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Alzheimer is a disease that has aroused the interest of several researchers around the world for decades, and so far, no cure has been found to end its proliferation. One of the critical symptoms of AD is memory loss, something that puts the patient at great risk and puts his family in pain. This research aims to resolve this problem, it focuses on the development of an integrated solution, which will help Alzheimer's patient to have a certain autonomy and to create a certain peace of mind among their relatives. This project is divided into two main parts an electronic bracelet and an informatic platform. Hence, the electronic bracelet, which is mainly based on a GPS technology, will allow a real-time monitoring of the movements of the patient wearing it, and the informatic platform will allow the people taking care of the patient to visualise his actual position. This smart solution will reduce the risk of patients to get lost in the streets, and can gather information form patient all over the world to study their behaviour, and consequently have more comprehensibility of this disease. In this article, we will present the core concept of the electronic bracelet, and the architecture of the informatic platform. [ABSTRACT FROM AUTHOR]

With the advent of an aging society, the number of people who are being afflicted with Alzheimer’s disease is also on a gradual rise. However, an effective medical treatment to contain the contraction of the disease still does not exist. There is a widespread lack of understanding about dementia among the general populace, and the amount of available research into early pathological prediction of Alzheimer's disease is also quite low. This study proposes a testing system to detect cases of dementia, which is designed to assist doctors in diagnosing the disease. The system can be applied to embedded devices and mobile devices in the hospital in the future to promote the development of artificial intelligence in the medical field and improve diagnosis efficiency. On the basis of minuscule size and a small number of parameters, lightweight convolutional neural networks can be deployed on devices with finite memory and computing without connecting any cloud platform to avoid the breach of image data and ensure the quality of its security. For this purpose, three common lightweight convolutional neural networks are used in this study — MobileNet V2, NASNetMobile, and ShuffleNet V2. In cases where the parameters are identical to those corresponding to other conditions, the Alzheimer's disease predictive identification is applied to use open-source magnetic resonance imaging (MRI) scans obtained from the Kaggle platform. The results of the study indicate that MobileNet V2 exhibits the highest prediction accuracy (80.78%). Additionally, the system proposed in this study can be integrated into physicians' workflows during the diagnosis of Alzheimer's disease, thereby making their medical judgments more accurate. It can, therefore, address the tedious and time-consuming nature of the current methods of diagnosis of the disease, improve the efficiency of the medical treatment of patients, and improve the possibility of early detection of the disease. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to provide an overview of the possible factors that cause Alzheimer's disease, how it is related with patterns of people who are more prone to have it. Moreover, this study will give a description of the gene apolipoprotein E(APOE) and how it could be related with other genes that affect people's health. Furthermore, this research will show the correlation with other illnesses such as: diabetes type 2. In addition, a brief of brain studies, people's behavior, diagnosis, proposal for prevention activities and risk factor analysis for the measures established in advanced stages of the disease. At the end of this research, it will discuss measures to control the illness, how to detect it in time to prevent it in relatives and future generations, and the prescription and medicaments more used in patients during all the cycle of the disease. In summary, this research will give synopsis and findings for the correlation between factors, pre-existing conditions, medicaments, environment, and genetics of people who have Alzheimer's disease and people who are more prone to have it. [ABSTRACT FROM AUTHOR]

Copyright of CISTI (Iberian Conference on Information Systems & Technologies / Conferência Ibérica de Sistemas e Tecnologias de Informação) Proceedings is the property of Conferencia Iberica de Sistemas Tecnologia de Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Neurodegenerative diseases are a group of pathologies that mainly affect neurons of the human brain. Particularly, Alzheimer disease (AD) is a neurodegenerative disorder, being manifested by neuronal and synaptic loss. One of the main pathological aspects of AD is aggregation of amyloid-β (Aβ) peptide. However, there are some neuropeptides that play an important role in immunity and neuroprotection. In addition, the presence of metal ions and their abnormal accumulation play a key role in this pathology. The interaction of Aβ peptides with metal ions produces a change in peptide conformation and leads to the occurrence of peptide-metal complexes. However, the molecular mechanism of Aβ neurotoxicity has not thoroughly been elucidated. Therefore, we focused on a particular neuroprotective peptide, called NAP, a small fragment derived from ADNP (activity-dependent neurotrophic protein), which is essential for a proper function of the brain. Here, we report the synthesis of some new structural analogs of both Aβ(9-16) peptide fragment and NAP peptide, which were investigated in the presence of copper and iron ions. Thus, in the native Aβ(9-16) sequence 9GYEVHHQK16, tyrosine (Tyr, Y10) and histidine (His, H13,14) residues were replaced with glycine (Gly). In parallel, serine (Ser, S5) residue was modified with glycine (Gly, G5) or histidine (His, H5) in the sequence of neuroprotective NAP peptide (¹NAPVSIPQ8). All peptides were synthesized by Fmoc/tbutyl solid-phase synthesis (SPPS) and their structure as well as that of metal-peptide complexes was confirmed by MALDI-TOF mass spectrometry. Purification and separation of the peptides were performed by reverse phase high performance liquid chromatography (RP-HPLC). [ABSTRACT FROM AUTHOR]

