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Image recognition and neuroimaging are increasingly being used to understand the progression of Alzheimer's disease (AD). However, image data from single-photon emission computed tomography (SPECT) are limited. Medical image analysis requires large, labeled training datasets. Therefore, studies have focused on overcoming this problem. In this study, the detection performance of five convolutional neural network (CNN) models (MobileNet V2 and NASNetMobile (lightweight models); VGG16, Inception V3, and ResNet (heavier weight models)) on medical images was compared to establish a classification model for epidemiological research. Brain scan image data were collected from 99 subjects, and 4711 images were used. Demographic data were compared using the chi-squared test and one-way analysis of variance with Bonferroni's post hoc test. Accuracy and loss functions were used to evaluate the performance of CNN models. The cognitive abilities screening instrument and mini mental state exam scores of subjects with a clinical dementia rating (CDR) of 2 were considerably lower than those of subjects with a CDR of 1 or 0.5. This study analyzed the classification performance of various CNN models for medical images and proved the effectiveness of transfer learning in identifying the mild cognitive impairment, mild AD, and moderate AD scoring based on SPECT images. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on cognitive tests, imaging techniques and cerebrospinal fluid (CSF) levels of amyloid-β1-42 (Aβ42), total tau protein and hyperphosphorylated tau (p-tau). However, the available methods are expensive and relatively invasive. Artificial intelligence techniques like machine learning tools have being increasingly used in precision diagnosis. Methods: We conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD. Methods: We searched PubMed, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews for reviews and trials that investigated the machine learning and novel biomarkers in diagnosis of AD. Results: In additional to Aβ and tau-related biomarkers, biomarkers according to other mechanisms of AD pathology have been investigated. Neuronal injury biomarker includes neurofiliament light (NFL). Biomarkers about synaptic dysfunction and/or loss includes neurogranin, BACE1, synaptotagmin, SNAP-25, GAP-43, synaptophysin. Biomarkers about neuroinflammation includes sTREM2, and YKL-40. Besides, d-glutamate is one of coagonists at the NMDARs. Several machine learning algorithms including support vector machine, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model to distinguish patients with AD from healthy controls. Conclusions: Our results revealed machine learning with novel biomarkers and multiple variables may increase the sensitivity and specificity in diagnosis of AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing AD in outpatient clinics. [ABSTRACT FROM AUTHOR]

Objective: Alzheimer's disease (AD) is a complex and severe neurodegenerative disease and still lacks effective methods of diagnosis. Dysfunction of the N-methyl-D-aspartate receptor (NMDAR) has been found to be involved in synapse dysfunction and neurotoxicity of AD mechanisms. d-Serine, an NMDAR receptor coagonist, is reported as a potential new biomarker for AD. However, the results of serum and cerebrospinal fluid (CSF) d-serine levels are conflicting. We conducted a meta-analysis to investigate the serum and CSF d-serine levels in patients with AD. Methods: We searched PubMed, the Cochrane central register of controlled trials, and the Cochrane database of systematic reviews for trials that measured d-serine levels both in patients with AD and in controls. We included controlled trials that analyzed d-serine levels in human samples (e.g., serum and CSF). Studies were pooled using a random-effect model for comparisons between AD and control group. We used effect size (ES; expressed as d-serine levels) in each selected meta-analysis to calculate standardized mean difference (SMD). Positive values indicated increased d-serine levels in AD group. We presented results with 95% confidence intervals (CIs). The heterogeneity of the included trials was evaluated through visually inspecting funnel plots and using the I2 statistic. Moderators of effects were explored using metaregression. Results: Seven trials with more than 1186 participants were included in this meta-analysis. d-serine levels in patients with AD were significantly higher than those in controls (SMD = 0.679, 95% CI = 0.335 to 1.022, p < 0.001). Subgroup analyses showed that the AD group had significantly higher d-serine levels in serum and CSF compared with the control group (SMD = 0.566 (serum) and 1.008 (CSF); 95% CI = 0.183 to 0.948 (serum) and 0.168 to 1.849 (CSF)). Moreover, a metaregression revealed a significant negative association between ES and mean mini-mental state examination score in AD group (slope = −0.1203, p = 0.0004). Conclusions: Our results revealed higher d-serine levels in the serum and CSF of patients with AD relative to the controls. Further studies with a larger sample size and longer follow-up are recommended to clarify this association. [ABSTRACT FROM AUTHOR]

Background: An increasing number of studies have shown that the brain–gut–microbiota axis may significantly contribute to Alzheimer's disease (AD) pathogenesis. Moreover, impaired memory and learning involve the dysfunction neurotransmission of glutamate, the agonist of the N-methyl-d-aspartate receptor and a major excitatory neurotransmitter in the brain. This systematic review aimed to summarize the current cutting-edge research on the gut microbiota and glutamate alterations associated with dementia. Methods: PubMed, the Cochrane Collaboration Central Register of Controlled Clinical Trials, and Cochrane Systematic Reviews were reviewed for all studies on glutamate and gut microbiota in dementia published up until Feb 2020. Results: Several pilot studies have reported alterations of gut microbiota and metabolites in AD patients and other forms of dementia. Gut microbiota including Bacteroides vulgatus and Campylobacter jejuni affect glutamate metabolism and decrease the glutamate metabolite 2-keto-glutaramic acid. Meanwhile, gut bacteria with glutamate racemase including Corynebacterium glutamicum, Brevibacterium lactofermentum, and Brevibacterium avium can convert l-glutamate to d-glutamate. N-methyl-d-aspartate glutamate receptor (NMDAR)-enhancing agents have been found to potentially improve cognition in AD or Parkinson's disease patients. These findings suggest that d-glutamate (d-form glutamate) metabolized by the gut bacteria may influence the glutamate NMDAR and cognitive function in dementia patients. Conclusions: Gut microbiota and glutamate are potential novel interventions to be developed for dementia. Exploring comprehensive cognitive functions in animal and human trials with glutamate-related NMDAR enhancers are warranted to examine d-glutamate signaling efficacy in gut microbiota in patients with AD and other neurodegenerative dementias. [ABSTRACT FROM AUTHOR]