Uncovering the Underlying Causes of Severe Acute Hepatitis of Unknown Aetiology in Children: A Comprehensive Review
Bibliographic record
Abstract
Background Since October 2021, multiple paediatric cases of severe acute hepatitis of unknown aetiology (SAHUA) not caused by hepatitis A–E viruses have been reported by multiple countries. As of 14 September 2022, approximately 1296 probable cases of SAHUA in 37 countries and regions had been reported. Objectives The purpose of this study was to present a complete picture of this outbreak, including its origin, current cases, clinical signs, possible hypotheses, and potential treatments. Methods A thorough search for literature from October 2021 to September 2023 was performed in the PubMed and Medline databases. Additional websites, including the WHO, CDC, ECDC, and the UKHSA, were searched for further relevant data. Results Common clinical symptoms include jaundice, vomiting, pale stools, diarrhoea, abdominal pain, and nausea, whereas fever is infrequent. Elevated AST and ALT are prevalent, and most cases test positive for adenovirus. However, immunohistochemical staining on liver tissue often yields negative results for adenovirus, thus challenging the hypothesis that adenovirus is a definitive cause. A recent compelling hypothesis has implicated AAV-2 as a likely etiologic agent of SAHUA in paediatric cases involving abnormal AAV-2 replication products and immune-mediated hepatic disease. Evidence of low immunogenicity, tissue tropism, and immune responses supports this hypothesis. SARS-CoV-2's role has also been explored. Some SAHUA cases have SARS-CoV-2 IgG positivity even when PCR tests are negative, thereby suggesting silent prior infections. Cidofovir, a suggested treatment for severe human adenovirus infection in immunocompromised patients, has not decreased adenoviral load in two cases. Notably, 29 deaths have been reported, and 55 cases have required or received liver transplant. Conclusion SAHUA in children presents a complex challenge with potential involvement of AAV-2 and immune-mediated factors. SARS-CoV-2 may affect disease severity–a possibility warranting further investigation. Treatment options include diagnostics, supportive care, antivirals, and immunosuppression. Prevention relies on infection control measures, and management requires advanced diagnostics and international collaboration. SAHUA remains an enigma, thus underscoring the need for continued research and adaptability to emerging infectious threats.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".