Narrow Results

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt–Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer’s Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

Aim of this study was to explore the EEG functional connectivity in amnesic mild cognitive impairments (MCI) subjects with multidomain impairment in order to characterize the Default Mode Network (DMN) in converted MCI (cMCI), which converted to Alzheimer’s disease (AD), compared to stable MCI (sMCI) subjects. A total of 59 MCI subjects were recruited and divided -after appropriate follow-up- into cMCI or sMCI. They were further divided in MCI with linguistic domain (LD) impairment and in MCI with executive domain (ED) impairment. Small World (SW) index was measured as index of balance between integration and segregation brain processes. SW, computed restricting to nodes of DMN regions for all frequency bands, evaluated how they differ between MCI subgroups assessed through clinical and neuropsychological four-years follow-up. In addition, SW evaluated how this pattern differs between MCI with LD and MCI with ED. Results showed that SW index significantly decreased in gamma band in cMCI compared to sMCI. In cMCI with LD impairment, the SW index significantly decreased in delta band, while in cMCI with ED impairment the SW index decreased in delta and gamma bands and increased in alpha1 band. We propose that the DMN functional alterations in cognitive impairment could reflect an abnormal flow of brain information processing during resting state possibly associated to a status of pre-dementia.

Alzheimer’s Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

Dementia is one of the most common neurological disorders causing defection of cognitive functions, and seriously affects the quality of life. In this study, various methods have been proposed for the detection and follow-up of Alzheimer’s dementia (AD) with advanced signal processing methods by using electroencephalography (EEG) signals. Signal decomposition-based approaches such as empirical mode decomposition (EMD), ensemble EMD (EEMD), and discrete wavelet transform (DWT) are presented to classify EEG segments of control subjects (CSs) and AD patients. Intrinsic mode functions (IMFs) are obtained from the signals using the EMD and EEMD methods, and the IMFs showing the most significant differences between the two groups are selected by applying previously suggested selection procedures. Five-time-domain and 5-spectral-domain features are calculated using selected IMFs, and five detail and approximation coefficients of DWT. Signal decomposition processes are conducted for both 1 min and 5 s EEG segment durations. For the 1 min segment duration, all the proposed approaches yield prominent classification performances. While the highest classification accuracies are obtained using EMD (91.8%) and EEMD (94.1%) approaches from the temporal/right brain cluster, the highest classification accuracy for the DWT (95.2%) approach is obtained from the temporal/left brain cluster for 1 min segment duration.

Computer-aided diagnosis of health problems and pathological conditions has become a substantial part of medical, biomedical, and computer science research. This paper focuses on the diagnosis of early and progressive dementia, building on the potential of deep learning (DL) models. The proposed computational framework exploits a magnetic resonance imaging (MRI) brain asymmetry biomarker, which has been associated with early dementia, and employs DL architectures for MRI image classification. Identification of early dementia is accomplished by an eight-layered convolutional neural network (CNN) as well as transfer learning of pretrained CNNs from ImageNet. Different instantiations of the proposed CNN architecture are tested. These are equipped with Softmax, support vector machine (SVM), linear discriminant (LD), or $k$ -nearest neighbor (KNN) classification layers, assembled as a separate classification module, which are attached to the core CNN architecture. The initial imaging data were obtained from the MRI directory of the Alzheimer’s disease neuroimaging initiative 3 (ADNI3) database. The independent testing dataset was created using image preprocessing and segmentation algorithms applied to unseen patients’ imaging data. The proposed approach demonstrates a 90.12% accuracy in distinguishing patients who are cognitively normal subjects from those who have Alzheimer’s disease (AD), and an 86.40% accuracy in detecting early mild cognitive impairment (EMCI).

