Anti‐kelch‐like 12 and anti‐hexokinase 1: novel autoantibodies in primary biliary cirrhosis
Bibliographic record
Abstract
BACKGROUND & AIMS: Using high-density human recombinant protein microarrays, we identified two potential biomarkers, kelch-like 12 (KLHL12) and hexokinase-1 (HK1), in primary biliary cirrhosis (PBC). The objective of this study was to determine the diagnostic value of anti-KLHL12/HK1 autoantibodies in PBC. Initial discovery used sera from 22 patients with PBC and 62 non-PBC controls. KLHL12 and HK1 proteins were then analysed for immunoglobulin reactivity by immunoblot and enzyme-linked immunosorbent assay (ELISA) in two independent cohorts of PBC and disease/healthy control patients. METHODS: Serum samples from 100 patients with PBC and 165 non-PBC disease controls were analysed by immunoblot and samples from 366 patients with PBC, 174 disease controls, and 80 healthy donors were tested by ELISA. RESULTS: Anti-KLHL12 and anti-HK1 antibodies were each detected more frequently in PBC compared with non-PBC disease controls (P < 0.001). Not only are both markers highly specific for PBC (≥95%) but they also yielded higher sensitivity than anti-gp210 and anti-sp100 antibodies. Combining anti-HK1 and anti-KLHL12 with available markers (MIT3, gp210 and sp100), increased the diagnostic sensitivity for PBC. Most importantly, anti-KLHL12 and anti-HK1 antibodies were present in 10-35% of anti-mitochondrial antibody (AMA)-negative PBC patients and adding these two biomarkers to conventional PBC assays dramatically improved the serological sensitivity in AMA-negative PBC from 55% to 75% in immunoblot and 48.3% to 68.5% in ELISA. CONCLUSIONS: The addition of tests for highly specific anti-KLHL12 and anti-HK1 antibodies to AMA and ANA serological assays significantly improves efficacy in the clinical detection and diagnosis of PBC, especially for AMA-negative subjects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".