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Alzheimer's disease (AD) is the most common neurodegenerative disorder affecting elderly people worldwide. Currently, there are no effective treatments for AD able to prevent disease progression, highlighting the urgency of finding new therapeutic strategies to stop or delay this pathology. Several plants exhibit potential as source of safe and multi-target new therapeutic molecules for AD treatment. Meanwhile, Eucalyptus globulus extracts revealed important pharmacological activities, namely antioxidant and anti-inflammatory properties, which can contribute to the reported neuroprotective effects. This review summarizes the chemical composition of essential oil (EO) and phenolic extracts obtained from Eucalyptus globulus leaves, disclosing major compounds and their effects on AD-relevant pathological features, including deposition of amyloid-β (Aβ) in senile plaques and hyperphosphorylated tau in neurofibrillary tangles (NFTs), abnormalities in GABAergic, cholinergic and glutamatergic neurotransmission, inflammation, and oxidative stress. In general, 1,8-cineole is the major compound identified in EO, and ellagic acid, quercetin, and rutin were described as main compounds in phenolic extracts from Eucalyptus globulus leaves. EO and phenolic extracts, and especially their major compounds, were found to prevent several pathological cellular processes and to improve cognitive function in AD animal models. Therefore, Eucalyptus globulus leaves are a relevant source of biological active and safe molecules that could be used as raw material for nutraceuticals and plant-based medicinal products useful for AD prevention and treatment.

As population aging is becoming more common worldwide, applying artificial intelligence into the diagnosis of Alzheimer's disease (AD) is critical to improve the diagnostic level in recent years. In early diagnosis of AD, the fusion of complementary information contained in multimodality data (e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF)) has obtained enormous achievement. Detecting Alzheimer's disease using multimodality data has two difficulties: (1) there exists noise information in multimodal data; (2) how to establish an effective mathematical model of the relationship between multimodal data? To this end, we proposed a method named LDF which is based on the combination of low-rank representation and discriminant correlation analysis (DCA) to fuse multimodal datasets. Specifically, the low-rank representation method is used to extract the latent features of the submodal data, so the noise information in the submodal data is removed. Then, discriminant correlation analysis is used to fuse the submodal data, so the complementary information can be fully utilized. The experimental results indicate the effectiveness of this method., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Zhigang Li et al.)