Evidence map›Paper›PMID 29623561›Full record

ReviewFrontiers of medicine2018

NKT cells in liver diseases.

Shasha Zhu, Huimin Zhang, Li Bai

Abstract readReview
PubMed Publisher
In one paragraph

Review in Frontiers of medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

24 citing papers in PubMed, 43 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

3 authors at 2 institutions in 1 country.

Shasha ZhuCAS Key Laboratory of Innate Immunity and Chronic Disease, CAS Center for Excellence in Molecular Cell Science, School of Life Sciences and Medical Center, University of Science and Technology of China, Hefei, 230027, China.
Huimin ZhangCAS Key Laboratory of Innate Immunity and Chronic Disease, CAS Center for Excellence in Molecular Cell Science, School of Life Sciences and Medical Center, University of Science and Technology of China, Hefei, 230027, China.
Li BaiCAS Key Laboratory of Innate Immunity and Chronic Disease, CAS Center for Excellence in Molecular Cell Science, School of Life Sciences and Medical Center, University of Science and Technology of China, Hefei, 230027, China. baili@ustc.edu.cn.
Center for Excellence in Molecular Cell Science · CNUniversity of Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Natural killer T cells are innate-like and tissue-resident lymphocytes, which recognize lipid antigens and are enriched in the liver. Natural killer T cells play important roles in infections, tumors, autoimmune diseases, and metabolic diseases. In this study, we summarize recent findings on biology of natural killer T cells and their roles in hepatitis B virus and hepatitis C virus infection, autoimmune liver diseases, alcoholic liver disease, nonalcoholic fatty liver disease, and hepatocellular carcinoma. Controversial results from previous studies are discussed, and indicate the dynamic alteration in the role of natural killer T cells during the progression of liver diseases, which might be caused by changes in natural killer T subsets, factors skewing cytokine responses, and intercellular crosstalk between natural killer T cells and CD1d-expressing cells or bystander cells.

Indexed as

AnimalsAutoimmune DiseasesHumansLiverLiver DiseasesNatural Killer T-Cellsalcoholic liver diseaseautoimmune liver diseaseshepatitis B virus and hepatitis C virus infectionhepatocellular carcinomanatural killer T cellsnonalcoholic fatty liver disease

Identifiers

PMID29623561
OpenAlexW2795429174

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.