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Mild cognitive impairment (MCI) is when brain function declines. MCI is the gray area transitioning from normal aging to the AD stage. Currently, the majority of early MCI diagnoses are processed through comprehensive neuropsychological tests. These tests may take the form of interviews, paper-and-pencil tests, or computer-based tests. There may be resistance from the subject if he/she has to undergo many screening tests simultaneously for multiple evaluation information, resulting in execution difficulty. The objectives of this study are to use 3D virtual reality to create an entertaining test scenario integrating the Mini-Cog, SPMSQ, MMSE, SLUMS, CDR, and CASI for middle-aged to older adults, furthermore, to employ fuzzy logic control (FLC) technology to develop a "MCI assessment system" for obtaining some pilot information for MCI assessment. There were 24 middle-aged to older adults aged from 50 to 65 years who participated in the evaluation experiment. The results showed that the MCI assessment system developed in this study is highly correlated with the traditional screening tests, including the Mini-Cog, SPMSQ, MMSE, SLUMS, and CASI. The assessment system can provide an integrated reference score for clinic workers in making judgments. In addition, the distribution of the System Usability Scale (SUS) evaluation scores for the MCI assessment system revealed that 87.5% were grade C (good to use) or above and 29.2% were grade B (extremely good to use) or above. The assessment system received positive feedback from the subjects. [ABSTRACT FROM AUTHOR]

Alzheimer's is a neurodegenerative disorder affecting the central nervous system and cognitive processes, explicitly impairing detailed mental analysis. Throughout this condition, the affected individual's cognitive abilities to process and analyze information gradually deteriorate, resulting in mental decline. In recent years, there has been a notable increase in endeavors aimed at identifying Alzheimer's disease and addressing its progression. Research studies have demonstrated the significant involvement of genetic factors, stress, and nutrition in developing this condition. The utilization of computer-aided analysis models based on machine learning and artificial intelligence has the potential to significantly enhance the exploration of various neuroimaging methods and non-image biomarkers. This study conducts a comparative assessment of more than 80 publications that have been published since 2017. Alzheimer's disease detection is facilitated by utilizing fundamental machine learning architectures such as support vector machines, decision trees, and ensemble models. Furthermore, around 50 papers that utilized a specific architectural or design approach concerning Alzheimer's disease were examined. The body of literature under consideration has been categorized and elucidated through the utilization of data-related, methodology-related, and medical-fostering components to illustrate the underlying challenges. The conclusion section of our study encompasses a discussion of prospective avenues for further investigation and furnishes recommendations for future research activities on the diagnosis of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

"Alzheimer's disease" (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. "Dementia" is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person's ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. "Deep learning" (DL) is a type of "machine learning" (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered "neural network" architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field. [ABSTRACT FROM AUTHOR]

The quantification of neurofibrillary tangles (NFTs) using specific PET tracers can facilitate the diagnosis of Alzheimer's disease (AD) and allow monitoring of disease progression and treatment efficacy. [18F]-THK523 has shown high affinity and selectivity for tau pathology. However, its high retention in white matter, which makes simple visual inspection difficult, may limit its use in research or clinical settings. In this paper, we optimized the automated radiosynthesis of [11C]-TKF and evaluated its biodistribution and toxicity in C57 mice. [11C]-TKF can be made by reaction precursor with [11C]MeOTf or 11CH3I, but [11C]MeOTf will give us higher labeling yields and specific activity. [11C]-TKF presented better brain uptake in normal mouse than [18F]-THK523 (3.23% ± 1.25% ID.g-1 vs. 2.62% ± 0.39% ID.g-1 at 2 min post-injection). The acute toxicity studies of [11C]-TKF were unremarkable. [ABSTRACT FROM AUTHOR]

