Evidence map›Paper›PMID 26662506›Full record

ReviewTransfusion2016

Metabolomics in transfusion medicine.

Travis Nemkov, Kirk C Hansen, Larry J Dumont, Angelo D'Alessandro

Abstract readReview
In one paragraph

Review in Transfusion, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing 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

55 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Trial
  4. Trial
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Platelet Storage-Problems, Improvements, and New Perspectives.International journal of molecular sciences · 2024
    Review
  11. Review
  12. Assessment of stored red blood cells through lab-on-a-chip technologies for precision transfusion medicine.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  13. Omics Technologies in Veterinary Medicine: Literature Review and Perspectives in Transfusion Medicine.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2023
    Review
  14. Red Blood Cell Omics and Machine Learning in Transfusion Medicine: Singularity Is Near.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2023
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Deformability of Stored Red Blood Cells.Frontiers in physiology · 2021
    Review
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

4 authors.

Travis NemkovDepartment of Biochemistry and Molecular Genetics, University of Colorado Denver-Anschutz Medical Campus, Aurora, Colorado.
Kirk C HansenDepartment of Biochemistry and Molecular Genetics, University of Colorado Denver-Anschutz Medical Campus, Aurora, Colorado.
Larry J DumontDepartment of Pathology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Angelo D'AlessandroDepartment of Biochemistry and Molecular Genetics, University of Colorado Denver-Anschutz Medical Campus, Aurora, Colorado.

Funding

Trauma Reprograms CellsP50GM049222 · NIGMS · UNIVERSITY OF COLORADO DENVER · PI BANERJEE, ANIRBAN · 1993 to 2017
$26.1M
NIGMS NIH HHS P50 GM049222
6 · The paper itself

Abstract

Biochemical investigations on the regulatory mechanisms of red blood cell (RBC) and platelet (PLT) metabolism have fostered a century of advances in the field of transfusion medicine. Owing to these advances, storage of RBCs and PLT concentrates has become a lifesaving practice in clinical and military settings. There, however, remains room for improvement, especially with regard to the introduction of novel storage and/or rejuvenation solutions, alternative cell processing strategies (e.g., pathogen inactivation technologies), and quality testing (e.g., evaluation of novel containers with alternative plasticizers). Recent advancements in mass spectrometry-based metabolomics and systems biology, the bioinformatics integration of omics data, promise to speed up the design and testing of innovative storage strategies developed to improve the quality, safety, and effectiveness of blood products. Here we review the currently available metabolomics technologies and briefly describe the routine workflow for transfusion medicine-relevant studies. The goal is to provide transfusion medicine experts with adequate tools to navigate through the otherwise overwhelming amount of metabolomics data burgeoning in the field during the past few years. Descriptive metabolomics data have represented the first step omics researchers have taken into the field of transfusion medicine. However, to up the ante, clinical and omics experts will need to merge their expertise to investigate correlative and mechanistic relationships among metabolic variables and transfusion-relevant variables, such as 24-hour in vivo recovery for transfused RBCs. Integration with systems biology models will potentially allow for in silico prediction of metabolic phenotypes, thus streamlining the design and testing of alternative storage strategies and/or solutions.

Indexed as

BiochemistryBlood PlateletsErythrocytesErythrocyte TransfusionHumansMetabolomicsPlatelet TransfusionTransfusion Medicine

Identifiers

PMID26662506
PMCPMC5794012

What OpenQuestion holds

Textmetadata
LicenceTDM
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.