The
study of Alzheimer’s disease (AD), the most common cause
of dementia, faces challenges in terms of understanding the cause,
monitoring the pathogenesis, and developing early diagnoses and effective
treatments. Rapid and accurate identification of AD biomarkers in
the brain is critical to providing key insights into AD and facilitating
the development of early diagnosis methods. In this work, we developed
a platform that enables a rapid screening of AD biomarkers by employing
graphene-assisted Raman spectroscopy and machine learning interpretation
in AD transgenic animal brains. Specifically, we collected Raman spectra
on slices of mouse brains with and without AD and used machine learning
to classify AD and non-AD spectra. By contacting monolayer graphene
with the brain slices, the accuracy was increased from 77% to 98%
in machine learning classification. Further, using a linear support
vector machine (SVM), we identified a spectral feature importance
map that reveals the importance of each Raman wavenumber in classifying
AD and non-AD spectra. Based on this spectral feature importance map,
we identified AD biomarkers including Aβ and tau proteins and
other potential biomarkers, such as triolein, phosphatidylcholine,
and actin, which have been confirmed by other biochemical studies.
Our Raman–machine learning integrated method with interpretability
will facilitate the study of AD and can be extended to other tissues
and biofluids and for various other diseases.
Once printed, books are always accompanied by the smells of volatile organic compounds (VOCs) which are continuously emitted not only by inks but also by papers themselves throughout their lives. Although the VOCs from papers may bring mild discomfort to readers, they are considered as very important factors that feature the degradation of papers and show potential applications in cultural relic appraisal. In this study, an analytical approach based on solid phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC/MS) was proposed for the evaluation of volatile organic compounds (VOCs) emitted by Chinese traditional handmade papers. The VOCs evaluations and artificial aging processes were both applied to recent-made papers and naturally aged papers from a traditional Chinese calligraphy and painting scroll (collected by the National Museum of China). To be noticed, a large number of aliphatic acids, aldehydes, ketones, furan derivatives, benzene series and terpenoid substances indicated that the VOCs signals not only reveal the degradation of paper but also tentatively reflect the storage environment along hundreds of years ago. The semi-quantitative evaluation of markers indicated that the historical paper is under a serious degradation due to the high capacity it releases. Our results provided a path way to get the degradation information of ancient paintings as well as potential realistic applications such as the conservation of paper-based relics and the environmental protection in libraries and museums.
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