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Alzheimer's Disease


AI detects protein signatures for Alzeihmer's disease in the blood - Advanced Science News

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Nanoparticles could make a reliable blood test for Alzheimer's disease a reality; image credit: National Cancer Institute, Daniel Sone Using nanoparticles with different surface properties, researchers are able to detect subtle changes in the composition of proteins in the plasma years before the presentation of clinical symptoms of Alzheimer's disease, which include memory loss, confusion, and cognitive difficulties. Owing to the unique properties of nanoparticles, different proteins in biological fluids selectively stick onto their surface forming a protein corona, which was found to change during disease. Researchers from the United States and Italy identify these subtle changes in plasma protein patterns to distinguish plasma samples from healthy individuals and those diagnosed with Alzheimer's disease. "Protein corona composition is both influenced by specific health conditions as well as the chemical and physical properties of the nanoparticles themselves," says Dr. Claudia Corbo of the University of Milano-Bicocca and lead author of the study published in Advanced Healthcare Materials. "Binding of proteins to the surface of particles is very precise and dependent on the chemistry and shape of the particles and the chemistry and structure of the proteins," says senior author Professor Omid Farokhzad of Brigham and Women's Hospital and Harvard Medical School.


Autosomal dominant VCP hypomorph mutation impairs disaggregation of PHF-tau

Science

Neurodegeneration in Alzheimer's disease dementia is associated with neurofibrillary tangles composed of aggregated tau protein. Darwich et al. describe an additional form of autosomal-dominant dementia with neurofibrillary tangles linked to a hypomorph mutation in valosin-containing protein (VCP). VCP was found to disaggregate pathologic tau, and the hypomorph mutation increased tau accumulation in cells and mice. These findings highlight the role of protein turnover in maintaining neuronal health and suggest that VCP may provide a therapeutic target for Alzheimer's disease. Science , this issue p. [eaay8826][1] ### INTRODUCTION Alzheimer’s disease (AD) is a fatal neurodegenerative disease in which progressive brain degeneration compromises cognitive function. AD neurodegeneration is tightly associated with abnormal neuronal inclusions called neurofibrillary tangles, which are composed of aggregated tau protein. The importance of tau protein in dementia is highlighted by various known autosomal-dominant mutations in MAPT (microtubule-associated protein tau), the gene that encodes for tau, that are associated with frontotemporal lobar degeneration with tau inclusions (FTLD-tau). Identifying additional disease-causing genes that affect tau accumulation, including genes involved in protein quality control and tau clearance, has the potential to reveal previously unknown mechanisms that maintain neuronal health. ### RATIONALE Two families were identified with autosomal-dominant dementia linked to a p.Asp395Gly mutation in VCP . Valosin-containing protein (VCP) is a AAA+ [adenosine triphosphatases (ATPases) associated with diverse cellular activities] protein and uses energy from adenosine 5′-triphosphate (ATP) hydrolysis to unfold substrates to assist the dismantling of macromolecular complexes. Other VCP mutations have been identified in a disease called multisystem proteinopathy (MSP), which is associated with neuronal TDP-43 [TAR DNA-binding protein 43] protein inclusions. The mechanisms by which p.Asp395Gly VCP leads to neurodegeneration are unknown. ### RESULTS Two kindred were identified with an autosomal-dominant inheritance pattern of frontotemporal degeneration linked to a p.Asp395Gly VCP mutation. We have named this disease vacuolar tauopathy because of the presence of neuronal vacuoles and tau aggregates. Tau aggregates were morphologically and biochemically similar to AD neurofibrillary tangles. Moreover, the presence of vacuoles and neurofibrillary tangles in the brain were inversely correlated. Degenerating brain regions such as the frontal neocortex exhibited tau aggregation, whereas nondegenerating brain regions such as the visual cortex exhibited vacuolization. To further characterize the p.Asp395Gly VCP mutation, we assessed recombinant VCP proteins for ATPase activity in an in vitro assay. This