Anti‐kelch‐like 12 and anti‐hexokinase 1: novel autoantibodies in primary biliary cirrhosis
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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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it