Evidence map›Paper›PMID 36503204›Full record

ReviewClinical and molecular hepatology2023

Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future.

Terry Cheuk-Fung Yip, Fei Lyu, Huapeng Lin, Guanlin Li, Pong-Chi Yuen, Vincent Wai-Sun Wong, Grace Lai-Hung Wong

Open access · goldAbstract readReview
In one paragraph

Review in Clinical and molecular hepatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 4 pooled it
5.5field-weighted citation impact, top 3% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 4 syntheses or guidelines pooled it, 39 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Terry Cheuk-Fung YipMedical Data Analytic Centre, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China.
Fei LyuDepartment of Computer Science, Hong Kong Baptist University, Hong Kong, China.
Huapeng LinMedical Data Analytic Centre, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China.
Guanlin LiMedical Data Analytic Centre, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China.
Pong-Chi YuenDepartment of Computer Science, Hong Kong Baptist University, Hong Kong, China.
Vincent Wai-Sun WongMedical Data Analytic Centre, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China.
Grace Lai-Hung WongMedical Data Analytic Centre, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China.
Chinese University of Hong Kong · HKHong Kong Baptist University · HK

Funding

Health and Medical Research Fund 07180216
6 · The paper itself

Abstract

Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the standard tool, yet it is well-known for substantial sampling, intraobserver, and interobserver variability. Serum proinflammatory cytokines and apoptotic markers, namely cytokeratin-18, are well-studied with reasonable accuracy, whereas serum metabolomics and lipidomics have been adopted in some commercially available diagnostic models. Ultrasound and computed tomography imaging techniques are attractive due to their wide availability; yet their accuracies may not be comparable with magnetic resonance imaging-based tools. Machine learning and deep learning models, be they supervised or unsupervised learning, are promising tools to identify various subtypes of NAFLD, including those with dominating liver inflammation, contributing to sustainable care pathways for NAFLD.

Indexed as

HepatitisNon-alcoholic Fatty Liver DiseaseBiomarkersHumansInflammationLiverLiver CirrhosisBiomarkersCytokeratin-18Deep learningFatty liverLiver cancerMachine learning

Identifiers

PMID36503204
PMCPMC10029958
OpenAlexW4311199997

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.