Advances in quantitative analysis of astrocytes using machine learning

Labate, Demetrio; Kayasandık, Cihan Bilge

Advances in quantitative analysis of astrocytes using machine learning

dc.contributor.author	Labate, Demetrio
dc.contributor.author	Kayasandık, Cihan Bilge
dc.date.accessioned	2022-08-19T08:14:43Z
dc.date.available	2022-08-19T08:14:43Z
dc.date.issued	2023
dc.department	İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstract	Astrocytes, a subtype of glial cells, are starshaped cells that are involved in the homeostasis and blood flow control of the central nervous system (CNS). They are known to provide structural and functional support to neurons, including the regulation of neuronal activation through extracellular ion concentrations, the regulation of energy dynamics in the brain through the transfer of lactate to neurons, and the modulation of synaptic transmission via the release of neurotransmitters such as glutamate and adenosine triphosphate. In addition, astrocytes play a critical role in neuronal reconstruction after brain injury, including neurogenesis, synaptogenesis, angiogenesis, repair of the blood-brain barrier, and glial scar formation after traumatic brain injury (Zhou et al., 2020). The multifunctional role of astrocytes in the CNS with tasks requiring close contact with their targets is reflected by their morphological complexity, with processes and ramifications occurring over multiple scales where interactions are plastic and can change depending on the physiological conditions. Another major feature of astrocytes is reactive astrogliosis, a process occurring in response to traumatic brain injury, neurological diseases, or infection which involves substantial morphological alterations and is often accompanied by molecular, cytoskeletal, and functional changes that ultimately play a key role in the disease outcome (Schiweck et al., 2018). Because morphological changes in astrocytes correlate so significantly with brain injury and the development of pathologies of the CNS, there is a major interest in methods to reliably detect and accurately quantify such morphological alterations. We review below the recent progress in the quantitative analysis of images of astrocytes. We remark that, while our discussion is focused on astrocytes, the same methods discussed below can be applied to other types of complex glial cells.
dc.description.sponsorship	National Science Foundation grants Division of Mathmatical Science	en_US
dc.identifier.citation	Labate, D. ve Kayasandık, C. B. (2023). Advances in quantitative analysis of astrocytes using machine learning. Neural Regeneration Research, 18(2), 313-314. http://doi.org/10.4103/1673-5374.346474
dc.identifier.doi	10.4103/1673-5374.346474
dc.identifier.endpage	314
dc.identifier.issn	1673-5374
dc.identifier.issn	1876-7958
dc.identifier.issue	2
dc.identifier.pmid	35900411
dc.identifier.scopus	2-s2.0-85135450895
dc.identifier.scopusquality	N/A
dc.identifier.startpage	313
dc.identifier.uri	http://doi.org/10.4103/1673-5374.346474
dc.identifier.uri	https://hdl.handle.net/20.500.12511/9660
dc.identifier.volume	18
dc.identifier.wos	000834672700016	en_US
dc.identifier.wosquality	Q1
dc.indekslendigikaynak	Web of Science
dc.indekslendigikaynak	Scopus
dc.indekslendigikaynak	PubMed
dc.institutionauthor	Kayasandık, Cihan Bilge
dc.language.iso	en
dc.publisher	Wolters Kluwer Medknow Publications
dc.relation.ispartof	Neural Regeneration Research	en_US
dc.relation.publicationcategory	Diğer
dc.rights	Attribution-NonCommercial-ShareAlike 4.0 International	*
dc.rights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by-nc-sa/4.0/	*
dc.subject	Quantitative Analysis
dc.subject	Astrocytes
dc.subject	Machine Learning
dc.title	Advances in quantitative analysis of astrocytes using machine learning
dc.type	Editorial