Anti-neutrophil cytoplasmic antibodies (ANCA): Antigen interactions and downstream effects
Bibliographic record
Abstract
Neutrophils are the most abundant leukocytes in circulation and are key "first responders" in the immune response to infectious and non-infectious stimuli. Unlike other immune cells, neutrophils can mount a robust response (including a change in surface markers and the production of extracellular traps and reactive oxygen species) just minutes after sensing a disturbance. It has been speculated that, in some individuals, the activation of neutrophils inadvertently leads to the generation of anti-neutrophil cytoplasmic autoantibodies (ANCA) against particular neutrophil proteins (antigens) such as myeloperoxidase (MPO) and proteinase 3 (PR3). In these individuals, continuous ANCA-antigen interactions are thought to drive persistent activation of neutrophils, chronic immune activation, and disease, most notably, small vessel vasculitis. There are significant gaps however in our understanding of the underlying mechanisms and even the pathogenicity of ANCA given that vasculitis can develop in the absence of ANCA, and that ANCA have been found in circulation in other conditions with no apparent contribution to disease. These gaps are particularly evident in the context of human studies. Herein, we review knowledge on neutrophil-derived ANCA antigens PR3 and MPO, ANCA generation, and ANCA-antigen interaction(s) that may promote immune activation and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".