scholarly journals Novel blood test for early biomarkers of preeclampsia and Alzheimer's disease

2021 ◽  
Author(s):  
Shibin Cheng ◽  
Sayani Banerjee ◽  
Lori Daiello ◽  
Akitoshi Nakashima ◽  
Sukanta Jash ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Serum Protein ◽  
Blood Test ◽  
Early Stage ◽  
Protein Aggregates ◽  
Disease Diagnosis ◽  
Alpha Synuclein ◽  
Early Stage Disease ◽  
Specificity And Sensitivity

Non-invasive and sensitive blood test has long been a goal for early stage disease diagnosis and treatment for Alzheimer's disease (AD) and other proteinopathy diseases. However, a blood test based on a mechanistic link to pathologic protein aggregate complexes has not been yet elucidated. We previously reported that preeclampsia (PE), a severe pregnancy complication, is another proteinopathy disorder with impaired autophagy. We hypothesized that induced autophagy deficiency would promote accumulation of pathologic protein aggregates. Here, we describe a novel, sensitive assay that detects serum protein aggregates from patients with PE as well as AD in both dementia and prodromal mild cognitive impairment (MCI) stages. The assay employs exposure of genetically engineered, autophagy-deficient human trophoblasts (ADTs) to serum from patients. The aggregated protein complexes and their individual components, including transthyretin, amyloid beta-42, alpha-synuclein, and phosphorylated tau231, can be detected and quantified by co-staining with ProteoStat, a rotor dye with affinity to aggregated proteins, and respective antibodies. Autophagy-proficient human trophoblasts failed to accumulate serum protein aggregates under similar culture conditions. Detection of protein aggregates in ADTs was not dependent on transcriptional upregulation of these biomarkers. The ROC curve analysis validated the robustness of the assay for its specificity and sensitivity. In conclusion, we have developed a novel noninvasive diagnostic and predictive assay for AD, MCI and PE.

scholarly journals Novel blood test for early biomarkers of preeclampsia and Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shibin Cheng ◽  
Sayani Banerjee ◽  
Lori A. Daiello ◽  
Akitoshi Nakashima ◽  
Sukanta Jash ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Blood Test ◽  
Late Onset ◽  
Early Stage ◽  
Amyloid Β ◽  
Protein Aggregates ◽  
Disease Diagnosis ◽  
Early Stage Disease ◽  
Specificity And Sensitivity

AbstractA non-invasive and sensitive blood test has long been a goal for early stage disease diagnosis and treatment for Alzheimer’s disease (AD) and other proteinopathy diseases. We previously reported that preeclampsia (PE), a severe pregnancy complication, is another proteinopathy disorder with impaired autophagy. We hypothesized that induced autophagy deficiency would promote accumulation of pathologic protein aggregates. Here, we describe a novel, sensitive assay that detects serum protein aggregates from patients with PE (n = 33 early onset and 33 late onset) and gestational age-matched controls (n = 77) as well as AD in both dementia and prodromal mild cognitive impairment (MCI, n = 24) stages with age-matched controls (n = 19). The assay employs exposure of genetically engineered, autophagy-deficient human trophoblasts (ADTs) to serum from patients. The aggregated protein complexes and their individual components, including transthyretin, amyloid β-42, α-synuclein, and phosphorylated tau231, can be detected and quantified by co-staining with ProteoStat, a rotor dye with affinity to aggregated proteins, and respective antibodies. Detection of protein aggregates in ADTs was not dependent on transcriptional upregulation of these biomarkers. The ROC curve analysis validated the robustness of the assay for its specificity and sensitivity (PE; AUC: 1, CI: 0.949–1.00; AD; AUC: 0.986, CI: 0.832–1.00). In conclusion, we have developed a novel, noninvasive diagnostic and predictive assay for AD, MCI and PE.

scholarly journals Diagnosis of Early Alzheimer’s Disease: Clinical Practice in 2021

2021 ◽  
pp. 1-16
Author(s):  
A.P. Porsteinsson ◽  
R.S. Isaacson ◽  
S. Knox ◽  
M.N. Sabbagh ◽  
I. Rubino
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Practice ◽  
Early Stage ◽  
Current Model ◽  
Healthcare Providers ◽  
Disease Diagnosis ◽  
Lifestyle Changes ◽  
Behavioral Impairment ◽  
Alzheimer’S Disease Dementia