Background: About 50 million people worldwide suffered from dementia in 2018—two-thirds of those with Alzheimer's disease (AD). By 2050, this number is expected to rise to 152 million—which is slightly larger than the country of Russia. About 90% of these people are over the age of 65, but early-onset dementia can occur at younger ages. Early detection is imperative to expedient treatment, which can improve outcomes over the span of diagnosis. Objectives: To conduct a meta-analysis of similar studies along with a systematic literature review to hasten the development of clinical practice guidelines to assist clinicians in the diagnosis of AD. We analyzed data points in each article published over the last 10 years to meet this objective: cost, efficiency, accuracy, acceptability (by physician and patient), patient satisfaction, and barriers to adoption. Methods: Four research databases were queried (PubMed, CINAHL Ultimate, Web of Science, and ScienceDirect). The review was conducted in accordance with a published protocol, the Kruse Protocol, and reported in accordance with PRISMA (2020). Results: Ten interventions were identified to help diagnose AD among older patients, and some involved a combination of methods (such as MRI and PET). The average sample size was 320.32 (SD = 437.51). These 10 interventions were identified as accurate, non-invasive, non-stressful, inexpensive, convenient, and rapid. Only one intervention was identified as statistically ineffective, and this same intervention was used effectively in other studies. The barriers identified were cost, training, expense of travel, and required physical presence of patient. The weighted average sensitivity was 85.16%, specificity was 88.53, and the weighted average effect size was 0.7339 (medium). Conclusion: Innovation can accurately diagnose AD, but not all methods are successful. Providers must ensure they have the proper training and familiarity with these interventions to ensure accuracy in diagnosis. While the physical presence of the patient is often required, many interventions are non-invasive, non-stressful, and relatively inexpensive. [ABSTRACT FROM AUTHOR]

Overexpression of the amyloid precursor protein (APP) and the hyperphosphorylation of the tau protein are vital in the understanding of the cause of Alzheimer's disease (AD). As a consequence, regulation of the expression of both APP and tau proteins is one important approach in combating AD. The APP and tau proteins can be targeted at the levels of transcription, translation and protein structural integrity. This paper reports the utilization of a bi-cistronic vector containing either APP or tau internal ribosome entry site (IRES) elements flanked by ß-galactosidase gene (cap-dependent) and secreted alkaline phosphatase (SEAP) (cap-independent) to discern the mechanism of action of memantine, an N-methyl-D-aspartate (NMDA) receptor antagonist. Results indicate that memantine could reduce the activity of both the APP and tau IRES at a concentration of ~10 µM (monitored by SEAP activity) without interfering with the cap-dependent translation as monitored by the ß-galactosidase assay. Western blot analysis of the tau protein in neuroblastoma (N2A) and rat hippocampal cells confirmed the halting of the expression of the tau proteins. We also employed this approach to identify a preparation named NB34, extracts of Boussingaultia baselloides (madeira-vine) fermented with Lactobacillus spp., which can function similarly to memantine in both IRES of APP and Tau. The water maze test demonstrated that NB34 could improve the spatial memory of a high fat diet induced neurodegeneration in apolipoprotein E-knockout (ApoE-/-) mice. These results revealed that the bi-cistronic vector provided a simple, and effective platform in screening and establishing the mechanistic action of potential compounds for the treatment and management of AD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a common neurological disorder and mental disability that causes memory loss and cognitive decline, presenting a major challenge to public health due to its impact on millions of individuals worldwide. It is crucial to diagnose and treat Alzheimer's in a timely manner to improve the quality of life of both patients and caregivers. In the recent past, machine learning techniques have showed potential in detecting Alzheimer's disease by examining neuroimaging data, especially Magnetic Resonance Imaging (MRI). This research proposes an attention-based mechanism that employs the vision transformer approach to detect Alzheimer's using MRI images. The presented technique applies preprocessing to the MRI images and forwards them to a vision transformer network for classification. This network is trained on the publicly available Kaggle dataset, and it illustrated impressive results with an accuracy of 99.06%, precision of 99.06%, recall of 99.14%, and F1-score of 99.1%. Furthermore, a comparative study is also conducted to evaluate the performance of the proposed method against various state-of-the-art techniques on diverse datasets. The proposed method demonstrated superior performance, outperforming other published methods when applied to the Kaggle dataset. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) represents the prevalent type of dementia in the elderly, and is characterized by the presence of neurofibrillary tangles and amyloid plaques that eventually leads to the loss of neurons, resulting in atrophy in specific brain areas. Although the process of degeneration can be visualized through various modalities of medical imaging and has proved to be a valuable biomarker, the accurate diagnosis of Alzheimer’s disease remains a challenge, especially in its early stages. In this paper, we propose a novel classification method for Alzheimer’s disease/cognitive normal discrimination in structural magnetic resonance images (MRI), based on the extension of the concept of histons to volumetric images. The proposed method exploits the relationship between grey matter, white matter and cerebrospinal fluid degeneration by means of a segmentation using supervoxels. The calculated histons are then processed for a reduction in dimensionality using principal components analysis (PCA) and the resulting vector is used to train an support vector machine (SVM) classifier. Experimental results using the OASIS-1 database have proven to be a significant improvement compared to a baseline classification made using the pipeline provided by Clinica software. [ABSTRACT FROM AUTHOR]

