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1.
Artigo em Russo | MEDLINE | ID: mdl-33081441

RESUMO

OBJECTIVE: To study the relationship between cognitive deficits and retinal neuroarchitectonics in Alzheimer's disease, vascular dementia, and glaucoma based on optical coherence tomography. MATERIAL AND METHODS: A comprehensive examination of 90 patients with Alzheimer's disease, vascular dementia and glaucoma was conducted. The patients were divided into three groups of 30 people each. The groups were comparable by gender and age and initial socio-economic status. All patients underwent a comprehensive neurological and neuropsychological study as well as optical coherence tomography. RESULTS AND CONCLUSION: The results of optical coherence tomography in Alzheimer's disease and glaucoma reveal retinal changes in the perifocal region in the upper and lower quadrants. In patients with vascular dementia, the process is observed in the foveal (central) region of the retina, which can be considered as a potential biomarker of the neurodegenerative damage. The severity of cognitive deficit in the Alzheimer's disease group correlates with the degree of degeneration in the layers of the peripapillary layer of the nerve fibers of the retina of the temporal region, the perifocal region of the lower quadrant of the retina, ganglion cells, and the inner plexiform layers of the retina. In the vascular dementia group, the severity of cognitive deficit positively correlates with the degree of cell degeneration in the foveal region of the inner plexiform retinal layer.


Assuntos
Doença de Alzheimer , Disfunção Cognitiva , Doença de Alzheimer/complicações , Doença de Alzheimer/diagnóstico por imagem , Disfunção Cognitiva/diagnóstico por imagem , Humanos , Fibras Nervosas , Retina/diagnóstico por imagem , Tomografia de Coerência Óptica
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4226-4228, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018929

RESUMO

The purpose of this paper is to develop an inexpensive, wearable, and portable monitoring system with wireless capabilities for signal acquisition of the user's surrounding soundscape and electroencephalography (EEG). The end-goal of this device is to monitor high-risk populations that are developing into earlier stages of Alzheimer's Disease (AD). Currently, the development of such device is still within preliminary phase and has only been tested in healthy individuals. Future applications of our monitoring system may be used as a non-invasive and inexpensive diagnostic tool for early detection of AD, potentially paving a new platform for therapeutic intervention. The system consists of low-weight bearing components, including an analog front-end and a single-board computer. The analog front-end contains three independent EEG, reference, bias, and auditory recording channels. The single-board computer timestamps and encrypts the incoming channels prior to local or "cloud" storage. Cloud storage provides ease-of-access and offline data analysis without the need to physically extract the data from the monitoring system. A portable/rechargeable battery provides power to the entire monitoring system for over 4 hours of operation. A graphical user-interface (GUI) was developed for secured remote access to data, parameter settings, and system configurations. The performance of the system was tested by measuring the frequency following response (FFR) in the captured EEG signals with respect to periodic auditory stimuli.


Assuntos
Doença de Alzheimer , Doença de Alzheimer/diagnóstico , Eletrocardiografia , Eletroencefalografia , Desenho de Equipamento , Humanos , Monitorização Fisiológica
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5432-5435, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019209

RESUMO

Early detection of Alzheimer's Disease (AD) is critical in creating better outcomes for patients. Performance in complex tasks such as vehicular driving may be a sensitive tool for early detection of AD and serve as a good indicator of functional status. In this study, we investigate the classification of AD patients and controls using driving simulator data. Our results show that machine learning algorithms, especially random forest classifier, can accurately discriminate AD patients and controls (AUC = 0.96, Sensitivity = 87%, and Specificity = 93%). The model-identified most important features include Pothole Avoidance, Road Signs Recalled, Inattention Measurements, Reaction Time, and Detection Times, among others, all of which closely align with previous studies about cognitive functions that are affected by AD.


Assuntos
Doença de Alzheimer , Condução de Veículo , Doença de Alzheimer/diagnóstico , Cognição , Humanos , Aprendizado de Máquina , Tempo de Reação
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5486-5489, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019221

RESUMO

The ability to accurately detect onset of dementia is important in the treatment of the disease. Clinically, the diagnosis of Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) patients are based on an integrated assessment of psychological tests and brain imaging such as positron emission tomography (PET) and anatomical magnetic resonance imaging (MRI). In this work using two different datasets, we propose a behavior score-embedded encoder network (BSEN) that integrates regularly adminstrated psychological tests information into the encoding procedure of representing subject's resting-state fMRI data for automatic classification tasks. BSEN is based on a 3D convolutional autoencoder structure with contrastive loss jointly optimized using behavior scores from Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). Our proposed classification framework of using BSEN achieved an overall recognition accuracy of 59.44% (3-class classification: AD, MCI and Healthy Control), and we further extracted the most discriminative regions between healthy control (HC) and AD patients.


