Investigation of type I interferon responses in ANCA-associated vasculitis
Bibliographic record
Abstract
Type I interferon (IFN) dysregulation is a major contributory factor in the development of several autoimmune diseases, termed type I interferonopathies, and is thought to be the pathogenic link with chronic inflammation in these conditions. Anti-neutrophil cytoplasmic antibody (ANCA)-Associated Vasculitis (AAV) is an autoimmune disease characterised by necrotising inflammation of small blood vessels. The underlying biology of AAV is not well understood, however several studies have noted abnormalities in type I IFN responses. We hypothesised that type I IFN responses are systemically dysregulated in AAV, consistent with features of a type I interferonopathy. To investigate this, we measured the expression of seven interferon regulated genes (IRGs) (ISG15, SIGLEC1, STAT1, RSAD2, IFI27, IFI44L and IFIT1) in peripheral blood samples, as well as three type I IFN regulated proteins (CXCL10, MCP-1 and CCL19) in serum samples from AAV patients, healthy controls and disease controls. We found no difference in type I IFN regulated gene or protein expression between AAV patients and healthy controls. Furthermore, IRG and IFN regulated protein expression did not correlate with clinical measurements of disease activity in AAV patients. Thus, we conclude that systemic type I IFN responses are not key drivers of AAV pathogenesis and AAV should not be considered a type I interferonopathy.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".