Background and Purpose: A majority studies on diagnosis of Alzheimer's Disease (AD) are based on an assumption: the training and testing data are drawn from the same distribution. However, in the diagnosis of AD and mild cognitive impairment (MCI), this identical-distribution assumption may not hold. To solve this problem, we utilize the transfer learning method into the diagnosis of AD. Methods: The MR (Magnetic Resonance) images were segmented using spm-Dartel toolbox and registrated with Automatic Anatomical Labeling (AAL) atlas, then the gray matter (GM) tissue volume of the anatomical region were computed as characteristic parameter. The information gain was introduced for feature selection. The TrAdaboost algorithm was used to classify AD, MCI, and normal controls (NC) data from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, meanwhile, the "knowledge" learned from ADNI was transferred to AD samples from local hospital. The classification accuracy, sensitivity and specificity were calculated and compared with four classical algorithms. Results: In the experiment of transfer task: AD to MCI, 177 AD and 40NC subjects were grouped as training data; 245 MCI and 45 remaining NC subjects were combined as testing data, the highest accuracy achieved 85.4%, higher than the other four classical algorithms. Meanwhile, feature selection that is based on information gain reduced the features from 90 to 7, controlled the redundancy efficiently. In the experiment of transfer task: ADNI to local hospital data, the highest accuracy achieved 93.7%, and the specificity achieved 100%. Conclusions: The experimental results showed that our algorithm has a clear advantage over classic classification methods with higher accuracy and less fluctuation. [ABSTRACT FROM AUTHOR]

Aptamers are versatile oligonucleotide ligands used for molecular recognition of diverse targets. However, application of aptamers to the field of amyloid β-protein (Aβ) has been limited so far. Aβ is an intrinsically disordered protein that exists in a dynamic conformational equilibrium, presenting time-dependent ensembles of short-lived, metastable structures and assemblies that have been generally difficult to isolate and characterize. Moreover, despite understanding of potential physiological roles of Aβ, this peptide has been linked to the pathogenesis of Alzheimer disease, and its pathogenic roles remain controversial. Accumulated scientific evidence thus far highlights undesirable or nonspecific interactions between selected aptamers and different Aβ assemblies likely due to the metastable nature of Aβ or inherent affinity of RNA oligonucleotides to β-sheet-rich fibrillar structures of amyloidogenic proteins. Accordingly, lessons drawn from Aβ-aptamer studies emphasize that purity and uniformity of the protein target and rigorous characterization of aptamers' specificity are important for realizing and garnering the full potential of aptamers selected for recognizing Aβ or other intrinsically disordered proteins. This review summarizes studies of aptamers selected for recognizing different Aβ assemblies and highlights controversies, difficulties, and limitations of such studies. [ABSTRACT FROM AUTHOR]

Background: On diagnosing Alzheimer's disease (AD), most existing imaging-based schemes have relied on analyzing the hippocampus and its peripheral structures. Recent studies have confirmed that volumetric variations are one of the primary indicators in differentiating symptomatic AD from healthy aging. In this study, we focused on deriving discriminative shape-based parameters that could effectively identify early AD from volumetric computerized tomography (VCT) delineation, which was previously almost intangible. Methods: Participants were 63 volunteers of Thai nationality, whose ages were between 40 and 90 years old. Thirty subjects (age 68.51 ± 5.5) were diagnosed with early AD, by using Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) criteria and the National Institute of Neurological and Communicative Disorders and the Stroke and the Alzheimer's disease and Related Disorders Association (NINCDS-ADRDA) criteria, while the remaining 33 were in the healthy control group (age 67.93 ± 5.5). The structural imaging study was conducted by using VCT. Three uninformed readers were asked to draw left and right hippocampal outlines on a coronal section. The resultant shapes were aligned and then analyzed with statistical shape analysis to obtain the first few dominant variational parameters, residing in hyperplanes. A supervised machine learning, i.e., support vector machine (SVM) was then employed to elucidate the proposed scheme. Results: Provided trivial delineations, relatively as low as 5 to 7 implicit model parameters could be extracted and used as discriminants. Clinical verification showed that the model could differentiate early AD from aging, with high sensitivity, specificity, accuracy and F-measure of 0.970, 0.968, 0.983 and 0.983, respectively, with no apparent effect of left-right asymmetry. Thanks to a less laborious task required, yet high discriminating capability, the proposed scheme is expected to be applicable in a typical clinical setting, equipped with only a moderate-specs VCT. [ABSTRACT FROM AUTHOR]

