Evidence map›Paper›PMID 35464083›Full record

ReviewFrontiers in physiology2022

Non-Coding RNAs in Sepsis-Associated Acute Kidney Injury.

Yanna Chen, Huan Jing, Simin Tang, Pei Liu, Ye Cheng, Youling Fan, Hongtao Chen, Jun Zhou

Abstract readReview
In one paragraph

Review in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. 2D TiMikrochimica acta · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

8 authors.

Yanna ChenDepartment of Anesthesiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China.
Huan JingDepartment of Anesthesiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China.
Simin TangDepartment of Anesthesiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China.
Pei LiuDepartment of Anesthesiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China.
Ye ChengDepartment of Anesthesiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China.
Youling FanDepartment of Anesthesiology, The First People's Hospital of Kashgar, Xinjiang, China.
Hongtao ChenDepartment of Anesthesiology, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.
Jun ZhouDepartment of Anesthesiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is a systemic inflammatory response caused by a severe infection that leads to multiple organ damage, including acute kidney injury (AKI). In intensive care units (ICU), the morbidity and mortality associated with sepsis-associated AKI (SA-AKI) are gradually increasing due to lack of effective and early detection, as well as proper treatment. Non-coding RNAs (ncRNAs) exert a regulatory function in gene transcription, RNA processing, post-transcriptional translation, and epigenetic regulation of gene expression. Evidence indicated that miRNAs are involved in inflammation and programmed cell death during the development of sepsis-associated AKI (SA-AKI). Moreover, lncRNAs and circRNAs appear to be an essential regulatory mechanism in SA-AKI. In this review, we summarized the molecular mechanism of ncRNAs in SA-AKI and discussed their potential in clinical diagnosis and treatment.

Indexed as

acute kidney injurycircRNAlncRNAmiRNAsepsis

Identifiers

PMID35464083
PMCPMC9024145

What Socratic 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 Socratic graph.