ArticleZhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology2023
[Application of omics in the diagnosis of metabolic dysfunction-associated fatty liver disease].
Article in Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 2 citations in OpenAlex.
- [Research progress and future prospects for artificial intelligence in the diagnosis and treatment of fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Genetic predeterminants and recent advancements in steatotic liver disease: A roadmap toward precision hepatology.World journal of hepatology · 2025Review
- [Metabolic associated fatty liver disease induced cirrhosis: epidemiology, risk factors, and new strategies for precise prevention and control].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Illuminating diabetesWorld journal of diabetes · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most common chronic liver disease worldwide, and the risk of all-cause and liver-related mortality significantly increases with the degree of fibrosis. Early diagnosis of MAFLD and its degree of liver fibrosis are of great significance, so it is particularly important to find an accurate and simple, non-invasive diagnostic method. In recent years, high-throughput omics technology has developed rapidly and played an important role in the non-invasive diagnosis and prediction of fibrosis degree in MAFLD. This article summarizes the application progress of genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, radiomics, and the combination of multi-omics for the diagnosis of MAFLD disease.
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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.