Background: On diagnosing Alzheimer's disease (AD), most existing imaging-based schemes have relied on analyzing the hippocampus and its peripheral structures. Recent studies have confirmed that volumetric variations are one of the primary indicators in differentiating symptomatic AD from healthy aging. In this study, we focused on deriving discriminative shape-based parameters that could effectively identify early AD from volumetric computerized tomography (VCT) delineation, which was previously almost intangible. Methods: Participants were 63 volunteers of Thai nationality, whose ages were between 40 and 90 years old. Thirty subjects (age 68.51 ± 5.5) were diagnosed with early AD, by using Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) criteria and the National Institute of Neurological and Communicative Disorders and the Stroke and the Alzheimer's disease and Related Disorders Association (NINCDS-ADRDA) criteria, while the remaining 33 were in the healthy control group (age 67.93 ± 5.5). The structural imaging study was conducted by using VCT. Three uninformed readers were asked to draw left and right hippocampal outlines on a coronal section. The resultant shapes were aligned and then analyzed with statistical shape analysis to obtain the first few dominant variational parameters, residing in hyperplanes. A supervised machine learning, i.e., support vector machine (SVM) was then employed to elucidate the proposed scheme. Results: Provided trivial delineations, relatively as low as 5 to 7 implicit model parameters could be extracted and used as discriminants. Clinical verification showed that the model could differentiate early AD from aging, with high sensitivity, specificity, accuracy and F-measure of 0.970, 0.968, 0.983 and 0.983, respectively, with no apparent effect of left-right asymmetry. Thanks to a less laborious task required, yet high discriminating capability, the proposed scheme is expected to be applicable in a typical clinical setting, equipped with only a moderate-specs VCT. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is the most common cause of dementia. Over the last three decades, research has advanced dramatically and provided a detailed understanding of the molecular events underlying the pathogenesis of Alzheimer's disease. In parallel, assays for the detection of biomarkers that reflect the typical Alzheimer's disease-associated pathology have been developed and validated in myriads of clinical studies. Such biomarkers complement clinical diagnosis and improve diagnostic accuracy. The use of biomarkers will become even more important with the advent of disease-modifying therapies. Such therapies will likely be most beneficial when administered early in the disease course. Here, we summarise the development of the core Alzheimer's disease cerebrospinal fluid biomarkers: amyloid-β and tau. We provide an overview of their role in cellular physiology and Alzheimer's disease pathology, and embed their development as cerebrospinal fluid biomarkers into the historical context of Alzheimer's disease research. Finally, we summarise recommendations for their use in clinical practice, and outline perspectives for novel cerebrospinal fluid candidate biomarkers. [ABSTRACT FROM AUTHOR]

Through early diagnosis of symptoms, the Alzheimer's disease process can be decelerated. The main concern is to encourage the population at risk to take responsible actions at the earliest stage of the onset of the disease. Persuasive communication is essential to achieve this. In an experimental study, the evaluation of awareness messages for early diagnosis containing weak and strong arguments and negative and positive images was performed on a sample of older Belgians. The mediating role of affective responses and message thoughts was explored. Strong arguments led to a more positive evaluation of the message than weak arguments directly and indirectly via the positive effect they had on message affect and thoughts, which, in turn, positively affected message evaluation. A negative message image led to a more positive message evaluation than a positive one. This effect was not mediated by either message affect or message thoughts. [ABSTRACT FROM AUTHOR]

