Anti‐α actinin antibodies as new predictors of response to treatment in autoimmune hepatitis type 1
Bibliographic record
Abstract
BACKGROUND: We reported that combined presence of autoantibodies (Abs) against filamentous-actin (AFA) and α-actinin are specific for autoimmune hepatitis type 1 (AIH-1) diagnosis. AIM: To explore our data and assess whether anti-α-actinin and AFA Abs could be used as indicators of response to treatment and predictors of AIH-1 flares in a large cohort of AIH-1 patients. METHODS: Seven hundred and sixty-four serial serum samples of 86 consecutive AIH-1 patients, 509 pathological and 110 normal controls were tested for the presence of anti-α-actinin and AFA Abs by an in-house IgG-specific ELISA and a standardised commercially available ELISA respectively. Patients sera were divided into baseline group (active disease before treatment initiation, n = 86) and then according to treatment response into group A-responders (n = 40 patients), group B-relapsers/incomplete responders (n = 37 patients) and group C-not-treated (n = 9 patients). RESULTS: Anti-α-actinin and AFA levels were significantly higher at baseline. Double reactivity against α-actinin and AFA was associated with disease activity (OR 4.9; 95% CI: 2.7-9). Anti-α-actinin optical densities (ODs) before treatment decreased significantly at first remission (P < 0.05). Treatment response was associated with anti-α-actinin Abs negativity before treatment (OR 3.4; 95% CI: 1.3-8.9) and absence of double positivity for anti-α-actinin and AFA Abs before treatment (OR 3.8; 95% CI: 1.4-10.4). Responders had lower baseline levels of anti-α-actinin than relapsers and/or incomplete responders (P = 0.002). Binary logistic regression revealed lower levels of anti-α-actinin as the only independent predictors of response (P = 0.05). CONCLUSIONS: Anti-α-actinin Abs at baseline appear to predict treatment response and therefore they might be used for monitoring treatment outcome in AIH-1.
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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) | 0.000 | 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) | 0.002 | 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".