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Alzheimer's disease (AD), the most common form of dementia, is a progressive neurodegenerative disease. In the past decades, numbers of promising drug candidates showed significant anti‐AD effects in preclinical studies but failed in clinical trials. One of the major reasons might be the limitation of appropriate animal models for evaluating anti‐AD drugs. More than 95% of AD cases are sporadic AD (sAD). However, the anti‐AD drug candidates were mainly tested in the familial AD (fAD) animal models. The diversity between the sAD and fAD might lead to a high failure rate during the development of anti‐AD drugs. Therefore, an ideal sAD animal model is urgently needed for the development of anti‐AD drugs. Here, we summarized the available sAD animal models, including their methodology, pathologic features, and potential underlying mechanisms. The limitations of these sAD animal models and future trends in the field were also discussed. HIGHLIGHTS: Alzheimer's disease (AD) is the most common dementia and no drug for it untill now.Sporadic AD (sAD) animal models can be established by metabolic damage, aging, traumatic brain injury, and acrolein.sAD animal models can also be established by introducing protein phosphatase 2A inhibitor peptide, metals, and combining factors.Nonhuman primates may overcome the gap between rodents and humans for AD animal model.Herpes simplex virus type 1 and other pathogens may be used in establishing sAD animal models. [ABSTRACT FROM AUTHOR]

The importance of automating the diagnosis of Alzheimer disease (AD) towards facilitating its early prediction has long been emphasized, hampered in part by lack of empirical support. Given the evident association of AD with age and the increasing aging population owing to the general well-being of individuals, there have been unprecedented estimated economic complications. Consequently, many recent studies have attempted to employ the language deficiency caused by cognitive decline in automating the diagnostic task via training machine learning (ML) algorithms with linguistic patterns and deficits. In this study, we aim to develop multiple heterogeneous stacked fusion models that harness the advantages of several base learning algorithms to improve the overall generalizability and robustness of AD diagnostic ML models, where we parallelly utilized two different written and spoken-based datasets to train our stacked fusion models. Further, we examined the effect of linking these two datasets to develop a hybrid stacked fusion model that can predict AD from written and spoken languages. Our feature spaces involved two widely used linguistic patterns: lexicosyntactics and character n-gram spaces. We firstly investigated lexicosyntactics of AD alongside healthy controls (HC), where we explored a few new lexicosyntactic features, then optimized the lexicosyntactic feature space by proposing a correlation feature selection technique that eliminates features based on their feature-feature inter-correlations and feature-target correlations according to a certain threshold. Our stacked fusion models establish benchmarks on both datasets with AUC of 98.1% and 99.47% for the spoken and written-based datasets, respectively, and corresponding accuracy and F1 score values around 95% on spoken-based dataset and around 97% on the written-based dataset. Likewise, the hybrid stacked fusion model on linked data presents an optimal performance with 99.2% AUC as well as accuracy and F1 score falling around 97%. In view of the achieved performance and enhanced generalizability of such fusion models over single classifiers, this study suggests replacing the initial traditional screening test with such models that can be embedded into an online format for a fully automated remote diagnosis. [ABSTRACT FROM AUTHOR]

The aims of this study were to examine the levels of serum and exosomal miR-137, miR-155 and miR-223, three neuroinflammation-related miRNAs, in dementia patients and to explore the value of these miRNAs for the diagnosis and prognostic evaluation of dementia. Thirty-two patients with dementia were enrolled, and sixteen volunteers without dementia served as controls. Serum exosomes were isolated by precipitation with ExoQuick and characterized by western blotting, nanoparticle-tracking analysis and immunofluorescence microscopy. The levels of both total serum miRNAs and serum exosomal miRNAs were determined by real-time quantitative PCR. Total serum miRNAs and serum exosomal miRNAs were both detected to be down-regulated. The median level of serum exosomal miR-223 was significantly decreased in dementia patients ( p  < 0.01). The level of miR-223 was significantly correlated with Mini-Mental State Examination (MMSE) scores, Clinical Dementia Rating (CDR) scores, magnetic resonance spectroscopy (MRS) spectral ratios and serum concentrations of IL-1β, IL-6, TNF-α, and CRP. The diagnostic utility of exosomal miR-233 was evaluated by the area under the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) was 0.875. This study suggests that serum exosomal miR-223 is a promising biomarker for diagnosing dementia and evaluating the progression of disease. [ABSTRACT FROM AUTHOR]