Early detection of Alzheimer’s disease is important for deploying interventions to prevent or slow disease progression. We propose a multi-view dependence modeling framework that integrates multiple data sources to distinguish patients at different stages of the disease. We design interpretable models that can handle heterogeneous data types including neuroimages, bio- and clinical markers, and historical and genotypical characteristics of the subjects. We learn the dependence structure from data with guidance from domain knowledge in Bayesian Networks, visualizing and quantifying the conditional probabilistic dependence among the variables. Our results indicate that the hybrid dependence models also improve prediction performance. [ABSTRACT FROM AUTHOR]

With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. MRI scans are shown to be the most effective strategy to enable early diagnosis of dementia. Nevertheless, the early diagnosis of dementia is a challenging task due to the high dimensionality of MRI scans that may degrade the effectiveness of machine learning models. This paper proposes a novel method in diagnosis of Alzheimer disease by the analysis of MRI images of the brain, obtaining the white to black pixel ratio, Neighborhood gray tone difference matrix (NGTDM) and the volume of CSF. [ABSTRACT FROM AUTHOR]

Rob'Zheimer proposes an approach to improve communication skills of people with dementia. The program focuses on the people's active position and checks the participation effects. Rob'Zheimer is divided into 20 sessions of 1 hour each, alternating non-robotic and robotic sessions. During non-robotic sessions people register the voices reading story and create the scenery. During the robotic sessions, people program the robot gestures according to the registered voices. In this program, the robots are used as an extension, and not as a companion as traditionally performed in other research programs. Consequently, people use it as a tool in order to communicate with others showing improvements of their communication. This article review a theorical approach to set up a use of robots by older people with dementia and preliminary observations of Rob'Zheimer. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is associated with conformational changes of amyloid-β peptides, which result in peptide oligomerization and fibril formation and, finally, in the appearance of senile plaques and extensive neuronal loss. However, the factors regulating the process of plaque-associated beta-amyloid peptide (Aβ) aggregation are only partly understood. Therefore, several β-amyloid peptide fragments have been synthesized by SPPS according to Boc/Bzl strategy. Binding of metal ions to β-amyloid peptides was studied by electrospray ion trap mass spectrometry (ESI-MS) at pH 7.4 or 6.6. Copper ions bind strongly and specifically to β-amyloid(1-40) peptide and to its Nand C-terminal truncated versions. It was found that Aβ(1-10) peptide binds only one Cu(II) ion at pH 7.4. However, the intensity of peak corresponding to the doubly charged ion [M+Cu]2+ increases significantly with time and copper concentration. Nevertheless, the Aβ(1-16) peptide is able to bind one Cu(II) at 1:1 and 1:2 peptide-Cu ratio, and up to two Cu(II) at a peptide-Cu ratio of 1:10. Copper binding to Aβ peptides changed much the peptide conformation. During the binding of Cu(II) to N-terminal truncated β-amyloid peptides, no copper ion was found to bind to Aβ(31-40) at 1:1 and 1:2 peptide: Cu(II) ratios. At 1:10 peptide-metal ion ratio, a low intense peak corresponding to [M+Cu]2+ ion was detected by ESI-MS after 7-8 min incubation of peptide-metal ion. A close relationship between pH, metal concentration and the proportion of conformers of Aβ fragments was found, as well. Our ESI-MS investigation showed that the C-terminal 31-40 sequence is not involved in copper binding. Our results also prove that Zn(II) has lower affinity toward Aβ-peptides as compared to Cu(II). At 1:10 peptide-Zn(II), no peak corresponding to the Aβ-peptides was detected by ESI-MS, where an aggregation process was suspected. These findings are discussed in the light of recent literature on neurodegeneration biochemistry. [ABSTRACT FROM AUTHOR]

Reconstructability Analysis (RA) is an information- and graph-theory-based method which has been successfully used in previous genomic studies. Here we apply it to genetic (14 SNPs) and non-genetic (Education, Age, Gender) data on Alzheimer disease in a well-characterized Case/Control sample of 424 individuals. We confirm the importance of APOE as a predictor of the disease, and identify one non-genetic factor, Education, and two SNPs, one in BINI and the other in SORCS1, as likely disease predictors. SORCS1 appears to be a common risk factor for people with or without APOE. We also identify a possible interaction effect between Education and BINI. Methodologically, we introduce and use to advantage some more powerful features of RA not used in prior genomic studies. [ABSTRACT FROM PUBLISHER]

There are several dementias but Alzheimer's Disease (AD) is leading cause of dementia in the world. There are an estimated 35.6 million people in the world with dementia and more than 18 million of them have Alzheimer's disease, which supposes more of the 50% of the total people with dementia. In this paper, intelligent algorithms are designed and implemented in order to manipulate Magnetic Resonance Images (MRI) stored in a big data base of patients, with the main purpose of being able for decision-making in classification task. It will be possible to predict whether the patient is healthy, has Mild Cognitive Impairment (MCI), or already has Alzheimer Disease. We tested our method on data from a large data base (more than 1500 patient were analized), with a sensitivity of and specificity close to 90%which is considerable better the human experts, taking into account that in this paper also MCI is classified. It is noteworthy that with this last study we could offer a tool to assist the early diagnosis of dementia and has the potential to be a useful help for clinicians. [ABSTRACT FROM PUBLISHER]

Metal cations such as Cu2+ have been shown to induce amyloid aggregation and formation of reactive oxygen species. Elucidation of the structural features of Cu2+-Aß is thus, essential to understand their role in the aggregation of Aß, formation of ROS and to rationally design new chelators with potential therapeutic applications. Present contribution reviews our computational studies in this field. First, computational strategies used to determine three dimensional structures for Cu2+ Aß(l-16) and the redox properties of these complexes will be discussed and second, we will summarize our recent studies on Cu2+ chelators. [ABSTRACT FROM AUTHOR]

Alzheimer’s Disease (AD) is an illness that degenerates an individual’s cognitive functions, leaving them unable to take care of themselves. Even without a definitive cure, AD should be treated with remedies and cognitive enhancement. This article presents an application that assists in the cognitive reinforcement of AD patients through games, supports the medical follow-up of patients, and facilitates the daily exchange of information between the caregiver and the doctor. [ABSTRACT FROM AUTHOR]

Background & Aim: Caregiver burden refers to the presence of problems, difficulties which affect the life of caregivers of elders with Alzheimer's disease. The burden can affect caregivers' ability to provide quality care to the patients. It is necessary to investigate caregiver burden in order to design programs to ease it. Therefore, the aim of this study was to determine the female caregivers' burden of elders with Alzheimer Disease. Methods: A cross-sectional study recruited a convenience sample of 112 women as family care givers of patients with Alzheimer disease from the Iranian Alzheimer Association in Tehran, Iran. For data collection, a self report questionnaire was used. Data were analyzed using descriptive and inferential statistics. Results: The score of caregiver's burden was 34.12 ±1.21 that means the subjective burden level was high in the majority of the women. There was a significant correlation between the two variables of caregivers' burden and economic status (P=0.007). Discussion & Conclusion: Caring for a person with Alzheimer is a stressful experience, particularly in female caregivers with different roles. Our findings highlight the strong association of caregiver burden and the economic status. Financial help and extended social support should be considered in the women. Moreover, health professionals who treat either caregivers or people with Alzheimer Disease should be aware of the impact of economic status has on the female caregivers burden. [ABSTRACT FROM AUTHOR]

The present contribution analyzes the binding of ThT and neutral ThT derivatives to a ß-sheet model by means of quantum chemical calculations. In addition, we study the properties of four molecules: (2-(2- hydroxyphenyl)benzoxazole (HBX), 2-(2-hydroxyphenyl)benzothiazole (HBT) and their respective iodinated compounds, HBXI and HBTI, in binding to amyloid fibril models and Aß1-40fibrils by using a combination of docking, molecular dynamics and quantum mechanics calculations. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurological degenerative disorder that leads to progressive mental deterioration. This work introduces a computational approach to improve our understanding of the progression of AD. We use ensemble learning methods and deep neural networks to identify salient structural correlations among brain regions that degenerate together in AD; this provides an understanding of how AD progresses in the brain. The proposed technique has a classification accuracy of 81.79% for AD against healthy subjects using a single modality imaging dataset. [ABSTRACT FROM AUTHOR]