Anti‐phospholipid antibodies are elevated and functionally active in chronic rhinosinusitis with nasal polyps
Bibliographic record
Abstract
BACKGROUND: Polyps from patients with chronic rhinosinusitis with nasal polyps (CRSwNP) contain increased levels of autoreactive antibodies, B cells and fibrin deposition. Anti-phospholipid antibodies (APA) are autoantibodies known to cause thrombosis but have not been implicated in chronic rhinosinusitis (CRS). OBJECTIVE: -glycoprotein (anti-B2GP)) in nasal polyp (NP) tissue with tissue from control and CRS without nasal polyp (CRSsNP) patients, we tested whether NP antibodies affect coagulation, and correlate APAs with anti-dsDNA IgG and markers of coagulation. METHODS: Patient specimens were assayed for APA IgG, anti-dsDNA IgG and thrombin-anti-thrombin (TaT) complex by ELISA. Antibodies from a subset of specimens were tested for modified activated partial thromboplastin time (aPTT) measured on an optical-mechanical coagulometer. RESULTS: Anti-cardiolipin IgG in NP was 5-fold higher than control tissue (p < .0001). NP antibodies prolonged aPTT compared to control tissue antibodies at 400 µg/mL (36.7 s vs. 33.8 s, p = .024) and 600 µg/mL (40.9 s vs. 34.7 s, p = .0037). Anti-PE IgG antibodies were increased in NP (p = .027), but anti-B2GP IgG was not significantly higher (p = .084). All APAs correlated with anti-dsDNA IgG levels, which were also elevated (R = .77, .71 and .54, respectively, for anti-cardiolipin, anti-PE, and anti-B2GP; all p < .001), but only anti-cardiolipin (R = .50, p = .0185) and anti-PE (R = 0.45, p = .037) correlated with TaT complex levels. CONCLUSIONS: APA IgG antibodies are increased in NP and correlate with autoreactive tissue antibodies. NP antibodies have in vitro anti-coagulant activity similar to those observed in anti-phospholipid syndrome, suggesting that they may have pro-coagulant effects in polyp tissue.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".