Evidence map›Paper›PMID 35263512›Full record

ReviewAnimal models and experimental medicine2022

Chinese medicine in the treatment of autoimmune hepatitis: Progress and future opportunities.

Jia Liu, Zhi Ma, Han Li, Xiaojiaoyang Li

Open access · diamondAbstract readReview
In one paragraph

Review in Animal models and experimental medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

7 citing papers in PubMed, 11 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Jia LiuSchool of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
Zhi MaSchool of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
Han LiSchool of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
Xiaojiaoyang LiSchool of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.ORCID 0000-0002-8997-9651
Beijing University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autoimmune hepatitis (AIH) is a chronic inflammatory liver disease occurring in individuals of all ages with a higher incidence in females and characterized by hypergammaglobulinemia, elevated serum autoantibodies and histological features of interface hepatitis. AIH pathogenesis remains obscure and still needs in-depth study, which is likely associated with genetic susceptibility and the loss of immune homeostasis. Steroids alone and in combination with other immunosuppressant agents are the primary choices of AIH treatment in the clinic, whereas, in some cases, severe adverse effects and disease relapse may occur. Chinese medicine used for the treatment of AIH has proven its merits over many years and is well tolerated. To better understand the pathogenesis of AIH and to evaluate the efficacy of novel therapies, several animal models have been generated to recapitulate the immune microenvironment of patients with AIH. In the current review, we summarize recent advances in the study of animal models for AIH and their application in pharmacological research of Chinese medicine-based therapies and also discuss current limitations. This review aims to provide novel insights into the discovery of Chinese medicine-originated therapies for AIH using cutting-edge animal models.

Indexed as

Hepatitis, AutoimmuneAnimalsFemaleGenetic Predisposition to DiseaseHumansImmunosuppressive AgentsMedicine, Chinese TraditionalImmunosuppressive Agentsanimal modelsautoimmune hepatitisimmune regulationTraditional Chinese medicine

Identifiers

PMID35263512
PMCPMC9043711
OpenAlexW4205637970

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.