Evidence map›Paper›PMID 37137838›Full record

ArticleZhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology2023

[Research progress of single-cell sequencing technology in liver diseases].

S S Chen, F Ren, M Liu

Open access · greenAbstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

S S ChenOncology Department of Beijing Youan Hospital Affiliated to Capital Medical University, Beijing Institute of Hepatology, Beijing 100069, China.
F RenOncology Department of Beijing Youan Hospital Affiliated to Capital Medical University, Beijing Institute of Hepatology, Beijing 100069, China.
M LiuOncology Department of Beijing Youan Hospital Affiliated to Capital Medical University, Beijing Institute of Hepatology, Beijing 100069, China.
Beijing YouAn Hospital · CN

Funding

Key Projects of the Beijing Municipal Education Commission's Science and Technology Plan KZ202010025035National Natural Science Foundation of China 81770611Special Key Research Project of Capital Health Development Scientific Research SF2020-1-1151Talent Cultivation Plan of "Climbing the Peak" of Beijing Municipal Hospital Administration DFL20221503the Demonstrating Application and Research of Clinical Diagnosis and Treatment Technology in Beijing Z191100006619096 and Z191100006619097
6 · The paper itself

Abstract

Liver disease is one of the most burdensome diseases in the world. Therefore, new technologies are needed to study its pathogenesis in depth; however, because of its complex pathogenesis, there are relatively limited treatment options. Single-cell sequencing (SCS), as an emerging sequencing method, reflects the heterogeneity between cells by sequencing the genome, transcriptome, and epigenome of a single cell, thereby revealing the complex mechanisms of disease occurrence and development. The application of SCS in the study of liver diseases will enrich our understanding of the pathogenesis of liver diseases and provide a new direction for exploring the diagnosis and treatment. This article mainly reviews the research progress of SCS technology in liver diseases.

Indexed as

Liver DiseasesSingle-Cell AnalysisHumansTranscriptomeHeterogeneityLiver diseasePathogenesisSingle-cell sequencing

Identifiers

PMID37137838
PMCPMC12814408
OpenAlexW4368361591

What OpenQuestion holds

Textmetadata
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.