Assuntos
Doença de Alzheimer , Disfunção Cognitiva , Doença de Alzheimer/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Disfunção Cognitiva/diagnóstico por imagem , Humanos , Imagem por Ressonância Magnética , Tomografia Computadorizada por Raios X
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5523-5526, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019230

RESUMO

Early detection of Alzheimer's disease (AD) is of vital importance in the development of disease-modifying therapies. This necessitates the use of early pathological indicators of the disease such as amyloid abnormality to identify individuals at early disease stages where intervention is likely to be most effective. Recent evidence suggests that cerebrospinal fluid (CSF) amyloid ß1-42 (Aß42) level may indicate AD risk earlier compared to amyloid positron emission tomography (PET). However, the method of collecting CSF is invasive. Blood-based biomarkers indicative of CSF Aß42 status may remedy this limitation as blood collection is minimally invasive and inexpensive. In this study, we show that APOE4 genotype and blood markers comprising EOT3, APOC1, CGA, and Aß42 robustly predict CSF Aß42 with high classification performance (0.84 AUC, 0.82 sensitivity, 0.62 specificity, 0.81 PPV and 0.64 NPV) using machine learning approach. Due to the method employed in the biomarker search, the identified biomarker signature maintained high performance in more than a single machine learning algorithm, indicating potential to generalize well. A minimally invasive and cost-effective solution to detecting amyloid abnormality such as proposed in this study may be used as a first step in a multi-stage diagnostic workup to facilitate enrichment of clinical trials and population-based screening.


Assuntos
Doença de Alzheimer , Peptídeos beta-Amiloides , Doença de Alzheimer/diagnóstico , Amiloide , Apolipoproteína E4 , Humanos , Tomografia Computadorizada por Raios X
7.
Artigo em Inglês | MEDLINE | ID: mdl-33017923

RESUMO

This study had two main objectives: (i) to study the effects of volume conduction on different connectivity metrics (Amplitude Envelope Correlation AEC, Phase Lag Index PLI, and Magnitude Squared Coherence MSCOH), comparing the coupling patterns at electrode- and sensor-level; and (ii) to characterize spontaneous EEG activity during different stages of Alzheimer's disease (AD) continuum by means of three complementary network parameters: node degree (k), characteristic path length (L), and clustering coefficient (C). Our results revealed that PLI and AEC are weakly influenced by volume conduction compared to MSCOH, but they are not immune to it. Furthermore, network parameters obtained from PLI showed that AD continuum is characterized by an increase in L and C in low frequency bands, suggesting lower integration and higher segregation as the disease progresses. These network changes reflect the abnormalities during AD continuum and are mainly due to neuronal alterations, because PLI is slightly affected by volume conduction effects.


Assuntos
Doença de Alzheimer , Benchmarking , Encéfalo , Eletroencefalografia , Humanos , Rede Nervosa
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 256-259, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017977

RESUMO

In recent years, electroencephalography (EEG) has emerged as a low-cost, accessible and objective tools for the early diagnosis of Alzheimer's disease (AD). AD is preceded by Mild Cognitive Impairment (MCI), typically refers to early-stage AD disease. The purpose of this study is to classify MCI patients from the multi-domain features of their electroencephalography (EEG). Firstly, we extracted the multi-domain (time, frequency and information theory) features from resting-state EEG signals before and after a cognitive task from 15 MCI groups and 15 age-matched healthy controls. Then, principal component analysis (PCA) was used to perform feature selection. After that, we compared the performance between SVM and KNN on our EEG dataset. The good performance was observed both from SVM and KNN, which demonstrates the effectiveness of multi-domain features. Furthermore, KNN performs better than SVM and the EEG signals after the cognitive task works better than those before the task.


