Evidence map›Paper›PMID 31843011›Full record

ReviewStem cell research & therapy2019

Current understanding of the administration of mesenchymal stem cells in acute kidney injury to chronic kidney disease transition: a review with a focus on preclinical models.

Lingfei Zhao, Fei Han, Junni Wang, Jianghua Chen

Abstract readReview
In one paragraph

Review in Stem cell research & therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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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.

Lingfei ZhaoKidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People's Republic of China.
Fei HanKidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People's Republic of China.
Junni WangKidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People's Republic of China.
Jianghua ChenKidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People's Republic of China. zjukidney@zju.edu.cn.ORCID 0000-0001-6061-5697

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Incomplete recovery from acute kidney injury (AKI) can result in long-term functional deficits and has been recognized as a major contributor to chronic kidney disease (CKD), which is termed the AKI-CKD transition. Currently, an effective intervention for this disorder is still lacking. Principally, therapeutic strategies targeting the AKI-CKD transition can be divided into those reducing the severity of AKI or promoting the regenerative process towards beneficially adaptive repair pathways. Considering the fact that mesenchymal stem cells (MSCs) have the potential to address both aspects, therapeutic regimens based on MSCs have a promising future. In light of this information, we focus on the currently available evidence associated with MSC therapy involved in the treatment of the AKI-CKD transition and the underlying mechanisms. All of these discussions will contribute to the establishment of a reliable therapeutic strategy for patients with this problem, who can be easily ignored by physicians, and will lead to a better clinical outcome for them.

Indexed as

Acute Kidney InjuryHumansMesenchymal Stem CellsMesenchymal Stem Cell TransplantationRandomized Controlled Trials as TopicRenal Insufficiency, ChronicAKI-CKD transitionMesenchymal stem cells

Identifiers

PMID31843011
PMCPMC6916462

What OpenQuestion holds

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Registered trials

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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.