approach demonstrated that p.Asp395Gly VCP exhibited a partial loss of ATPase activity, in contrast with MSP mutations, which increase ATPase activity. Given that VCP unfolds protein substrates, we hypothesized that VCP may disaggregate pathologic tau aggregates. Indeed, VCP appeared to partially disaggregate pathologic tau aggregates derived from AD human brain tissues, and the p.Asp395Gly mutation impaired this activity. VCP activity against pathologic tau was energy (ATP) dependent and required polyubiquitination of the tau substrate. In addition, expression of p.Asp395Gly VCP in a cell culture model of tau aggregation was associated with enhanced accumulation of cellular tau aggregates. Last, we generated mice in which the p.Asp395Gly mutation was knocked in, which exhibited a minimal phenotype when unchallenged. However, upon initiating tau aggregation through microinjection of pathologic AD tau extracts into the mouse brain, mutant VCP mice showed an increase in tau accumulation compared with that of wild-type animals. ### CONCLUSION We describe a partial loss-of-function mutation in VCP that was associated with a neurodegenerative disease, which we named vacuolar tauopathy. VCP appeared to exhibit activity that promoted the disruption of tau aggregates in an energy- and polyubiquitin-dependent manner. Furthermore, the p.Asp395Gly VCP mutation enhanced tau aggregation in a cell culture model system and in a knock-in mouse model. These results highlight a potential role for protein disaggregation in the maintenance of neuronal health. Furthermore, our findings suggest that VCP may provide a potential therapeutic target in the development of future AD therapies. ![Figure][2] Vacuolar tauopathy and VCP function. Model of tau aggregation, highlighting different mechanisms that lead to neurodegeneration in AD and FTLD-tau. Tau is a soluble protein (green) that forms highly structured fibrils (purple) in AD. Tau aggregates are also a hallmark of FTLD-tau. Autosomal dominant FTLD-tau mutations in MAPT , the gene that encodes tau protein, can alter splicing or enhance tau protein aggregation, which results in the accumulation of insoluble tau inclusions in neurons and glia. VCP (blue) appears to exhibit disaggregase activity against pathologic tau. This activity is dependent on the availability of energy (ATP) and the presence of polyubiquitin on tau protein aggregates. Vacuolar tauopathy is an autosomal-dominant form of dementia linked to a p.Asp395Gly VCP mutation (orange-red). Neurodegeneration in vacuolar tauopathy was associated with tau aggregates of similar biochemical composition and morphology as those seen in Alzheimer’s disease. The accumulation of insoluble tau aggregates in vacuolar tauopathy appeared to be in part due to a partial loss of tau disaggregase function associated with the p.Asp395Gly VCP mutation. WT, wild-type. Neurodegeneration in Alzheimer’s disease (AD) is closely associated with the accumulation of pathologic tau aggregates in the form of neurofibrillary tangles. We found that a p.Asp395Gly mutation in VCP (valosin-containing protein) was associated with dementia characterized neuropathologically by neuronal vacuoles and neurofibrillary tangles. Moreover, VCP appeared to exhibit tau disaggregase activity in vitro, which was impaired by the p.Asp395Gly mutation. Additionally, intracerebral microinjection of pathologic tau led to increased tau aggregates in mice in which p.Asp395Gly VCP mice was knocked in, as compared with injected wild-type mice. These findings suggest that p.Asp395Gly VCP is an autosomal-dominant genetic mutation associated with neurofibrillary degeneration in part owing to reduced tau disaggregation, raising the possibility that VCP may represent a therapeutic target for the treatment of AD. [1]: /lookup/doi/10.1126/science.aay8826 [2]: pending:yes


IBM's latest AI predicts Alzheimer's better than standard tests

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IBM has developed a new AI model which predicts the onset of Alzheimer's better than standard clinical tests. The AI is designed to be non-invasive and uses a short language sample from a verbal cognitive test given to a patient. Using this sample, the AI model is able to predict the onset of Alzheimer's with around 71 percent accuracy. For comparison, standard clinical tests are correct approximately 59 percent of the time and take much longer to diagnose. Current tests analyse the descriptive abilities of people as they age for potential warning signs.


