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• More citations. • Clarified research question and hypotheses. • Reorganized methods section for improved clarity. • Added more experimental results for findings not included in original manuscript. • Improved overall writing style and grammar. Mild Cognitive Impairment (MCI) is an early stage of Alzheimer's Disease (AD), often mistaken for natural aging. Early detection and treatment of MCI are crucial for effective treatment, but the condition can be difficult to diagnose. In recent years, multi-modal data and deep learning methods have shown promise in this field. The objective of this study is to develop a computer-aided MCI diagnosis system that effectively processes multi-modal data using deep learning methods. We proposed a Dual Fusion Cluster Graph Convolution Network (DFCGCN) model, which combines two channels of feature extraction, one adjacency matrix, and the Cluster GCN in series. Brain imaging is downsampled using graph pooling and flattened into sparse vectors, from which advanced features are extracted. Similarity between connectivity matrices is calculated using the Gaussian kernel function and combined with non-imaging details to construct a population graph that better represents inter-subject variability. Finally, features are assigned to subjects in the population graph, and node embeddings are learned using Cluster GCN to output diagnostic results. We tested the proposed algorithm on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving an accuracy, sensitivity, and specificity of 90.7%, 91.1%, and 94.0%, respectively. The DFCGCN model presented in this study enhances the diagnosis of MCI and outperforms other state-of-the-art algorithms. This approach has potential to be a valuable tool for early detection and treatment of MCI. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a common degenerative disease among the elderly population. In addition to cognitive impairment, AD is often accompanied by behavioral manifestations. However, little attention has been paid to changes in bone metabolism and related mechanisms in patients with AD. We found that AD mice (APPswe/PS1dE9) had reduced bone density, weakened bone strength, and amyloid beta (Aβ) deposition in the bone tissue. It was further found that targeting autophagy receptors Optineurin (OPTN) and Sequestosome 1 (SQSTM1) increased bone density and bone strength in AD mice, promoted the clearance of Aβ in the bone tissue, and maintained bone homeostasis. Our study suggests that abnormal Aβ deposition may be the co-pathogenesis of AD and osteoporosis (OP). Targeting OPTN and SQSTM1 has a dual-functional effect of alleviating both AD and OP through selective autophagy that specifically targets Aβ for clearance. Therapeutic strategies targeting autophagy may help guide the treatment of patients with AD complicated with OP. • Alzheimer's disease is often accompanied by behavioral manifestations. • Aβ deposition may be the co-pathogenesis of Alzheimer's disease and osteoporosis. • Selective autophagy contributes to the clearance of abnormally deposited Aβ. • Targeting autophagy is beneficial for Alzheimer's disease and osteoporosis. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and accumulating evidence suggested that proteostatic imbalance is a key feature of the disease. Traditional Chinese medicine exhibits a multi-target therapeutic effect, making it highly suitable for addressing protein homeostasis imbalance in AD. Dendrobium officinale is a traditional Chinese herbs commonly used as tonic agent in China. In this study, we investigated protection effects of D. officinale phenolic extract (SH-F) and examined its underlying mechanisms by using transgenic Caenorhabditis elegans models. We found that treatment with SH-F (50 μg/mL) alleviated Aβ and tau protein toxicity in worms, and also reduced aggregation of polyglutamine proteins to help maintain proteostasis. RNA sequencing results showed that SH-F treatment significantly affected the proteolytic process and autophagy–lysosomal pathway. Furthermore, we confirmed that SH-F showing maintainance of proteostasis was dependent on bec-1 by qRT-PCR analysis and RNAi methods. Finally, we identified active components of SH-F by LC-MS method, and found the five major compounds including koaburaside, tyramine dihydroferulate, N-p-trans-coumaroyltyramine, naringenin and isolariciresinol are the main bioactive components responsible for the anti-AD activity of SH-F. Our findings provide new insights to develop a treatment strategy for AD by targeting proteostasis, and SH-F could be an alternative drug for the treatment of AD. [Display omitted] [ABSTRACT FROM AUTHOR]