The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer’s diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer’s disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer’s disease.
ObjectiveTo systematically review the diagnostic value of miRNAs for Alzheimer’s disease (AD).MethodsPubMed, Web of Science, EMbase, The Cochrane Library, CNKI, WanFang Data, VIP, and CBM databases were electronically searched to collect diagnostic tests of miRNAs for AD from inception to October 31, 2020. Two researchers independently screened literature, extracted data, and assessed the risk of bias of the included studies. RevMan 5.3 and Stata 14.0 software were used for meta-analysis. ResultsA total of 22 studies involving 4 006 subjects were included. The meta-analysis results showed that the pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and the areas under the working characteristic curve of miRNA in AD diagnosis were 0.83 (95%CI 0.79 to 0.87), 0.80 (95%CI 0.76 to 0.83), 4.07 (95%CI 3.37 to 4.92), 0.21 (95%CI 0.17 to 0.27), 19.20 (95%CI 12.96 to 28.48) and 0.88 (95%CI 0.85 to 0.90), respectively. ConclusionThe current evidence shows that miRNAs have a high diagnostic value for AD. However, because of the limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusion.
We in the present research proposed a classification method that applied infomax independent component analysis (ICA) to respectively extract single modality features of structural magnetic resonance imaging (sMRI) and positron emission tomography (PET). And then we combined these two features by using a method of weight combination. We found that the present method was able to improve the accurate diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Compared AD to healthy controls (HC): the study achieved a classification accuracy of 93.75%, with a sensitivity of 100% and a specificity of 87.64%. Compared MCI to HC: classification accuracy was 89.35%, with a sensitivity of 81.85% and a specificity of 99.36%. The experimental results showed that the bi-modality method performed better than the individual modality in comparison to classification accuracy.
Caveolin-1 (Cav-1) protein plays a very important role in the central nervous system, and is closely related to Alzheimer’s disease (AD). Through literature review, this article summarizes the present research status of Cav-1 protein in the field of AD from three aspects: the relationship between Cav-1 gene and AD; the relationship of Cav-1 protein with learning and memory; the relationship of Cav-1 protein with amyloid β-protein and Tau protein. And the aim of this paper is to provide a new thought and evidence for exploring the mechanism of AD via Cav-1 protein.
With the wide application of deep learning technology in disease diagnosis, especially the outstanding performance of convolutional neural network (CNN) in computer vision and image processing, more and more studies have proposed to use this algorithm to achieve the classification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal cognition (CN). This article systematically reviews the application progress of several classic convolutional neural network models in brain image analysis and diagnosis at different stages of Alzheimer’s disease, and discusses the existing problems and gives the possible development directions in order to provide some references.
ObjectivesTo systematically review the efficacy and safety of butylphthalide soft capsule with routine treatment for Alzheimer’s disease (AD).MethodsDatabases including CNKI, WanFang Data, VIP, CBM, PubMed, EMbase, and The Cochrane Library were electronically searched from September 2002 to July 2018 to collect randomized controlled trials of butylphthalide soft capsule with routine treatment for Alzheimer’s disease. The trial was screened based on inclusion and exclusion criteria, and the methodological quality of the included trial was assessed. Meta-analysis was then performed by Revman 5.3 software.ResultsA total of 8 studies involving 576 patients were included. The butylphthalide soft capsule group included 283 patients and the control group included 293 patients. The result of meta-analysis showed that butylphthalide soft capsule with routine treatment (Donepezil hydrochloride or Memantine or EGb761) significantly improved the score of mini-mental state examination (MMSE) (MD=3.19, 95% CI 2.69 to 3.69, P<0.001) and clinical efficacy (RR=1.36, 95%CI 1.21 to 1.53, P<0.001). There was no significant difference in number of adverse events between the butylphthalide group and the control group (RR=1.13, 95%CI 0.77 to 1.67, P=0.52).ConclusionsBased on the routine treatment, combining with butylphthalide soft capsule can further facilitate cognitive function of AD and improve clinical efficacy. At the same time, no increase in adverse reactions has been found. However, due to the low quality of the included studies, more high quality randomized controlled trials are required to verify the results.