Assuntos
Doença de Alzheimer , Disfunção Cognitiva , Doença de Alzheimer/diagnóstico , Disfunção Cognitiva/diagnóstico , Eletroencefalografia , Humanos
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1084-1087, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018174

RESUMO

Recently, more evidences manifest that the subjective cognitive decline (SCD) of unimpaired individual may represent first symptom of Alzheimer's disease (AD). This study investigated the differences of intrinsic glucose metabolic functional connectivity between SCD and healthy subject (HC) groups from the perspective of brain network topology. In this study we attained 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans from Xuanwu Hospital, Beijing, China, including 85 SCD subjects (male = 16, mean age = 66, MMSE = 28.4) and 74 HC subjects (male = 37, mean age = 65,MMSE=29.0). Graph theory method has been used in this study. Network parameters, including global efficiency, local efficiency, characteristic path length, clustering coefficient, betweenness centrality, sigma and modularity were calculated and compared between two groups. As a result, both SCD and HC groups showed the small-world property. Meanwhile, SCD showed loss of small-world properties, for example, sigma in SCD was significantly lower than HC (p<0.05). In addition, the clustering coefficient and local efficiency of SCD were both higher than HC significantly (p<0.05). In contrast, the characteristic path length and global efficiency of SCD were lower than HC, which led to the regularization of brain network in SCD group. Furthermore, we found global modularity of SCD was lower than HC and the number of modules also decreased. Our findings suggested that there exist differences in glucose metabolic brain network between two groups, demonstrating that the graph theory analysis method could be useful and helpful to predict risks in the preclinical stage of AD.


Assuntos
Doença de Alzheimer , Disfunção Cognitiva , Encéfalo/diagnóstico por imagem , China , Glucose , Humanos
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1100-1103, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018178

RESUMO

Alzheimer's disease (AD) is a degenerative brain disease and the most common cause of dementia. Early stage ß-amyloid oligomers (AßOs) and late stage Aß plaques are the pathological hallmarks of AD brains. AßOs are known to be more neurotoxic and contribute to neuronal damage. Most current approaches are focused on detecting Aß plaques, which occurs at the late stage of AD, and are limited by poor sensitivity and/or contrast agent toxicity. In previous studies, we developed a new curcumin-conjugated magnetic nanoparticle (Cur-MNPs) to target the Aß pathologies. In this study, we investigate the in vivo feasibility of this novel Cur-MNPs to detect Aß pathologies at the early and late stages of AD in transgenic AD mice and perform immunohistochemical examinations to validate the specific targeting of various form of Aß pathologies.


Assuntos
Doença de Alzheimer , Doença de Alzheimer/diagnóstico , Peptídeos beta-Amiloides , Animais , Diagnóstico Precoce , Imagem por Ressonância Magnética , Camundongos , Placa Amiloide/diagnóstico por imagem
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1104-1107, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018179

RESUMO

Alzheimer's disease (AD) is progressive neurodegenerative disease. It is important to identify effective biomarkers to explore changes of complex functional brain networks in AD patients based on functional magnetic resonance imaging (fMRI). Recently, four fMRI brain network parameters were frequently used, including regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (f/ALFF) and degree centrality (DC). However, these parameters only present the changes of brain networks in a full time quantum, but ignore changes over a short period of time and lack space information. In this study we propose a new brain network parameter for fMRI, called multilayer network modularity and spatiotemporal network switching rate (stNSR). This parameter is calculated combing Pearson correlation sliding Hamming window and the Louvain algorithm. To verify the efficiency of stNSR, we selected 61 AD patients and 110 healthy controls (HC) from Xuanwu Hospital, Beijing, China. First, we used two-sample t test to identify regions of interest (ROI) between AD patients and HCs. Second, we calculated the stNSR values in these ROIs, and compared them with ReHo, ALFF, f/ALFF, and DC values between AD and HC groups. The results showed that, stNSR values in left calcimine fissure and surrounding cortex, left Lingual gyrus and left cerebellum inferior significantly increased, while stNSR values significantly decreased in left Para hippocampal gyrus, left temporal and superior temporal gyrus. As a comparison, changes in these ROIs could not be observed using ReHo, ALFF, f/ALFF, and DC. The results indicated that stNSR may reflect differences of brain networks between AD patients and HCs.


