepsis, autoimmunity, and pregnancy. Their significance in ANCA vasculitis (AAV) is little understood. As these cells bear the autoantigens associated with this condition and have been found to undergo spontaneous NETosis in other diseases, we hypothesized that they were key drivers of vascular inflammation. We found that LDGs comprise a 3-fold higher fraction of total granulocytes in active vs. remission AAV and disease controls. They are heterogeneous, split between cells displaying mature (75%), and immature (25%) phenotypes. Surprisingly, LDGs (unlike normal density granulocytes) are hyporesponsive to anti-myeloperoxidase antibody stimulation, despite expressing myeloperoxidase on their surface. They are characterized by reduced CD16, CD88, and CD10 expression, higher LOX-1 expression and immature nuclear morphology. Reduced CD16 expression is like that observed in the LDG population in umbilical cord blood and in granulocytes of humanized mice treated with G-CSF. LDGs in AAV are thus a mixed population of mature and immature neutrophils. Their poor response to anti-MPO stimulation suggests that, rather than being a primary driver of AAV pathogenesis, LDGs display characteristics consistent with generic emergency granulopoiesis responders in the context of acute inflammation.
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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".