ReviewVirology journal2024
Liver injury in COVID-19: an insight into pathobiology and roles of risk factors.
Review in Virology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- Clinical, Laboratory, and Imaging Characteristics of Liver Involvement in Hospitalized Children With COVID-19 in Iran During 2020-2021: A Cross-Sectional Study.Health science reports · 2026Article
- Post-acute organ complications within one year following COVID-19 hospitalization and related socioeconomic inequalities.Nature communications · 2026Article
- Benign course of hepatitis A and COVID-19 coinfection: A retrospective observational case series with comparative analysis.IDCases · 2026Article
- COVID-19 Infection, Drugs, and Liver Injury.Journal of clinical medicine · 2025Review
- Entropy-Based CT Radiomics as an Imaging Marker of Hepatic Injury in COVID-19.Diagnostics (Basel, Switzerland) · 2025Article
- Hepatic dysfunction in individuals with COVID-19 and its impact on pregnancy outcomes.Medicine · 2025Article
- Review
- Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients-Machine learning models for clinical decision support.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is a complex disease that can lead to fatal respiratory failure with extrapulmonary complications, either as a direct result of viral invasion in multiple organs or secondary to oxygen supply shortage. Liver is susceptible to many viral pathogens, and due to its versatile functions in the body, it is of great interest to determine how hepatocytes may interact with SARS-CoV-2 in COVID-19 patients. Liver injury is a major cause of death, and SARS-CoV-2 is suspected to contribute significantly to hepatopathy. Owing to the lack of knowledge in this field, further research is required to address these ambiguities. Therefore, we aimed to provide a comprehensive insight into host-virus interactions, underlying mechanisms, and associated risk factors by collecting results from epidemiological analyses and relevant laboratory experiments. Backed by an avalanche of recent studies, our findings support that liver injury is a sequela of severe COVID-19, and certain pre-existing liver conditions can also intensify the morbidity of SARS-CoV-2 infection in synergy. Notably, age, sex, lifestyle, dietary habits, coinfection, and particular drug regimens play a decisive role in the final outcome and prognosis as well. Taken together, our goal was to unravel these complexities concerning the development of novel diagnostic, prophylactic, and therapeutic approaches with a focus on prioritizing high-risk groups.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.