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Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disorder. Mild cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a progressive state (i.e., pMCI) and a stable state (i.e., sMCI). With the development of deep learning, the convolutional neural networks (CNNs) have made great progress in image recognition using magnetic resonance imaging (MRI) and positron emission tomography (PET) for AD diagnosis. However, due to the limited availability of these imaging data, it is still challenging to effectively use CNNs for AD diagnosis. Toward this end, we design a novel deep learning framework. Specifically, the virtues of 3D-CNN and fully stacked bidirectional long short-term memory (FSBi-LSTM) are exploited in our framework. First, we design a 3D-CNN architecture to derive deep feature representation from both MRI and PET. Then, we apply FSBi-LSTM on the hidden spatial information from deep feature maps to further improve its performance. Finally, we validate our method on the AD neuroimaging initiative (ADNI) dataset. Our method achieves average accuracies of 94.82%, 86.36%, and 65.35% for differentiating AD from normal control (NC), pMCI from NC, and sMCI from NC, respectively, and outperforms the related algorithms in the literature.

A rapid and efficient method for the separation and purification of fucoxanthin from edible brown algae by microwave-assisted extraction coupled with high-speed countercurrent chromatography was developed. The algae were first extracted using microwave-assisted extraction, then the dried extract was dissolved and directly introduced into the high-speed countercurrent chromatography system with a two-phase solvent system consisting of hexane-ethyl acetate-ethanol-water (5:5:6:4, v/v/v/v). The isolation was done in less than 75 min, and a total of 0.83 mg, 1.09 mg, and 0.20 mg fucoxanthin were obtained from 25.0 g fresh Laminaria japonica Aresch, 1.5 g dry Undaria pinnatifida (Harv) Sur, and 15.0 g dry Sargassum fusiforme (Harv) Setch, respectively. The purity of fucoxanthin determined by HPLC was over 90% and its structure was further identified by LC-ESI-MS and (1) H-NMR.

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Silica particle chemically modified with ionic liquid is used for the first time as the stationary phase in HPLC for the separation of alkaloids following a dual mechanism.

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