Assuntos
Doença de Alzheimer , Doenças Neurodegenerativas , Doença de Alzheimer/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , China , Humanos
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1420-1423, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018256

RESUMO

Alzheimers disease is characterized by complex changes in brain tissue including the accumulation of tau-containing neurofibrillary tangles (NFTs) and dystrophic neurites (DNs) within neurons. The distribution and density of tau pathology throughout the brain is evaluated at autopsy as one component of Alzheimers disease diagnosis. Deep neural networks (DNN) have been shown to be effective in the quantification of tau pathology when trained on fully annotated images. In this paper, we examine the effectiveness of three DNNs for the segmentation of tau pathology when trained on noisily labeled data. We train FCN, SegNet and U-Net on the same set of training images. Our results show that using noisily labeled data, these networks are capable of segmenting tau pathology as well as nuclei in as few as 40 training epochs with varying degrees of success. SegNet, FCN and U-Net are able to achieve a DICE loss of 0.234, 0.297 and 0.272 respectively on the task of segmenting regions of tau. We also apply these networks to the task of segmenting whole slide images of tissue sections and discuss their practical applicability for processing gigapixel sized images.


Assuntos
Doença de Alzheimer , Redes Neurais de Computação , Doença de Alzheimer/diagnóstico , Encéfalo/diagnóstico por imagem , Humanos , Emaranhados Neurofibrilares
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1762-1765, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018339

RESUMO

Subjective cognitive decline (SCD) is a high-risk preclinical stage in the progress of Alzheimer's disease (AD). Its timely diagnosis is of great significance for older adults. Though multi-parameter magnetic resonance imaging (MPMRI) is a noninvasive neuroimaging technique to detect SCD, the lack of biomarkers and computed aided diagnosis (CAD) tools is a major concern for its application. Radiomics, a high-dimensional imaging feature extraction method, has been widely used for identifying biomarkers and developing CAD tools in oncological studies. Therefore, in this study, we aimed to investigate whether the radiomic approach could be used for the diagnosis of SCD. In the proposed radiomic approach, we mainly performed four steps: image preprocessing, feature extraction and screening, and classification. The dataset from Xuanwu Hospital, Beijing, China, was used in this study, including 105 healthy controls (HC) and 130 SCD subjects. All subjects were divided into one training & validation group and one test group. We extracted 30128 radiomic features from MPMRI of each subject. The t-test, autocorrelation, and Fisher score were performed for feature selection, and we deployed the support vector machine (SVM) for classification. The above process was performed 100 times with 5-fold cross-validation. The results showed that the accuracy, sensitivity, and specificity of classification was 89.03%±5.37%, 85.44%±9.28% and 91.97%±6.38% in the validation set and 84.70%±4.68%, 86.98%±10.49% and 82.59%±7.07% in the test set. In conclusion, this study has shown that the radiomic approach could be used to discriminate SCD and HC with high accuracy and sensitivity effectively.


Assuntos
Doença de Alzheimer , Disfunção Cognitiva , Idoso , Doença de Alzheimer/diagnóstico , China , Disfunção Cognitiva/diagnóstico , Humanos , Imagem por Ressonância Magnética , Máquina de Vetores de Suporte
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3204-3207, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018686

RESUMO

Alzheimer's disease (AD) affects approximately 30 million people worldwide, and this number is predicted to triple by 2050 unless further discoveries facilitate the early detection and prevention of the disease. Computerized walkways for simultaneous assessment of motor-cognitive performance, known as a dual-task assessment, has been used to associate changes in gait characteristics to mild cognitive impairment (MCI) with early-stage disease. However, to our best knowledge, there is no validated method to detect MCI using the collective analysis of these gait characteristics. In this paper, we develop a machine learning approach to analyze the gait data from the dual-task assessment in order to detect subjects with cognitive impairment from healthy individuals. We collected dual-task gait data from a computerized walkway of a total of 92 subjects with 31 healthy control (HC) and 61 MCI. Using support vector machine (SVM) and gradient tree boosting, we developed a classifier to differentiate MCI from HC subjects and compared the results with a paper-based questionnaire assessment that has been commonly used in clinical practice. SVM provided the highest accuracy of 77.17% with 81.97% sensitivity and 67.74% specificity. Our results indicate the potential of machine learning + dual-task assessment to enable early diagnosis of cognitive decline before it advances to dementia and AD, so that early intervention or prevention strategies can be initiated.


