Novel diagnostic and clinical aspects of anti-PCNA antibodies detected by novel detection methods
Bibliographic record
Abstract
Autoantibodies targeting the proliferating cell nuclear antigen have been considered as a specific biomarker for systemic lupus erythematosus, and were historically identified by indirect immunofluorescence and then confirmed by other more specific immunoassays. Our objective was to investigate the anti-PCNA immune response in various disease conditions. Unselected sera referred to a clinical diagnostic laboratory and other sera from various diseases cohorts and controls were tested for anti-PCNA antibodies by enzyme-linked immunosorbent assay (ELISA), line immunoassay (LIA) and an addressable laser bead assay (ALBIA) using full-length human proliferating cell nuclear antigen. Two out of 2500 sequential, unselected sera (0.07%) referred to a diagnostic laboratory for autoantibody analysis showed a proliferating cell nuclear antigen-like staining pattern. Good agreement was found between ELISA, ALBIA and LIA. At cut-off values resulting in 100% specificity, 52.5% (ELISA), 42.5% (ALBIA) and 35% (LIA) of samples with a proliferating cell nuclear antigen-like indirect immunofluorescence staining pattern were positive. In the indirect immunofluorescence proliferating cell nuclear antigen immunoblot (IB)-positive group, anti-PCNA antibodies were frequently accompanied by anti-Ro52, and in the indirect immunofluorescence PCNA-negative but LIA PCNA-positive group by various other autoantibodies. The prevalence of anti-PCNA antibodies was highest in Sjögren's syndrome (5.0%). In conclusion, the proliferating cell nuclear antigen-like staining pattern was rarely found (0.07%) in sequential, unselected sera. Further, indirect immunofluorescence is not an accurate screening method to identify anti-PCNA antibodies as their presence may be masked by other autoantibodies. The specific association of anti-PCNA antibodies with systemic lupus erythematosus was not confirmed in our study.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".