Objective To analyze whether there is a causal association between psoriasis and Alzheimer disease (AD) by a two-sample two-way Mendelian randomization (MR) method. Methods In the forward study, the single nucleotide polymorphisms (SNPs) associated with psoriasis were obtained from the comprehensive statistical data of the genome-wide association study database as the instrumental variables, and AD as the outcome; in the reverse study, the SNPs associated with AD were taken as instrumental variables, and psoriasis as the outcome. Using two-sample two-way MR analysis, the odds ratio (OR) value and 95% confidence interval (CI) of regression models, namely inverse variance weighted (IVW) method, MR-Egger regression method, weighted median method, simple pattern method, and weighted pattern method, were used to evaluate the causal relationship between psoriasis and AD. Cochran’s Q test was used to assess the heterogeneity of genetic instrumental variables, MR-Egger intercept method was used to test the horizontal pleiotropy of the assessment, “leave-one-out” method was used to assess the sensitivity of a SNP to the effect of causality, and the symmetry of funnel plot was observed to assess bias. Results A total of 19 SNPs associated with psoriasis were included as instrumental variables in the forward study. The IVW analysis of the forward study showed that there was a causal correlation between psoriasis and AD [OR=1.032, 95%CI (1.014, 1.051), P<0.001], and MR-Egger regression method [OR=1.042, 95%CI (1.012, 1.073), P=0.013], weighted median [OR=1.048, 95%CI (1.023, 1.074), P<0.001], and weighted model [OR=1.046, 95%CI (1.020, 1.073), P=0.002] all supported this result. Heterogeneity test (IVW result: Q=13.752, P=0.745; MR-Egger regression result: Q=13.134, P=0.727), MR-Egger intercept method (Egger intercept=–0.004, P=0.442), the results of “leave-one-out” method and funnel plot showed that the results of MR analysis were reliable. A total of 127 AD-related SNPs were included as instrumental variables in the reverse study. In reverse research, there was no evidence to support the AD could increase the risk of psoriasis (P>0.05). Heterogeneity test (IVW result: Q=232.496, P<0.001; MR-Egger regression result: Q=232.119, P<0.001) suggested heterogeneity, but MR-Egger intercept method (Egger intercept=0.003, P=0.652), the results of “leave-one-out” method and funnel plot showed that the results of MR analysis were reliable. Conclusion There is a causal association between psoriasis and AD, and psoriasis may increase the risk of AD.
This article investigates the role of AMP-activated protein kinase (AMPK) and its downstream signaling targets in mediating cellular processes such as autophagy, apoptosis, and inflammation, offering insights into how acupuncture may treat common central nervous system (CNS) diseases, including ischemic stroke, spinal cord injury, Parkinson disease, and Alzheimer disease. AMPK and its downstream effectors are pivotal in the signaling pathways that underlie the pathophysiology of CNS diseases. These pathways are implicated in a variety of cellular responses that contribute to the progression of neurological disorders. During CNS injury, AMPK can be activated through phosphorylation, triggering the regulation of downstream molecules and exerting protective effects on neuronal function. Acupuncture has been shown to promote neuroprotection and enhance recovery in CNS diseases through multiple mechanisms, one of which involves the activation of AMPK-related signaling pathways. Nevertheless, numerous unresolved challenges remain in this research field.
It is generally considered that various regulatory activities between genes are contained in the gene expression datasets. Therefore, the underlying gene regulatory relationship and the biologically useful information can be found by modeling the gene regulatory network from the gene expression data. In our study, two unsupervised matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF), were proposed to identify significant genes and model the regulatory network using the microarray gene expression data of Alzheimer's disease (AD). By bio-molecular analyzing of the pathways, the differences between ICA and NMF have been explored and the fact, which the inflammatory reaction is one of the main pathological mechanisms of AD, is also emphasized. It was demonstrated that our study gave a novel and valuable method for the research of early detection and pathological mechanism, biomarkers' findings of AD.
Biological markers play a pivotal role in the early and accurate diagnosis of Alzheimer’s disease, enabling precise identification and monitoring of therapeutic interventions. The detection of central β-amyloid and Tau proteins has become an indispensable tool in clinical trials. Recent years have witnessed substantial progress in the development of readily accessible and cost-effective blood biomarkers. This comprehensive article provides a comprehensive overview of the clinical applications of blood biomarkers, encompassing β-amyloid, phosphorylated Tau protein, neurofilament light chain protein, and glial fibrillary acidic protein, all of which have demonstrated clinical relevance in Alzheimer’s disease diagnosis. Notably, phosphorylated Tau protein exhibits superior diagnostic efficacy. The incorporation of blood biomarkers facilitates early screening, accurate diagnosis, and efficacious treatment of Alzheimer’s disease.