Variants in the APOE gene region may explain ethnic differences in the association of Alzheimer’s disease (AD) with ε4. Ethnic differences in allele frequencies for three APOE region SNPs (single nucleotide polymorphisms) were identified and tested for association in 19,398 East Asians (EastA), including Koreans and Japanese, 15,836 European ancestry (EuroA) individuals, and 4985 African Americans, and with brain imaging measures of cortical atrophy in sub-samples of Koreans and EuroAs. Among ε4/ε4 individuals, AD risk increased substantially in a dose-dependent manner with the number of APOE promoter SNP rs405509 T alleles in EastAs (TT: OR (odds ratio) = 27.02, p = 8.80 × 10−94; GT: OR = 15.87, p = 2.62 × 10−9) and EuroAs (TT: OR = 18.13, p = 2.69 × 10−108; GT: OR = 12.63, p = 3.44 × 10−64), and rs405509-T homozygotes had a younger onset and more severe cortical atrophy than those with G-allele. Functional experiments using APOE promoter fragments demonstrated that TT lowered APOE expression in human brain and serum. The modifying effect of rs405509 genotype explained much of the ethnic variability in the AD/ε4 association, and increasing APOE expression might lower AD risk among ε4 homozygotes.

This study examines brain functional connectivity in both cognitively normal seniors and patients with mild cognitive impairment (MCI) to elucidate prospective markers of MCI. A homemade four‐channel functional near‐infrared spectroscopy (fNIRS) system was employed to measure hemodynamic responses in the subjects' prefrontal cortex during a resting state, an oddball task, a 1‐back task, and a verbal fluency task. Brain functional connectivity was calculated as the Pearson correlation coefficients between fNIRS channels. The results show that during the verbal fluency task, while the healthy control (HC) group presents a significantly stronger inter‐hemispheric connectivity compared to intra‐hemispheric connectivity, there is no difference between the inter‐ and intra‐hemispheric connectivity in the MCI group. In addition, a comparison between the MCI and HC connectivity reveals that the MCI group has a statistically higher right and inter‐hemispheric connectivity during the resting state, but a significantly lower left and inter‐hemispheric connectivity during the verbal fluency test. These findings demonstrate the potential of fNIRS to study brain functional connectivity in neurodegenerative diseases.

PURPOSEGrinding with less stress on 3Y-TZP through proper selection of methods and instruments can lead to a long-term success of prosthesis. The purpose of this study was to compare the phase transformation and physical properties after zirconia surface grinding with 3 different grinding burs.MATERIALS AND METHODSForty disc-shaped zirconia specimens were fabricated. Each Ten specimens were ground with AllCeramic SuperMax (NTI, Kahla, Germany), Dura-Green DIA (Shofu Inc., Kyoto, Japan), and Dura-Green (Shofu Inc., Kyoto, Japan). Ten specimens were not ground and used as a control group. After the specimen grinding, XRD analysis, surface roughness test, FE-SEM imaging, and biaxial flexural strength test were performed.RESULTSAfter surface grinding, small amount of monoclinic phase in all experimental groups was observed. The phase change was higher in specimens, which were ground with Dura-Green DIA and AllCeramic SuperMax burs. The roughness of surfaces increased in specimens, which were ground with Dura-Green DIA and AllCeramic SuperMax burs than control groups and ground with Dura-Green. All experimental groups showed lower flexural strength than control group, but there was no statistically significant difference between control group and ground with Dura-Green DIA and AllCeramic SuperMax burs. The specimens, which were ground with Dura- Green showed the lowest strength.CONCLUSIONThe use of dedicated zirconia-specific grinding burs such as Dura-Green DIA and AllCeramic SuperMax burs decreases the grinding time and did not significantly affect the flexural strength of zirconia, and therefore, they may be recommended. However, a fine polishing process should be accompanied to reduce the surface roughness after grinding.

Background/Aims: Disease-modifying therapy for Alzheimer’s disease (AD) has led to a need for biomarkers to identify prodromal AD and very early stage of AD dementia. We aimed to identify the cutoff values of cerebrospinal fluid (CSF) biomarkers for detecting prodromal AD. Methods: We assessed 56 patients with amnestic mild cognitive impairment (aMCI) who underwent lumbar puncture. Additionally, 87 healthy elderly individuals and 34 patients with AD dementia served as controls. Positron emission tomography was performed using florbetaben as a probe. We analyzed the concentration of Aβ_1–42, total tau protein (t-Tau), and tau protein phosphorylated at threonine 181 (p-Tau¹⁸¹) in CSF with INNOTEST enzyme-linked immunosorbent assay. Results: For the detection of prodromal AD in patients with aMCI, the cutoff values of CSF Aβ_1–42, t-Tau, and p-Tau¹⁸¹ were 749.5 pg/mL, 225.6 pg/mL, and 43.5 pg/mL, respectively. To discriminate prodromal AD in patients with aMCI, the t-Tau/Aβ_1–42 and ­p-Tau¹⁸¹/Aβ_1–42 ratios defined cutoff values at 0.298 and 0.059, respectively. Conclusions: CSF biomarkers are very useful tools for the differential diagnosis of prodromal AD in aMCI patients. The concentration of CSF biomarkers is well correlated with the stages of the AD spectrum.

In this paper, we present a two-stage ensemble-based approach to localize the anatomical structure of interest from magnetic resonance imaging (MRI) scans. We combine a Hough voting method with a convolutional neural network to automatically localize brain anatomical structures such as the hippocampus. The hippocampus is one of the regions that can be affected by the Alzheimer's disease, and this region is known to be related to memory loss. The structural changes of the hippocampus are important biomarkers for dementia. To analyze the structural changes, accurate localization plays a vital role. Furthermore, for segmentation and registration of anatomical structures, exact localization is desired. Our proposed models use a deep convolutional neural network (CNN) to calculate displacement vectors by exploiting the Hough voting strategy from multiple 3-viewpoint patch samples. The displacement vectors are added to the sample position to estimate the target position. To efficiently learn from samples, we employed a local and global strategy. The multiple global models were trained using randomly selected 3-viewpoint patches from the whole MRI scan. The results from global models are aggregated to obtain global predictions. Similarly, we trained multiple local models, extracting patches from the vicinity of the hippocampus location and assembling them to obtain a local prediction. The proposed models exploit the Alzheimer's disease neuroimaging initiative (ADNI) MRI dataset and the Gwangju Alzheimer's and related dementia (GARD) cohort MRI dataset for training, validating and testing. The average prediction error using the proposed two-stage ensemble Hough convolutional neural network (Hough-CNN) models are 2.32 and 2.25 mm for the left and right hippocampi, respectively, for 65 test MRIs from the GARD cohort dataset. Similarly, for the ADNI MRI dataset, the average prediction error for the left and right hippocampi are 2.31 and 2.04 mm, respectively, for 56 MRI scans.