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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".