Analysis of nonlinear quantitative EEG (qEEG) markers describing complexity of signal in relation to severity of Alzheimer's disease (AD) was the focal point of this study. In this study, 79 patients diagnosed with probable AD were recruited from the multi-centric Prospective Dementia Database Austria (PRODEM). EEG recordings were done with the subjects seated in an upright position in a resting state with their eyes closed. Models of linear regressions explaining disease severity, expressed in Mini Mental State Examination (MMSE) scores, were analyzed by the nonlinear qEEG markers of auto mutual information (AMI), Shannon entropy (ShE), Tsallis entropy (TsE), multiscale entropy (MsE), or spectral entropy (SpE), with age, duration of illness, and years of education as co-predictors. Linear regression models with AMI were significant for all electrode sites and clusters, where R² is 0.46 at the electrode site C3, 0.43 at Cz, F3, and central region, and 0.42 at the left region. MsE also had significant models at C3 with R² > 0.40 at scales τ = 5 and τ = 6. ShE and TsE also have significant models at T7 and F7 with R² > 0.30. Reductions in complexity, calculated by AMI, SpE, and MsE, were observed as the MMSE score decreased. [ABSTRACT FROM AUTHOR]

The analysis of electroencephalograms (EEGs) of patients with Alzheimer's disease (AD) could contribute to the diagnosis of this dementia. In this study, a new non-linear signal processing metric, distance-based Lempel-Ziv complexity (dLZC), is introduced to characterise changes between pairs of electrodes in EEGs in AD. When complexity in each signal arises from different sub-sequences, dLZC would be greater than when similar sub-sequences are present in each signal. EEGs from 11 AD patients and 11 age-matched control subjects were analysed. The dLZC values for AD patients were lower than for control subjects for most electrode pairs, with statistically significant differences (p < 0.01, Student's t-test) in 17 electrode pairs in the distant left, local posterior left, and interhemispheric regions. Maximum diagnostic accuracies with leave-one-out cross-validation were 77.27% for subject-based classification and 78.25% for epoch-based classification. These findings suggest not only that EEGs from AD patients are less complex than those from controls, but also that the richness of the information contained in pairs of EEGs from patients is also lower than in age-matched controls. The analysis of EEGs in AD with dLZC may increase the insight into brain dysfunction, providing complementary information to that obtained with other complexity and synchrony methods. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a degenerative brain disorder leading to memory loss and changes in other cognitive abilities. The complexity of electroencephalogram (EEG) signals may help to characterise AD. To this end, we propose an extension of multiscale entropy based on variance (MSEσ2) to multichannel signals, termed multivariate MSEσ2 (mvMSEσ2), to take into account both the spatial and time domains of time series. Then, we investigate the mvMSEσ2 of EEGs at different frequency bands, including the broadband signals filtered between 1 and 40 Hz, θ, α, and β bands, and compare it with the previously-proposed multiscale entropy based on mean (MSE), multivariate MSEμ (mvMSEμ), and MSEσ2, to distinguish different kinds of dynamical properties of the spread and the mean in the signals. Results from 11 AD patients and 11 age-matched controls suggest that the presence of broadband activity of EEGs is required for a proper evaluation of complexity. MSEσ2 and mvMSEσ2 results, showing a loss of complexity in AD signals, led to smaller p-values in comparison with MSEμ and mvMSEμ ones, suggesting that the variance-based MSE and mvMSE can characterise changes in EEGs as a result of AD in a more detailed way. The p-values for the slope values of the mvMSE curves were smaller than for MSE at large scale factors, also showing the possible usefulness of multivariate techniques. [ABSTRACT FROM AUTHOR]

