'E-nose' could someday diagnose Parkinson's disease by 'smelling' skin


Date:
February 23, 2022
Source:
American Chemical Society
Summary:
Scientists have been trying to build devices that could diagnose
Parkinson's disease (PD) through odor compounds on the skin. Now,
researchers have developed a portable, artificially intelligent
olfactory system, or 'e-nose,' that could someday diagnose the
disease in a doctor's office.



FULL STORY ========================================================================== Scientists have been trying to build devices that could diagnose
Parkinson's disease (PD) through odor compounds on the skin. Now,
researchers reporting in ACS Omegahave developed a portable, artificially intelligent olfactory system, or "e-nose," that could someday diagnose
the disease in a doctor's office.


==========================================================================
PD causes motor symptoms, such as tremors, rigidity and trouble walking,
as well as non-motor symptoms, including depression and dementia. Although there's no cure, early diagnosis and treatment can improve one's quality
of life, relieve symptoms and prolong survival. However, the disease
usually isn't identified until patients develop motor symptoms, and by
that time, they've already experienced irreversible neuron loss. Recently, scientists discovered that people with PD secrete increased sebum (an
oily, waxy substance produced by the skin's sebaceous glands), along with increased production of yeast, enzymes and hormones, which combine to
produce certain odors. Although human "super smellers" are very rare, researchers have used gas chromatography (GC)- mass spectrometry to
analyze odor compounds in the sebum of people with PD. But the instruments
are bulky, slow and expensive. Jun Liu, Xing Chen and colleagues wanted
to develop a fast, easy to use, portable and inexpensive GC system to
diagnose PD through smell, making it suitable for point-of-care testing.

The researchers developed an e-nose, combining GC with a surface acoustic
wave sensor -- which measures gaseous compounds through their interaction
with a sound wave -- and machine learning algorithms. The team collected
sebum samples from 31 PD patients and 32 healthy controls by swabbing
their upper backs with gauze. They analyzed volatile organic compounds emanating from the gauze with the e-nose, finding three odor compounds (octanal, hexyl acetate and perillic aldehyde) that were significantly different between the two groups, which they used to build a model for
PD diagnosis.

Next, the researchers analyzed sebum from an additional 12 PD patients
and 12 healthy controls, finding that the model had an accuracy of 70.8%
in predicting PD. The model was 91.7% sensitive in identifying true PD patients, but its specificity was only 50%, indicating a high rate of
false positives. When machine learning algorithms were used to analyze the entire odor profile, the accuracy of diagnosis improved to 79.2%. Before
the e-nose is ready for the clinic, the team needs to test it on many
more people to improve the accuracy of the models, and they also need
to consider factors such as race, the researchers say.

The authors acknowledge funding from the National Natural Science
Foundation of China, the National Key Research and Development Program
of China, the Zhejiang Public Welfare Technology Application Research
Project, the Key Research and Development Program of Shaanxi, the Major Scientific Project of Zhejiang Lab, the Zhejiang Provincial Natural
Science Foundation of China, the China Postdoctoral Science Foundation
and the Major Consulting Project of the Chinese Academy of Engineering.

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Content may be edited for style and length.


========================================================================== Journal Reference:
1. Wei Fu, Linxin Xu, Qiwen Yu, Jiajia Fang, Guohua Zhao, Yi Li,
Chenying
Pan, Hao Dong, Di Wang, Haiyan Ren, Yi Guo, Qingjun Liu, Jun Liu,
Xing Chen. Artificial Intelligent Olfactory System for the Diagnosis
of Parkinson's Disease. ACS Omega, 2022; 7 (5): 4001 DOI: 10.1021/
acsomega.1c05060 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220223085828.htm

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