ANTI-DFS70 ANTIBODIES AS A MARKER FOR EXCLUSION OF SYSTEMIC AUTOIMMUNE RHEUMATIC DISEASES
Bibliographic record
Abstract
PV033 / #298 Poster Topic: AS04 - Biomarkers Background/Purpose The DFS70 pattern, which is rare in patients (pts) with systemic autoimmune rheumatic diseases (SARDs), has been described as the second most common in serum healthy individuals (HI). The purpose of our study was the frequency of detection of anti-DFS70 in HI, pts with undifferentiated SARDs and systemic lupus erythematosus (SLE). Methods A total of 45 HI, 17 undifferentiated SARDs pts and 81 SLE pts were included in the study. Groups were comparable in gender and age among themselves. The diagnosis of SLE was performed according to the ACR/EULAR 2019 classification criteria. Serum samples were tested for anti-DFS70 (ANA HEp-2 ELITE/DFS70 knockout indirect immunofluorescence assay (IFA), “Trinity Biotech”, Ireland). Fluorescence titers ≥1:160 were considered as positive for ANA HEp-2 cell patterns. Results Positive results of the ANA study were found in 81 (100.0%) pts with SLE, in 16 (94.0%) pts with undifferentiated SARDs and 7 (15.6%) HI. Isolated antibodies to DFS70 detected in 4 (57.1%) HI, in 9 (56.2%) undifferentiated SARDs, but were not detected in SLE pts. Classical HEp-2 cell patterns (homogeneous AC-1, speckled AC-4,5, homogeneous+speckled AC-1,4,5, cytoplasmic AC-19,20,21) without anti-DFS70 were determined in 3 (42.9%) HI, in 7 (43.8%) pts with undifferentiated SARDs and in 81 (100%) pts with SLE (Table 1). Therefore, monospecific anti-DFS70 were detected in the HI and undifferentiated SARDs groups, but were not detected in pts with a reliable diagnosis of SLE. Table 1. Detection rate of patterns in ANA HEp-2-positive pts Conclusions The presence of monospecific DFS70 pattern in IFA without concomitant SARD-associated antibodies could be an exclusion biomarker for SARDs and a sign of benign autoimmunity.
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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 | 0.002 | 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) | 0.005 | 0.001 |
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".