Assuntos
Doença de Alzheimer , Disfunção Cognitiva , Doença de Alzheimer/diagnóstico , Disfunção Cognitiva/diagnóstico , Diagnóstico Precoce , Marcha , Humanos , Aprendizado de Máquina
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3557-3560, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018771

RESUMO

Anodal transcranial direct current stimulation (AtDCS) can improve memory and cognitive dysfunction in patients with Alzheimer's disease (AD), which has been proven in basic and clinical studies. Intervention of AD in preclinical stage is important to prevent progression of AD in the aging society. At the same time, there is increasing evidence that a close link exists between cerebrovascular dysfunction and AD disease. Here we investigated the changes of local cerebral blood microcirculation in preclinical AD mouse model after AtDCS based on the previous studies. Twenty-four 6-month-old male APP/PS1 double transgenic mice were randomly divided into three groups: a model group (AD), a model sham stimulation (ADST) group and a model stimulation group (ATD). Eight 6-month-old male C57 wild-type mice served as a control group (CTL). Mice in the ATD group received 10 AtDCS sessions. Two months after the end of AtDCS in the ATD group, the microcirculation parameters of the frontal cortex of the mice in each group, including cerebral blood flow (CBF), blood flow velocity (Velo), oxygen saturation (SO2) and relative hemoglobin content (rHb), were obtained by the non-invasive laser-Doppler spectrophotometry system "Oxygen-to-See (O2C)". The results showed that AtDCS increased CBF, Velo and SO2, and reduce rHb in APP/PS1 double transgenic mice at the preclinical stage of AD.Clinical Relevance-This shows the positive effect of AtDCS on preclinical AD in cerebrovascular function, and provides effective basic research facts for AtDCS to intervene and delay the clinical application of AD disease.


Assuntos
Doença de Alzheimer , Estimulação Transcraniana por Corrente Contínua , Doença de Alzheimer/genética , Precursor de Proteína beta-Amiloide/genética , Animais , Criança , Modelos Animais de Doenças , Humanos , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Transgênicos , Microcirculação , Dados Preliminares , Presenilina-1/genética
16.
Hipertens. riesgo vasc ; 37(3): 125-132, jul.-sept. 2020. graf, tab
Artigo em Espanhol | IBECS | ID: ibc-193521

RESUMO

La hipertensión arterial es considerada el principal factor de riesgo vascular modificable que causa daño en forma silente en los vasos del cerebro. Esta injuria vascular cerebral podría ser el núcleo común que justifique los síntomas cognitivos (deterioro cognitivo, demencia y enfermedad de Alzheimer) y conductuales (depresión de inicio tardío) del daño de órgano blanco mediado por la hipertensión arterial. El conocimiento incompleto sobre los complejos vínculos fisiopatológicos que relacionan la hipertensión arterial con los cambios cognitivo-conductuales soslaya la participación del cerebro como órgano blanco subestimando el riesgo cardio y cerebrovascular. La convergencia de deterioro cognitivo, depresión e hipertensión arterial en adultos mayores, advierte sobre la necesidad de una evaluación integral que permita planificar el tratamiento, mejorar pronóstico y contribuir a la disminución del riesgo de demencia y su incidencia


Arterial hypertension is considered the main modifiable vascular risk factor that causes silent damage to brain vessels. This vascular brain injury could be the common nucleus that justifies the cognitive (cognitive impairment, dementia and Alzheimer's disease) and behavioural symptoms (late-life depression) of target organ damage mediated-hypertension. Incomplete knowledge about the complex pathophysiology that links hypertension with cognitive-behavioural changes is overlooking brain involvement and underestimating cardio and cerebrovascular risk. The confluence of cognitive impairment, depression and arterial hypertension in elderly adults, warns of the need for a comprehensive evaluation to plan treatment, improve prognosis and contribute to reducing the risk of dementia and its incidence


Assuntos
Humanos , Hipertensão/fisiopatologia , Hipertensão/psicologia , Transtornos Cognitivos/etiologia , Transtornos Cognitivos/psicologia , Disfunção Cognitiva/prevenção & controle , Transtornos de Início Tardio/fisiopatologia , Doença de Alzheimer/etiologia , Fatores de Risco
18.
BMC Bioinformatics ; 21(1): 377, 2020 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-32883200

