Understanding the Biological Significance of Anti-DFS70 Antibodies: Effect of Biologic Therapies on Their Occurrence in Inflammatory Arthritis
Bibliographic record
Abstract
To the Editor: The anti-dense fine speckled 70 (anti-DFS70) antibodies have recently become of interest because of their occurring in heterogeneous disorders including chronic inflammatory conditions, cancer, and systemic autoimmune rheumatic diseases (SARD), as well as in healthy individuals1,2,3. The frequency of anti-DFS70 antibodies in rheumatoid arthritis (RA) ranged from 0 to 2.6%4. There have been no studies examining the frequency of anti-DFS70 antibodies in spondyloarthritis (SpA) as a group, while only 1 study evaluated anti-DFS70 positivity in ankylosing spondylitis (AS)5. These autoantibodies could play protective or pathogenic roles, but the factors inducing their trigger are still uncertain6. In particular, the effect of biological treatments, extensively used in SARD management, on anti-DFS70 antibodies expression has not yet been investigated and thus represents an intriguing matter. Despite a vast amount of data supporting a role of anti–tumor necrosis factor-α (TNF-α) agents in the occurrence of immunogenicity7, no data were available about these drugs’ effect on the occurrence of anti-DFS70 antibodies. In addition, the induction of autoimmune phenomena such as the drug-induced lupus erythematosus … Address correspondence to Dr. V. Pafundi, Immunopathology Laboratory, San Carlo Hospital, Potito Petrone St., 85100 Potenza, Italy. E-mail: vito.pafundi{at}ospedalesancarlo.it
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".