Role of deleterious single nucleotide variants in the coding regions of TNFAIP3 for Japanese autoimmune hepatitis with cirrhosis
Bibliographic record
Abstract
Autoimmune hepatitis (AIH) is an autoimmune liver disease and cirrhosis is sometimes complicated with AIH at diagnosis, influencing its prognosis. TNFAIP3 gene encodes A20, an inhibitor of nuclear factor-κB pathway, and is a susceptibility gene for autoimmune diseases. We investigated deleterious variants in the coding regions of TNFAIP3 gene of Japanese AIH patients or those with cirrhosis. The deleterious variants in the coding regions of TNFAIP3 gene were analyzed by the cycle sequencing method and the frequencies of deleterious TNFAIP3 alleles of AIH or AIH with cirrhosis were compared with those of Japanese controls. The deleterious alleles in TNFAIP3 were not associated with AIH. A significant association was shown for the deleterious alleles in TNFAIP3 (P = 0.0180, odds ratio (OR) 4.28, 95% confidence interval (CI) 1.53-11.95) with AIH with cirrhosis at presentation. The serum IgM levels in AIH patients with deleterious alleles in TNFAIP3 were tended to be lower than those without (P = 0.0152, Q = 0.1216). The frequency of deleterious alleles in TNFAIP3 was higher in the AIH subset without the DRB1 risk alleles than that with (P = 0.0052, OR 5.10, 95%CI 1.55-16.74). The deleterious alleles in TNFAIP3were associated with AIH with cirrhosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".