Evidence map›Paper›PMID 30809283›Full record

ArticleTheranostics2019

Delayed Remote Ischemic Preconditioning ConfersRenoprotection against Septic Acute Kidney Injury via Exosomal miR-21.

Tianyi Pan, Ping Jia, Nan Chen, Yi Fang, Yiran Liang, Man Guo, Xiaoqiang Ding

Open access · goldAbstract read
In one paragraph

Article in Theranostics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
125citing papers in PubMed, 4 pooled it
10.0field-weighted citation impact, top 1% 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

125 citing papers in PubMed, 4 syntheses or guidelines pooled it, 188 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Trial
  7. Protective Effects and Mechanisms of Extracellular Vesicles in Remote Ischemic Preconditioning on Renal Injury Induced by Inflammation.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    Article
  8. Article
  9. Review
  10. Review
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Review
  20. Article

65 more citing papers are in PubMed but not listed here.

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

7 authors at 2 institutions in 1 country.

Tianyi PanInstitutes of Biomedical Sciences, Fudan University.
Ping JiaDepartment of Nephrology, Zhongshan Hospital, Fudan University.
Nan ChenDepartment of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai.
Yi FangDepartment of Nephrology, Zhongshan Hospital, Fudan University.
Yiran LiangDepartment of Nephrology, Zhongshan Hospital, Fudan University.
Man GuoDepartment of Nephrology, Zhongshan Hospital, Fudan University.
Xiaoqiang DingDepartment of Nephrology, Zhongshan Hospital, Fudan University.
Zhongshan Hospital · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is a common and life-threatening systemic disorder, often leading to acute injury of multiple organs. Here, we show that remote ischemic preconditioning (rIPC), elicited by brief episodes of ischemia and reperfusion in femoral arteries, provides protective effects against sepsis-induced acute kidney injury (AKI).

Indexed as

Acute Kidney InjuryAnimalsCell LineDisease Models, AnimalEpithelial CellsExosomesIschemiaIschemic PreconditioningKidneyLower ExtremityMiceMice, KnockoutMicroRNAsModels, TheoreticalSepsisMicroRNAsMIRN21 microRNA, mouseacute kidney injuryexosomesmiR-21remote ischemic preconditioningsepsis

Identifiers

PMID30809283
PMCPMC6376188
OpenAlexW2909779866

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.