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Multiple system atrophy (MSA) is a rare and fatal neurological disease with unknown etiology
.
Its clinical manifestations are characterized by motor and autonomic dysfunction
A major clinical challenge is how to distinguish the relationship between MSA-P and PD or MSA-C and idiopathic late-onset cerebellar ataxia
.
Although the current clinical diagnostic criteria perform well in the later stages of the disease, it is reported that the sensitivity of the first examination is only about 30%, and a clear MSA diagnosis still relies on post-mortem examinations
diagnosis
The neuropathological feature of MSA is the cytoplasmic content of glial and neurons, which contains misfolded α-synuclein (αSYN), which in particular leads to the prominent loss of neurons in the substantia nigra and cerebellar structure
.
The pathological progression of MSA is accompanied by neuroinflammation, which is manifested as increased levels of pro-inflammatory cytokines and extensive microglial hyperplasia
In this way, Aurelija Jucaite and others of Karolinska University studied the diagnostic sensitivity and specificity of TSPO imaging between MSA and PD and MSA subtypes
.
In addition to visual reading, they also applied machine learning (ML) methods to obtain the glial cell imaging features of MSA
They found that compared with PD, the regional [11 C]PBR28 binding of MSA to TSPO was significantly higher, and "hot spots" appeared in the celestial nucleus and cerebellar white matter
.
Visual reading distinguishes MSA from PD, with a specificity of 100% and a sensitivity of 83%
.
Machine learning methods increase the sensitivity to 96%
The main significance of this study lies in the discovery: The pattern of significantly increased regional glial TSPO binding in MSA patients
.
Interestingly, the data is consistent with the severe neuroinflammation of MSA
Glia Imaging Differentiates Multiple System Atrophy from Parkinson's Disease:
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