RESUMO

BACKGROUND: A large number of experimental studies show that the mutation and regulation of long non-coding RNAs (lncRNAs) are associated with various human diseases. Accurate prediction of lncRNA-disease associations can provide a new perspective for the diagnosis and treatment of diseases. The main function of many lncRNAs is still unclear and using traditional experiments to detect lncRNA-disease associations is time-consuming. RESULTS: In this paper, we develop a novel and effective method for the prediction of lncRNA-disease associations using network feature similarity and gradient boosting (LDNFSGB). In LDNFSGB, we first construct a comprehensive feature vector to effectively extract the global and local information of lncRNAs and diseases through considering the disease semantic similarity (DISSS), the lncRNA function similarity (LNCFS), the lncRNA Gaussian interaction profile kernel similarity (LNCGS), the disease Gaussian interaction profile kernel similarity (DISGS), and the lncRNA-disease interaction (LNCDIS). Particularly, two methods are used to calculate the DISSS (LNCFS) for considering the local and global information of disease semantics (lncRNA functions) respectively. An autoencoder is then used to reduce the dimensionality of the feature vector to obtain the optimal feature parameter from the original feature set. Furthermore, we employ the gradient boosting algorithm to obtain the lncRNA-disease association prediction. CONCLUSIONS: In this study, hold-out, leave-one-out cross-validation, and ten-fold cross-validation methods are implemented on three publicly available datasets to evaluate the performance of LDNFSGB. Extensive experiments show that LDNFSGB dramatically outperforms other state-of-the-art methods. The case studies on six diseases, including cancers and non-cancers, further demonstrate the effectiveness of our method in real-world applications.


Assuntos
Algoritmos , Neoplasias/patologia , RNA Longo não Codificante/metabolismo , Doença de Alzheimer/genética , Doença de Alzheimer/patologia , Área Sob a Curva , Insuficiência Cardíaca/genética , Insuficiência Cardíaca/patologia , Humanos , Neoplasias/genética , RNA Longo não Codificante/genética , Curva ROC
19.
Nat Commun ; 11(1): 4413, 2020 09 04.
Artigo em Inglês | MEDLINE | ID: mdl-32887883

RESUMO

The molecular signatures of cells in the brain have been revealed in unprecedented detail, yet the ageing-associated genome-wide expression changes that may contribute to neurovascular dysfunction in neurodegenerative diseases remain elusive. Here, we report zonation-dependent transcriptomic changes in aged mouse brain endothelial cells (ECs), which prominently implicate altered immune/cytokine signaling in ECs of all vascular segments, and functional changes impacting the blood-brain barrier (BBB) and glucose/energy metabolism especially in capillary ECs (capECs). An overrepresentation of Alzheimer disease (AD) GWAS genes is evident among the human orthologs of the differentially expressed genes of aged capECs, while comparative analysis revealed a subset of concordantly downregulated, functionally important genes in human AD brains. Treatment with exenatide, a glucagon-like peptide-1 receptor agonist, strongly reverses aged mouse brain EC transcriptomic changes and BBB leakage, with associated attenuation of microglial priming. We thus revealed transcriptomic alterations underlying brain EC ageing that are complex yet pharmacologically reversible.


Assuntos
Envelhecimento/patologia , Barreira Hematoencefálica , Encéfalo/fisiopatologia , Células Endoteliais/metabolismo , Exenatida/farmacologia , Doença de Alzheimer/fisiopatologia , Animais , Barreira Hematoencefálica/efeitos dos fármacos , Barreira Hematoencefálica/fisiopatologia , Capilares/metabolismo , Células Cultivadas , Humanos , Camundongos , Microglia/efeitos dos fármacos , Doenças Neurodegenerativas/fisiopatologia , Transcriptoma/efeitos dos fármacos
20.
Nan Fang Yi Ke Da Xue Xue Bao ; 40(4): 531-537, 2020 Apr 30.
Artigo em Chinês | MEDLINE | ID: mdl-32895137

RESUMO

OBJECTIVE: To propose a coupled convolutional and graph convolutional network (CCGCN) model for diagnosis of Alzheimer's disease (AD) and its prodromal stage. METHODS: The disease-related brain regions generated by group-wise comparison were used as the input. The convolutional neural networks (CNNs) were used to extract disease-related features from different locations on brain magnetic resonance (MR) images. The generated features via the graph convolutional network (GCN) were processed, and graph pooling was performed to analyze the inherent relationship between the brain topology and the diagnosis task adaptively. Through ADNI dataset, we acquired the accuracy, sensitivity and specificity of the diagnosis tasks for AD and its prodromal stages, followed by an ablation study on the model structure. RESULTS: The CCGCN model outperformed the current state-of-the-art methods and showed a classification accuracy of 92.5% for AD with a sensitivity of 88.1% and a specificity of 96.0%. CONCLUSIONS: Based on the structural and topological features of the brain MR images, the proposed CCGCN model shows excellent performance in AD diagnosis and is expected to provide important assistance to physicians in disease diagnosis.


Assuntos
Doença de Alzheimer , Doença de Alzheimer/diagnóstico por imagem , Encéfalo , Humanos , Imagem por Ressonância Magnética , Redes Neurais de Computação
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