Curcumin derivatives labelled with fluorine-18 or technetium-99m have recently shown their potential as diagnostic tools for Alzheimer's disease. Nevertheless, no study by exploiting the labelling with gallium-68 has been performed so far, in spite of its suitable properties (positron emitter, generator produced radionuclide). Herein, an evaluation of the affinity for synthetic β-amyloid fibrils and for amyloid plaques of three nat/68Ga-labelled curcumin analogues, namely curcumin curcumin (CUR), bis-dehydroxy-curcumin (bDHC) and diacetyl-curcumin (DAC), was performed. Affinity and specificity were tested in vitro on amyloid synthetic fibrils by using gallium-68 labelled compounds. Post-mortem brain cryosections from Tg2576 mice were used for the ex vivo visualization of amyloid plaques. The affinity of 68Ga(CUR)2 +, 68Ga(DAC)2 and +, and 68Ga(bDHC)2 + for synthetic β-amyloid fibrils was moderate and their uptake could be observed in vitro. On the other hand, amyloid plaques could not be visualized on brain sections of Tg2576 mice after injection, probably due to the low stability of the complexes in vivo and of a hampered passage through the blood-brain barrier. Like curcumin, all nat/68Ga-curcuminoid complexes maintain a high affinity for β-amyloid plaques. However, structural modifications are still needed to improve their applicability as radiotracers in vivo. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) and Alzheimer's disease (AD) can coexist in severely affected; elderly patients. Since they have different pathological causes and lesions and consequently require different treatments; it is critical to distinguish PD-related dementia (PD-D) from AD. Conventional electroencephalograph (EEG) analysis has produced poor results. This study investigated the possibility of using relative wavelet energy (RWE) and wavelet coherence (WC) analysis to distinguish between PD-D patients; AD patients and healthy elderly subjects. In EEG signals; we found that low-frequency wavelet energy increased and high-frequency wavelet energy decreased in PD-D patients and AD patients relative to healthy subjects. This result suggests that cognitive decline in both diseases is potentially related to slow EEG activity; which is consistent with previous studies. More importantly; WC values were lower in AD patients and higher in PD-D patients compared with healthy subjects. In particular; AD patients exhibited decreased WC primarily in the band and in links related to frontal regions; while PD-D patients exhibited increased WC primarily in the α and β bands and in temporo-parietal links. Linear discriminant analysis (LDA) of RWE produced a maximum accuracy of 79.18% for diagnosing PD-D and 81.25% for diagnosing AD. The discriminant accuracy was 73.40% with 78.78% sensitivity and 69.47% specificity. In distinguishing between the two diseases; the maximum performance of LDA using WC was 80.19%. We suggest that using a wavelet approach to evaluate EEG results may facilitate discrimination between PD-D and AD. In particular; RWE is useful for differentiating individuals with and without dementia and WC is useful for differentiating between PD-D and AD. [ABSTRACT FROM AUTHOR]

A large number of studies have analyzed measurable changes that Alzheimer's disease causes on electroencephalography (EEG). Despite being easily reproducible, those markers have limited sensitivity, which reduces the interest of EEG as a screening tool for this pathology. This is for a large part due to the poor signal-to-noise ratio of EEG signals: EEG recordings are indeed usually corrupted by spurious extra-cerebral artifacts. These artifacts are responsible for a consequent degradation of the signal quality. We investigate the possibility to automatically clean a database of EEG recordings taken from patients suffering from Alzheimer's disease and healthy age-matched controls. We present here an investigation of commonly used markers of EEG artifacts: kurtosis, sample entropy, zero-crossing rate and fractal dimension. We investigate the reliability of the markers, by comparison with human labeling of sources. Our results show significant differences with the sample entropy marker. We present a strategy for semi-automatic cleaning based on blind source separation, which may improve the specificity of Alzheimer screening using EEG signals. [ABSTRACT FROM AUTHOR]

Calreticulin is down-regulated in the cortical neurons of patients with Alzheimer's disease (AD) and may be a potential biomarker for the diagnosis of AD. A total of 128 AD patients were randomly recruited from May 2012 to July 2013. The mRNA levels of calreticulin were measured from the serum of tested subjects using real-time quantitative reverse transcriptase-PCR (real-time qRT-PCR). Serum levels of calreticulin were determined by ELISA and Western Blot. Serum levels of calreticulin in AD patients were significantly lower than those from a healthy group (p < 0.01). The baseline characters indicated that sample size, gender, mean age, diabetes and BMI (body mass index) were not major sources of heterogeneity. The serum levels of mRNA and protein of calreticulin were lower in AD patients than those from a healthy group, and negatively associated with the progression of AD according to CDR scores (p < 0.01). Thus, there is a trend toward decreased serum levels of calreticulin in the patients with progression of AD. Serum levels of calreticulin can be a negative biomarker for the diagnosis of AD patients. [ABSTRACT FROM AUTHOR]

