Editorial: Innate immunity in vasculitis
Bibliographic record
Abstract
Innate immunity in vasculitisPrimary systemic vasculitis is a heterogeneous group of rare disorders characterized by inflammation and/or necrosis affecting the blood vessel wall as the primary target of the immune system (1).Blood vessels of different types and sizes may be affected by the inflammatory process that, in turn, may involve several organs and organ systems in multiple combinations (2).Deriving from the 2013 Chapel Hill Consensus Conference, primary systemic vasculitides are classified as large-vessel, medium-vessel, small-vessel and variable-vessel vasculitis based on the size of the blood vessels that are predominantly affected by the inflammatory process (3).Although systemic vasculitides are commonly considered autoimmune in nature owing to the presence of autoreactive antibodies in the majority of patients, the innate immune system also has an important role in the pathogenesis of systemic vasculitides.Innate immune cells including neutrophils, monocytes, macrophages, NK cells, dendritic cells, eosinophil, and gd T cells are found in inflammatory infiltrates in affected vessels and act as effectors driving inflammation and damage to vessel walls (4).A detailed understanding of innate immunity mechanisms that contribute to inflammation and damage in systemic vasculitis, however, is still lacking.In this Research Topic, Tao et al. developed a NETosis score model and identified six NETosis-related genes with potential predictive utility in antineutrophil cytoplasmic antibody (ANCA)-associated glomerulonephritis (ANCA-GN).The expression of NETosis-related genes had a significant positive correlation with particular immune processes in ANCA-GN involving chemokines (CCR), macrophages, T-cell inhibition and tumor-infiltrating lymphocytes, as well as an inverse correlation with kidney function.Regarding IgA vasculitis (IgAV), Qin et al. performed a bidirectional Mendelian randomization study to analyze the interaction between IgAV and different inflammatory factors including C-reactive protein (CRP), growth factors, chemokines, Frontiers in Immunology frontiersin.org01
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 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".