Irish researcher develops AI to help prevent sight loss

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The ability to apply artificial intelligence (AI) to ophthalmology is gathering pace, a consequence of remarkable collaboration between eye specialists and technologists whose forte is the ability to process vast amounts of data quickly. Irish ophthalmologist Dr Pearse Keane – based in Moorfields Hospital, London – has been the chief catalyst in developing AI software to detect 50 sight-threatening eye diseases. It operates by interpreting optical coherence tomography (OCT) scans of the back of the eye, which soon will be routine when going for an eye check. Automation in analysing scans for diseases such as wet age-related macular degeneration (AMD), the main cause of blindness in Europe, and diabetic retinopathy, is about to revolutionise patient outcomes with faster results affording earlier diagnosis and prompt treatment, and ultimately preventing avoidable sight loss. Since that initial breakthrough, the Keane team has developed an alert system for a third of people with AMD who later get it in their good eye and, potentially, an early-warning system for onset of neurodegenerative diseases, notably Alzheimer's.


How a machine learning algorithm could identify the early stages of Alzheimer's in patients - Mental Daily

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A study published in the Journal of Medical Imaging unveils the use of machine learning to detect the early stages of Alzheimer's disease (AD) by functional magnetic resonance imaging. Alzheimer's disease is a neurodegenerative condition primarily occurring in late-adulthood and begins with symptoms of cognitive decline. Researchers from Texas Tech University developed a deep-learning algorithm called a convolutional neural network able to distinguish between the fMRI signals of healthy individuals, patients with mild cognitive impairment (MCI), and patients with Alzheimer's. "We present one such synergy of fMRI and deep learning, where we apply a simplified yet accurate method using a modified 3D convolutional neural networks to resting-state fMRI data for feature extraction and classification of Alzheimer's disease," the co-authors explained in their findings. "The convolutional neural network is designed in such a way that it uses the fMRI data with much less preprocessing, preserving both spatial and temporal information."


Artificial intelligence-based algorithm for the early diagnosis of Alzheimer's

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Alzheimer's disease (AD) is a neurodegenerative disorder that affects a significant proportion of the older population worldwide. It causes irreparable damage to the brain and severely impairs the quality of life in patients. Unfortunately, AD cannot be cured, but early detection can allow medication to manage symptoms and slow the progression of the disease. Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders. It measures minute changes in blood oxygen levels within the brain over time, giving insight into the local activity of neurons.


How a machine learning algorithm could identify the early stages of Alzheimer's in patients

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Researchers from Texas Tech University developed a deep-learning algorithm called a convolutional neural network able to distinguish between the …


How artificial intelligence can become game-changer in world of diagnostics

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Deployment of AI could mean a quantum leap for diagnostics--promising developments have already been reported for breast cancer, kidney disease and even Covid-19. But, neuropsychological pathologies have presented a different challenge altogether. A fortnight ago, however, a breakthrough was reported by IBM and Pfizer in the detection of the onset of Alzheimer's. Using language data from the Framingham Heart Study of 1948, the technique detected speech impairment that is typical of Alzheimer's. Observing 703 samples of 270 participants, the AI model predicted the probability of a person developing Alzheimer's.


Artificial intelligence-based algorithm for the early diagnosis of Alzheimer's

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Alzheimer's disease (AD) is a neurodegenerative disorder that affects a significant proportion of the older population worldwide. It causes irreparable damage to the brain and severely impairs the quality of life in patients. Unfortunately, AD cannot be cured, but early detection can allow medication to manage symptoms and slow the progression of the disease. Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders. It measures minute changes in blood oxygen levels within the brain over time, giving insight into the local activity of neurons.


Artificial intelligence-based algorithm for the early diagnosis of Alzheimer's

#artificialintelligence

IMAGE: Network activation map from the output of second temporal convolution layer mapped onto MNI brain atlas. Alzheimer's disease (AD) is a neurodegenerative disorder that affects a significant proportion of the older population worldwide. It causes irreparable damage to the brain and severely impairs the quality of life in patients. Unfortunately, AD cannot be cured, but early detection can allow medication to manage symptoms and slow the progression of the disease. Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders.