Anti‐phosphoenolpyruvate carboxykinase 2 antibody in patients with autoimmune hepatitis
Bibliographic record
Abstract
AIM: To identify a novel autoantibody specific to autoimmune hepatitis (AIH) and to evaluate its clinical significance. METHODS: Non-nuclear component protein extracted from normal human liver cell CyrohNHpes cultures that reacted with sera from AIH patients on a western blot was identified as an antigenic protein and subjected to N-terminal amino acid analysis to identify phosphoenolpyruvate carboxykinase 2 (PCK2). Enzyme-linked immunoassay (ELISA) for anti-PCK2 antibody was conducted on sera samples from patients with AIH (n = 42), primary biliary cirrhosis (PBC; n = 48), non-alcoholic steatohepatitis (NASH, n = 41), chronic hepatitis C (CHC, n = 20), drug-induced liver injury (DILI, n = 10), systemic lupus erythematosus (SLE, n = 16) and on sera samples from healthy volunteers (n = 30). Clinical findings were compared for AIH patients testing positive and negative for anti-PCK2 antibody. RESULTS: ELISA findings showed that mean anti-PCK2 antibody titer in sera from AIH patients was significantly higher than in PBC, NASH, CHC, DILI and SLE patients, as well as in healthy volunteers. Anti-PCK2 antibody was present in 50.0% (21/42) of AIH, 14.6% (7/48) of PBC, 4.9% (2/41) of NASH, and 10.0% (2/20) of CHC patients, 0% (0/10) of DILI, 12.5% (2/16) of SLE and in 3.3% (1/30) of healthy volunteers. The sensitivity, specificity and accuracy of using the detection of anti-PCK2 antibody in diagnosing AIH were 50.0%, 91.5% and 83.1%, respectively. None of the AIH patients positive for anti-PCK2 antibody showed characteristic clinical features. CONCLUSION: Although further investigations into the clinical usefulness are necessary, anti-PCK2 may have potential as a diagnostic marker for AIH.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".