Anti-U11/U12 Antibodies as a Rare but Important Biomarker in Patients with Systemic Sclerosis: A Narrative Review
Bibliographic record
Abstract
Anti-nuclear (ANA) are present in approximately 90% of systemic sclerosis (SSc) patients and are key biomarkers in supporting the diagnosis and determining the prognosis of this disease. In addition to the classification criteria autoantibodies for SSc [i.e., anti-centromere, anti-topoisomerase I (Scl-70), anti-RNA polymerase III], other autoantibodies have been associated with important SSc phenotypes. Among them, anti-U11/U12 ribonucleoprotein (RNP) antibodies, also known as anti-RNPC-3, were first reported in a patient with SSc, but very little is known about their association and clinical utility. The U11/U12 RNP macromolecular complex consists of several proteins involved in alternative mRNA splicing. More recent studies demonstrated associations of anti-anti-U11/U12 antibodies with SSc and severe pulmonary fibrosis as well as with moderate to severe gastrointestinal dysmotility. Lastly, anti-U11/U12 autoantibodies have been strongly associated with malignancy in SSc patients. Here, we aimed to summarize the knowledge of anti-U11/U12/RNPC-3 antibodies in SSc, including their seroclinical associations in a narrative literature review.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".