Serological markers of autoimmunity in children with hepatitis A
Bibliographic record
Abstract
OBJECTIVES: Hepatitis A virus (HAV) infection tends to be a self-limiting disease without serious sequelae, but fulminant hepatitis, with a high mortality, develops in 0.1-0.2% of the cases. Sometimes, HAV infection precipitates autoimmune hepatitis (AIH). We aimed to assess the frequency and clinical significance of serologic markers of autoimmunity during hepatitis A infection with an acute or fulminant presentation compared with those in AIH. METHODS: The study included 126 children: 46 with HAV infection (33 with acute and 13 with fulminant presentation), 53 with AIH, and 27 healthy controls. In all, we measured autoantibodies titer (antinuclear antibody, antismooth muscle antibody, and liver kidney microsomal antibody-1) and serum gammaglobulins. RESULTS: Autoantibodies were detected in the majority of HAV (63.1%) and AIH (79.2%) groups, but in none of the controls. Gammaglobulins were significantly higher in the HAV group (1.93±0.57 g/dl) than in the controls (1.32±0.29 g/dl), but lower than that in the AIH group (2.93±1.2 g/dl) (P<0.0001 for all). In the HAV group, gammaglobulins were significantly higher in those with fulminant (2.21±0.46 g/dl) than in those with acute presentation (1.82±0.57 g/dl) (P=0.019), but comparable with that in AIH (P=0.095). Gammaglobulins correlated significantly with disease severity in both HAV and AIH groups. CONCLUSION: Hypergammaglobulinemia and a high occurrence of autoantibodies are encountered in HAV infection. This may support the immunological basis of its pathogenesis. Moreover, the higher gammaglobulins in fulminant HAV, with an insignificant difference from that in AIH, suggest that a more aggressive immunological reaction is related to this presentation.
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 | 0.002 |
| 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 | 0.000 |
| 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 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".