Anti‐tRNP<sup>(ser)sec</sup>/SLA/LP autoantibodies. Comparative study using in‐house ELISA with a recombinant 48.8 kDa protein, immunoblot, and analysis of immunoprecipitated RNAs
Bibliographic record
Abstract
BACKGROUND: Antibodies against tRNP((ser)sec) (ribonucleoproteins, RNP) have been described in our laboratory as markers of poor outcome in type 1 autoimmune hepatitis (AIH). The antigenic protein has been sequenced and cloned as a 48.8 kDa protein and identified with soluble liver antigen (SLA) and liver-pancreas (LP) antigen. The aim of this paper was to determine the best assay by which to detect these antibodies in type 1 AIH. METHODS: A simple and reliable enzyme linked immunoassay based on prokaryotically expressed protein was compared with an immunoblot assay using prokaryotically- and eukaryotically-expressed proteins and an assay based on immunoprecipitated RNAs from HeLa cell extracts. Eighty-one sera from 58 patients with type 1 AIH, 168 sera from patients with autoimmune diseases or chronic hepatitis C, and 60 sera from healthy subjects were similarly tested. RESULTS: The specificity of the assays was 100%, but the frequency of seropositivity was higher in the assay based on immunoprecipitated RNAs (44.4%) than in the enzyme-linked immunosorbent assay (ELISA) (16%) and the immunoblot assay with prokaryotically (12.34%) and eukaryotically (14.8%)-expressed protein. There were no clinical differences between the patients positive by ELISA, immunoblot assay, or immunoprecipitated RNAs. CONCLUSIONS: These results suggest that the analysis of the immunoprecipitated RNAs is the most useful, sensitive and specific method to detect anti-tRNP((ser)sec)/SLA/LP autoantibodies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".