The significance of autoantibodies to DFS70/LEDGFp75 in health and disease: integrating basic science with clinical understanding
Bibliographic record
Abstract
Antinuclear autoantibodies (ANAs) displaying the nuclear dense fine speckled immunofluorescence (DFS-IIF) pattern in HEp-2 substrates are commonly observed in clinical laboratory referrals. They target the dense fine speckled autoantigen of 70 kD (DFS70), most commonly known as lens epithelium-derived growth factor p75 (LEDGFp75). Interesting features of these ANAs include their low frequency in patients with systemic autoimmune rheumatic diseases (SARD), elevated prevalence in apparently healthy individuals, IgG isotype, strong trend to occur as the only ANA specificity in serum, and occurrence in moderate to high titers. These autoantibodies have also been detected at varied frequencies in patients with diverse non-SARD inflammatory and malignant conditions such as atopic diseases, asthma, eye diseases, and prostate cancer. These observations have recently stimulated vigorous research on their clinical and biological significance. Some studies have suggested that they are natural, protective antibodies that could serve as biomarkers to exclude a SARD diagnosis. Other studies suggest that they might be pathogenic in certain contexts. The emerging role of DFS70/LEDGFp75 as a stress protein relevant to human acquired immunodeficiency syndrome, cancer, and inflammation also points to the possibility that these autoantibodies could be sensors of cellular stress and inflammation associated with environmental factors. In this comprehensive review, we integrate our current knowledge of the biology of DFS70/LEDGFp75 with the clinical understanding of its autoantibodies in the contexts of health and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".