ANTINEUTROPHIL EXTRACELLULAR TRAPS (NET) ANTIBODIES AND THEIR ASSOCIATION WITH DISEASE ACTIVITY AND SYSTEMIC LUPUS ERYTHEMATOSUS CLINICAL PHENOTYPES
Bibliographic record
Abstract
PV025 / #639 Poster Topic: AS04 - Biomarkers Background/Purpose Antineutrophil extracellular traps (NETs) antibodies have been observed in patients with lupus nephritis and may contribute to the pathogenic production and degradation of NETs in patients with lupus. However, the relationship of anti-NETs antibodies with clinical features of SLE patients have not been studied. Methods 87 patients fulfilled the ACR/EULAR 2019 classification criteria for SLE. Using ELISA, we quantified the plasmatic neutrophil elastase-DNA complexes as NETs remnants and the IgG anti-NETs antibodies in the same sample. 23 healthy controls were included to establish the cut-off point for anti-NETs antibodies, (0.076 arbitrary units), corresponding to 2 standard deviations above their mean optic density. We compared medians using Mann-Whitney U test. Associations between qualitative variables were assessed with Chi-square test. Correlations between quantitative variables were performed using Spearman’s rho. Results 35.6% of patients had positive anti-NETs antibodies (Table 1). The median of IgG anti-NETs antibodies was 0.30 AU (0-0.163 AU). Patients with anti-NETs antibodies were younger at disease onset and had prominent serological disease activity, with a higher prevalence of anti-double-stranded (ds)-DNA antibodies, at higher titers (148.2 mg/dl vs 35.6 mg/dl, p=0.015) and lower levels of C3 and C4 (Table 2). The positivity for anti-NETs antibodies was associated with lupus serositis (8 (25.8%) vs 6 (10.7%), P=0.022). Anti-NETs antibodies were positively correlated with the SLEDAI score (r=0.245, p <0.05) as well as titers of anti-dsDNA antibodies (r=0.290, p <0.01) (Figure 1, Table 3). Table 1. Clinical and Laboratory features of SLE patients Table 2. Comparison of clinical and laboratorial characteristics between patients with positive vs negative anti-NETs antibodies. Figure 1. Correlations of IgG anti-NET antibodies and SLEDAI, C3, C4, lymphocyte count and anti-dsDNA. Table 3. Anti-NETs antibodies Levels according to the clinical features of SLE patients. Conclusions IgG anti-NET antibodies were found in one-third of SLE patients. Higher prevalence was found in SLE patients with global and serological activity. This is the first description of the association between IgG anti-NET and clinical features of SLE. Their characterization might address their role as novel biomarkers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".