To investigate the cause of Alzheimer’s disease (senile dementia of Alzheimer’s disease type), we examined aluminium (Al) in the brain (hippocampus) of patients with Alzheimer’s disease using heavy ion (5 MeV Si³⁺) microprobe particle-induced X-ray emission (PIXE) analysis. Heavy ion microprobes (3 MeV Si²⁺) have several times higher sensitivity for Al detection than 2 MeV proton microprobes. We also examined Al in the brain of these patients by energy dispersive X-ray spectroscopy (EDX). (1) Al was detected in the cell nuclei isolated from the brain of patients with Alzheimer’s disease using 5 MeV Si³⁺ microprobe PIXE analysis, and EDX analysis. (2) EDX analysis demonstrated high levels of Al in the nucleolus of nerve cells in frozen sections prepared from the brain of these patients. Our results support the theory that Alzheimer’s disease is caused by accumulation of Al in the nuclei of brain cells.

Yukmijihwang-tang (YMJ), also known as Luweidihuang-tang in China, has been widely used as a general herbal tonic for hundreds of years in many Asian countries. This study examines whether YMJ derivatives (YMJd) enhance cognitive ability in normal human subjects and discusses its potential as treatment for dementia patients with deficient cognitive ability. Subjects were divided into two groups, the placebo-treated group (n=15) and the YMJd-treated group (n=20). K-WAIS tests, a Korean version of an individual intelligence quotient (IQ) test, and a P300 latency assessment of event-related potential (ERP) were conducted in order to measure changes in cognitive ability before and after 6 weeks of YMJd treatment. The K-WAIS mean scores of the group treated with YMJd were significantly higher than those of the placebo group (p<0.05), and their mean P300 latency was substantially shorter (p<0.005). These results suggest that YMJd treatment accelerates the speed of information processing and enhances cognitive ability. YMJd treatment may help dementia patients or the elderly recover from cognition deficiencies or degeneration in clinic.

The pathogenesis of Alzheimer’s disease (AD), a degenerative disease of the central nervous system, remains unclear. The main manifestations of AD include cognitive and behavioral disorders, neuropsychiatric symptoms, neuroinflammation, amyloid plaques, and neurofibrillary tangles. However, current drugs for AD once the dementia stage has been reached only treat symptoms and do not delay progression, and the research and development of targeted drugs for AD have reached a bottleneck. Thus, other treatment options are needed. Bioactive ingredients derived from plants are promising therapeutic agents. Specifically, Ginkgo biloba (Gb) extracts exert anti-oxidant, anticancer, neuroplastic, neurotransmitter-modulating, blood fluidity, and anti-inflammatory effects, offering alternative options in the treatment of cardiovascular, metabolic, and neurodegenerative diseases. The main chemical components of Gb include flavonoids, terpene lactones, proanthocyanidins, organic acids, polysaccharides, and amino acids. Gb and its extracts have shown remarkable therapeutic effects on various neurodegenerative diseases, including AD, with few adverse reactions. Thus, high-quality Gb extracts are a well-established treatment option for AD. In this review, we summarize the insights derived from traditional Chinese medicine, experimental models, and emerging clinical trials on the role of Gb and its chemical components in the treatment of the main clinical manifestations of AD.

Pre-Germinated Brown Rice May Possibly Benefit Alzheimer’s Patients.

Genetically Engineered Plants for Preventing Diabetes.

Gene Therapy — A Novel Treatment for Alzheimer’s.

Potential New Treatment for Stroke and Neurodegenerative Diseases.

Institute of Biological Chemistry researchers find new protein-DNA interaction.

Medicago to receive up to US$12 million.

INEX inks partnership with BGI for new non-invasive prenatal test.

China emerging as 'major player' for pharmaceuticals.

Possible cure for dementia found by Taiwan researchers.

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Biologists at Academia Sinica develops a new molecular strategy to suppress Hepatitis C virus replication.

Molecular cloning, characterization, and expression analysis of genes encoding Gibberellin 20-Oxidase in Dasypyrumvillosum dwarf mutant.

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SINGAPORE – IBN engineered artificial human livers for drug testing and discovery.

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UNITED KINGDOM – DaTSCAN™ SPECT imaging demonstrates impact on the diagnosis of patients with clinically uncertain dementia.

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Diabetes and the Elderly

The Necessity for Early Therapeutic Interventions in Dementia: The Role of Neurosteroids

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INDIA – Polio shots better than oral drops, says study.

