A2 Katsausartikkeli tieteellisessä aikauslehdessä

"Omics" in traumatic brain injury: novel approaches to a complex disease

Julkaisun tekijät: Abu Hamdeh Sami, Tenovuo Olli, Peul Wilco, Marklund Niklas


Julkaisuvuosi: 2021

Journal: Acta Neurochirurgica

Tietokannassa oleva lehden nimi: ACTA NEUROCHIRURGICA

Lehden akronyymi: ACTA NEUROCHIR

Volyymi: 163

Julkaisunumero: 9

Sivujen määrä: 14

ISSN: 0001-6268

DOI: http://dx.doi.org/10.1007/s00701-021-04928-7


Background: To date, there is neither any pharmacological treatment with efficacy in traumatic brain injury (TBI) nor any method to halt the disease progress. This is due to an incomplete understanding of the vast complexity of the biological cascades and failure to appreciate the diversity of secondary injury mechanisms in TBI. In recent years, techniques for high-throughput characterization and quantification of biological molecules that include genomics, proteomics, and metabolomics have evolved and referred to as omics.

Methods: In this narrative review, we highlight how omics technology can be applied to potentiate diagnostics and prognostication as well as to advance our understanding of injury mechanisms in TBI.

Results: The omics platforms provide possibilities to study function, dynamics, and alterations of molecular pathways of normal and TBI disease states. Through advanced bioinformatics, large datasets of molecular information from small biological samples can be analyzed in detail and provide valuable knowledge of pathophysiological mechanisms, to include in prognostic modeling when connected to clinically relevant data. In such a complex disease as TBI, omics enables broad categories of studies from gene compositions associated with susceptibility to secondary injury or poor outcome, to potential alterations in metabolites following TBI.

Conclusion: The field of omics in TBI research is rapidly evolving. The recent data and novel methods reviewed herein may form the basis for improved precision medicine approaches, development of pharmacological approaches, and individualization of therapeutic efforts by implementing mathematical "big data" predictive modeling in the near future.

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Last updated on 2021-22-09 at 15:32