Evidence map›Paper›PMID 39136896›Full record

ReviewCell biology and toxicology2024

Mesenchymal stem cell-based therapy for paraquat-induced lung injury.

Xiaping Zhang, Ting Li, Yuan-Qiang Lu

Abstract readReview
In one paragraph

Review in Cell biology and toxicology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Xiaping Zhang *Department of Emergency Medicine, School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310003, People's Republic of China.
Ting Li *Department of Emergency Medicine, School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310003, People's Republic of China.
Yuan-Qiang LuDepartment of Emergency Medicine, School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310003, People's Republic of China. luyuanqiang@zju.edu.cn.

Funding

National Key Research and Development Program of China 2023YFC3603100, 2023YFC3603105
6 · The paper itself

Abstract

Paraquat poisoning results in significant pulmonary damage, but current treatments are only minimally effective in repairing the injured lung tissues. Recent research has highlighted the promise of using stem cell therapy, namely mesenchymal stem cells, as a new method for treating paraquat toxicity. These cells have shown effectiveness in decreasing inflammation, apoptosis, and fibrosis in the mice lungs subjected to paraquat. The therapeutic implications of mesenchymal stem cells are believed to arise from their release of bioactive proteins and their capacity to regulate inflammatory responses. However, additional clinical study is required to validate these therapies' efficacy. This review thoroughly explores the pathophysiology of paraquat poisoning and the properties of mesenchymal stem cells. Additionally, it critically assesses the long-term safety and effectiveness of mesenchymal stem cell therapies, which is crucial for developing more dependable and effective treatment protocols. In summary, although mesenchymal stem cells offer promising prospects for treating lung injuries, more investigations are required to optimize their therapeutic promise and ensure their safe clinical application in the context of paraquat poisoning.

Indexed as

Lung InjuryMesenchymal Stem CellsMesenchymal Stem Cell TransplantationParaquatAnimalsHumansLungMiceParaquatInflammationLung injuryMSCsOxidative stressParaquat poisoning

Identifiers

PMID39136896
PMCPMC11322247

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.