JAPAN – Natural protein in rice helps keep arsenic out in grains.

THE PHILIPPINES – Philippines adopts inactivated polio vaccine strategy.

SINGAPORE – A*STAR's Institute of Microelectronics and Singapore Biomicro to develop a first-of-its-kind, implantable wireless blood glucose monitoring sensor device.

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SINGAPORE – VIVA Foundation and NUS Launches $10 Million Cancer Research Centre for Childhood Leukaemia.

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This paper reviews missing person incidents that occurred in older persons with dementia reported in local newspapers from 1 January 1999 to 1 May 2002. Ten relevant incidents, two of which happened to the same person, were found in WiseNews, an electronic database of 21 local newspapers. There were four (44%) males and five (56%) females with a mean age of 77 (standard deviation=5). In six (60%) cases, the missing persons eloped from home and four (40%) outside of home. Nine (90%) of the cases required less than a day to three days to locate the missing person. Six (60%) cases resulted in injuries or death due to falls or traffic accidents. It is not uncommon for dementia patients to get lost. Public awareness and understanding of the phenomenon is very important because the survival of missing persons depends on the implementation of timely and effective search and rescue. This analysis confirms the need for a prospective study to further examine the characteristics of missing older adults and missing incidents, as well as the search strategies adopted by caregivers.

这篇文章旨在回顾由一九九九年一月一日至二零零二年五月一日以来在本地报章上报导过的有关老年痴呆症患者走失的个案。WiseNews是一个包含二十一份本地报章的电子资料库。我们通过WiseNews搜索到十宗有关的报导，其中两宗个案发生在同一个患者身上。走失患者当中有四名男性，五名女性，年龄由七十到八十六岁［平均数：七十七］。在六宗［六成］走失个案当中，患者是从居所走失的，而另外四宗［四成］则发生在居所以外的地方。九宗［九成］个案需要少於一天至三天的时间去寻回走失患者。六宗［六成］个案涉及受伤或死亡。走失现象在老年痴呆症患者当中颇为普遍，走失患者的安危取决於及时和有效率的搜索及拯救策略。因此，大众需要对这现象有更多的了解。本文的分析显示本地需要对此问题作前瞻性的调查，使本地社会及健康服务从业员能掌握走失痴呆症患者的特徵，一般走失的情况及照顾者面对这问题的策略，从而提供合宜的协助。

Alzheimer’s disease (AD) is a disease that gradually develops and causes degeneration of the cells of the brain. The leading cause of AD is dementia that results in a person’s inability to work independently. In the early stages of AD, a person forgets recent conversations or the occurrence of an event. In the later stages, there could be severe loss of memory such that the person is not able to even perform everyday tasks. The medicines currently available for AD may improve its symptoms on a temporary basis in the early stage of the disease. Since no treatment is available for curing AD, its detection becomes extremely important. As the clinical treatments are very expensive, the need for automated diagnosis of AD is of critical importance. In this paper, a deep learning model based on a convolutional neural network has been used and applied to four classes of images of AD that is very mild demented, mild demented, average demented, and non-demented. It was found that the moderate demented class had the highest accuracy of 98.9%, a classification error rate of 0.01, and a specificity of 0.992. Also, the lowest false positive rate of 0.007 was obtained.

Alzheimer’s disease (AD) predominantly affects the elderly population with symptoms including, but not limited to, cognitive impairment and memory loss. Predicting AD and mild cognitive impairment (MCI) can lengthen the lifespan of patients and help them to access necessary medical resources. One potential approach to achieve an early diagnosis of AD is to use data mining techniques which explore various characteristic traits related to MCI, cognitively normal (CN), and AD subjects to build classifiers that reveal important contributors to the disease. These classifiers are used by physicians during the AD diagnostic process in a clinical evaluation. In this research, we compare between different data mining algorithms through empirical data approach to deal with the AD diagnosis. Experimental evaluation, using attribute selection methods, and classifiers from rule induction and other classification techniques have been conducted on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-MERGE). The results illustrate the good classification performance of classifiers with rules in predicting AD.