Evidence map›Paper›PMID 35510365›Full record

ArticleBioengineered2022

circSTRN3 aggravates sepsis-induced acute kidney injury by regulating miR-578/ toll like receptor 4 axis.

Qiuying Gao, Yan Zheng, Hui Wang, Limin Hou, Xingxing Hu

RetractedOpen access · goldAbstract readRetracted Publication
In one paragraph

Article in Bioengineered, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Development of Mathematical Models Using circRNA Combinations (International journal of molecular sciences · 2025
    Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Circular RNAs in organ injury: recent development.Journal of translational medicine · 2022
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Qiuying GaoDepartment of Hematology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Yan ZhengDepartment of Hematology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Hui WangDepartment of Hematology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Limin HouDepartment of Hematology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Xingxing HuDepartment of Hematology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.ORCID 0000-0002-5000-2739
Shanxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is a systemic inflammatory response caused by infection, and severe sepsis is commonly associated with the development of acute kidney injury (AKI). Accumulating evidence has revealed the implication of circular RNAs in AKI. In this study, we explored the potential engagement and the underlying mechanism of hsa_circ_010157 (circSTRN3) in sepsis-induced AKI. CircSTRN3 levels in HK2 cells and serum samples of patients were determined by RT-PCR. The protein levels of TLR4 (Toll Like Receptor 4), bax (Bcl-2-associated X protein), cleaved caspase 3 and bcl-2 (B-cell lymphoma-2) were detected by Western blotting (WB), and the levels of proinflammatory cytokines were detected by ELISA. The molecular interactions between mir-578/TLR4 and circSTRN3/miR-578 were analyzed by dual luciferase reporter assay as well as RNA pull-down experiment. Lipopolysaccharide (LPS) treated HK2 cells were used as an

Indexed as

Acute Kidney InjuryMicroRNAsSepsisApoptosisFemaleHumansLipopolysaccharidesMaleToll-Like Receptor 4LipopolysaccharidesMicroRNAsToll-Like Receptor 4acute renal injurycircSTRN3inflammatory responsesmiR-578/TLR4sepsis

Identifiers

PMID35510365
PMCPMC9275965
OpenAlexW4229007387

What OpenQuestion holds

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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.