Human stefin B is a protease inhibitor from the family of cystatins. It was reported that it forms oligomers in cells. We have shown that it has a role in cell's response to misfolded proteins. We also have shown that its oligomers bind amyloid-beta (Aβ). Here, we discuss ways, how stefin B could reduce build-up of protein aggregates by other proteins and consequently reduces ROS and, how this might be connected to autophagy. When overexpressed, stefin B forms protein aggregates itself and these protein aggregates induce autophagy. Similarly, cystatin C was shown to bind Aβ and to induce autophagy. It is also suggested how more knowledge about the role of stefin B in a cell's response to misfolded proteins could be used to modulate progressive myoclonus epilepsy of type 1 EPM1 disease. [ABSTRACT FROM AUTHOR]

Decline in visuo-spatial skills and memory failures are considered symptoms of Alzheimer's Disease (AD) and they can be assessed at early stages employing clinical tests. However, performance in a single test is generally not indicative of AD. Functional neuroimaging, such as functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests in an ecological setting to support diagnosis. Indeed, neuroimaging should not alter clinical practice allowing free doctor-patient interaction. However, block-designed paradigms, necessary for standard functional neuroimaging analysis, require tests adaptation. Novel signal analysis procedures (e.g., signal complexity evaluation) may be useful to establish brain signals differences without altering experimental conditions. In this study, we estimated fNIRS complexity (through Sample Entropy metric) in frontal cortex of early AD and controls during three tests that assess visuo-spatial and short-term-memory abilities (Clock Drawing Test, Digit Span Test, Corsi Block Tapping Test). A channel-based analysis of fNIRS complexity during the tests revealed AD-induced changes. Importantly, a multivariate analysis of fNIRS complexity provided good specificity and sensitivity to AD. This outcome was compared to cognitive tests performances that were predictive of AD in only one test. Our results demonstrated the capabilities of fNIRS and complexity metric to support early AD diagnosis. [ABSTRACT FROM AUTHOR]

Targeting exosome for liquid biopsy has gained significant attention for its diagnostic and therapeutic potential. For detecting neuronal disease diagnosis such as Alzheimer's disease (AD), the main technique for identifying AD still relies on positron-emission tomography (PET) imaging to detect the presence of amyloid-β (Aβ). While the detection of Aβ in cerebrospinal fluid has also been suggested as a marker for AD, the lack of quantitative measurements has compromised existing assays. In cerebrospinal fluid, in addition to Aβ, T-Tau, and P-Tau, alpha-synuclein has been considered a biomarker of neurodegeneration. This review suggests that and explains how the exosome can be used as a neuronal diagnostic component. To this end, we summarize current progress in exosome preparation/isolation and quantification techniques and comment on the outlooks for neuronal exosome-based diagnostic techniques. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD. [ABSTRACT FROM AUTHOR]

Determination of the conformation (monomer, oligomer, or fibril) of amyloid peptide aggregates in the human brain is essential for the diagnosis and treatment of Alzheimer’s disease (AD). Accordingly, systematic investigation of amyloid conformation using analytical tools is essential for precisely quantifying the relative amounts of the three conformations of amyloid peptide. Here, we developed a reduced graphene oxide (rGO) based multiplexing biosensor that could be used to monitor the relative amounts of the three conformations of various amyloid-β 40 (Aβ40) fluids. The electrical rGO biosensor was composed of a multichannel sensor array capable of individual detection of monomers, oligomers, and fibrils in a single amyloid fluid sample. From the performance test of each sensor, we showed that this method had good analytical sensitivity (1 pg/mL) and a fairly wide dynamic range (1 pg/mL to 10 ng/mL) for each conformation of Aβ40. To verify whether the rGO biosensor could be used to evaluate the relative amounts of the three conformations, various amyloid solutions (monomeric Aβ40, aggregated Aβ40, and disaggregated Aβ40 solutions) were employed. Notably, different trends in the relative amounts of the three conformations were observed in each amyloid solution, indicating that this information could serve as an important parameter in the clinical setting. Accordingly, our analytical tool could precisely detect the relative amounts of the three conformations of Aβ40 and may have potential applications as a diagnostic system for AD. [ABSTRACT FROM AUTHOR]