Science convergence applied to psychoneuroimmunology: The future of measurement and imaging.

Brain Disease Glia Laser Microscopy Nanoparticle Technology

Journal

Brain, behavior, and immunity
ISSN: 1090-2139
Titre abrégé: Brain Behav Immun
Pays: Netherlands
ID NLM: 8800478

Informations de publication

Date de publication:
08 2020
Historique:
received: 03 02 2020
revised: 06 03 2020
accepted: 10 04 2020
pubmed: 15 4 2020
medline: 28 4 2021
entrez: 15 4 2020
Statut: ppublish

Résumé

The future of psychoneuroimmunology requires breakthrough technology discoveries. These next generation technologies need to address the unique challenges that are raised by imaging and measuring the activity of the neuroimmune interface in health and disease. The complexity of this challenge is centred around the multidimensionality of the neuroimmune system. These include novel challenges of capturing potent and rare biological signals over long times and vast anatomical distances. Here is a summary of the outcomes of the investments made by the Australian Research Council Centre of Excellence for Nanoscale BioPhotonics which was presented as part of the PNIRSAsia-Pacific symposium at the 2019 International Brain Research Organization.

Identifiants

pubmed: 32289367
pii: S0889-1591(20)30133-1
doi: 10.1016/j.bbi.2020.04.029
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

262-269

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Auteurs

Mark R Hutchinson (MR)

Adelaide Medical School, Faculty of Health Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia; Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, South Australia 5005, Australia; Institute for Photonics and Advanced Sensing, University of Adelaide, Adelaide, South Australia 5005, Australia; Robinson Research Institute, University of Adelaide, Adelaide, South Australia 5005, Australia.

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Classifications MeSH