Evidence map›Paper›PMID 28469141›Full record

ArticleScientific reports2017

Serum MicroRNAs Reflect Injury Severity in a Large Animal Model of Thoracic Spinal Cord Injury.

Seth Tigchelaar, Femke Streijger, Sunita Sinha, Stephane Flibotte, Neda Manouchehri, Kitty So, Katelyn Shortt, Elena Okon, Michael A Rizzuto, Ivana Malenica and 5 more

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. MicroRNAs in spinal cord injury: A narrative review.Frontiers in molecular neuroscience · 2023
    Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. Diagnostic blood RNA profiles for human acute spinal cord injury.The Journal of experimental medicine · 2021
    Article
  18. Article
  19. Article
  20. 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

15 authors.

Seth TigchelaarInternational Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada.
Femke StreijgerInternational Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada.
Sunita SinhaFaculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
Stephane FlibotteFaculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
Neda ManouchehriInternational Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada.
Kitty SoInternational Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada.
Katelyn ShorttInternational Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada.
Elena OkonInternational Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada.
Michael A RizzutoInternational Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada.ORCID http://orcid.org/0000-0001-7743-4454
Ivana MalenicaTranslational Genomics, Phoenix, Arizona, United States of America.
Amanda Courtright-LimTranslational Genomics, Phoenix, Arizona, United States of America.
Andrew EisenAcorda Therapeutics, Ardsley, New York, United States of America.
Kendall Van Keuren-JensenTranslational Genomics, Phoenix, Arizona, United States of America.
Corey NislowFaculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada.ORCID http://orcid.org/0000-0002-4016-8874
Brian K KwonInternational Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada. brian.kwon@ubc.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Therapeutic development for spinal cord injury is hindered by the difficulty in conducting clinical trials, which to date have relied solely on functional outcome measures for patient enrollment, stratification, and evaluation. Biological biomarkers that accurately classify injury severity and predict neurologic outcome would represent a paradigm shift in the way spinal cord injury clinical trials could be conducted. MicroRNAs have emerged as attractive biomarker candidates due to their stability in biological fluids, their phylogenetic similarities, and their tissue specificity. Here we characterized a porcine model of spinal cord injury using a combined behavioural, histological, and molecular approach. We performed next-generation sequencing on microRNAs in serum samples collected before injury and then at 1, 3, and 5 days post injury. We identified 58, 21, 9, and 7 altered miRNA after severe, moderate, and mild spinal cord injury, and SHAM surgery, respectively. These data were combined with behavioural and histological analysis. Overall miRNA expression at 1 and 3 days post injury strongly correlates with outcome measures at 12 weeks post injury. The data presented here indicate that serum miRNAs are promising candidates as biomarkers for the evaluation of injury severity for spinal cord injury or other forms of traumatic, acute, neurologic injury.

Indexed as

AnimalsBiomarkersDisease Models, AnimalFemaleMicroRNAsROC CurveSeverity of Illness IndexSpinal CordSpinal Cord InjuriesSwineBiomarkersMicroRNAs

Identifiers

PMID28469141
PMCPMC5431108

What OpenQuestion 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 OpenQuestion graph.