Alzheimer’s disease is a progressive, irreversible neurodegenerative disease impacting cognition, function, and behavior. Alzheimer’s disease progresses along a continuum from preclinical disease, to mild cognitive and/or behavioral impairment and then Alzheimer’s disease dementia. Recently, clinicians have been encouraged to diagnose Alzheimer’s earlier, before patients have progressed to Alzheimer’s disease dementia. The early and accurate detection of Alzheimer’s disease-associated symptoms and underlying disease pathology by clinicians is fundamental for the screening, diagnosis, and subsequent management of Alzheimer’s disease patients. It also enables patients and their caregivers to plan for the future and make appropriate lifestyle changes that could help maintain their quality of life for longer. Unfortunately, detecting early-stage Alzheimer’s disease in clinical practice can be challenging and is hindered by several barriers including constraints on clinicians’ time, difficulty accurately diagnosing Alzheimer’s pathology, and that patients and healthcare providers often dismiss symptoms as part of the normal aging process. As the prevalence of this disease continues to grow, the current model for Alzheimer’s disease diagnosis and patient management will need to evolve to integrate care across clinical disciplines and the disease continuum, beginning with primary care. This review summarizes the importance of establishing an early diagnosis of Alzheimer’s disease, related practical ‘how-to’ guidance and considerations, and tools that can be used by healthcare providers throughout the diagnostic journey.

scholarly journals Support vector machine based alzheimer’s disease diagnosis using synchrony features

2020 ◽  
Vol 9 (1) ◽  
pp. 57
Author(s):  
Nilesh Kulkarni
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Early Stage ◽  
Research Work ◽  
Normal Subjects ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Experimental Database ◽  
Wavelet Features

Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.

scholarly journals Role of Body-Fluid Biomarkers in Alzheimer’s Disease Diagnosis

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 326 ◽  
Author(s):  
Thuy Trang Nguyen ◽  
Qui Thanh Hoai Ta ◽  
Thi Kim Oanh Nguyen ◽  
Thi Thuy Dung Nguyen ◽  
Van Giau Vo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Body Fluid ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Clinical Diagnostics ◽  
Research Priority ◽  
Development And Validation ◽  
The Brain

Alzheimer’s disease (AD) is a complex neurodegenerative disease that requires extremely specific biomarkers for its diagnosis. For current diagnostics capable of identifying AD, the development and validation of early stage biomarkers is a top research priority. Body-fluid biomarkers might closely reflect synaptic dysfunction in the brain and, thereby, could contribute to improving diagnostic accuracy and monitoring disease progression, and serve as markers for assessing the response to disease-modifying therapies at early onset. Here, we highlight current advances in the research on the capabilities of body-fluid biomarkers and their role in AD pathology. Then, we describe and discuss current applications of the potential biomarkers in clinical diagnostics in AD.

scholarly journals Automatic Segmentation of Hippocampus and Classification of brain MRI for Alzheimer’s Detection

2021 ◽  
Vol 10 (3) ◽  
pp. 1743-1750
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Standard Technique ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Shape Feature

Hippocampus is the structure of brain thatis mostly affected by Alzheimer’s disease at an early stage. Atrophy of hippocampus has been found asa predictive feature for Alzheimer’s disease diagnosis. To measure the atrophy of hippocampus we need to segment it out from surrounding structures of brain. Manual segmentation of hippocampus has beenfound standard technique for hippocampus segmentation in literature, but isvery time consuming and depends on particular anatomical information. In this work we have proposed an automatic approach to segment hippocampus considering texture and active contour from the brain Magnetic Resonance Image. After segmentation, features based on atrophy and shape of hippocampus has beenmeasured. Support vector machine classifier with radial basis function kernel has been analyzed with extracted features for classification of Alzheimer’s and control subjects. In the proposed technique, 200AD MRI and 200control MRI have been considered from Alzheimer’s Disease Neuroimaging Initiative database. The experiment have shown 93% accuracy, 0.96 sensitivity and 0.90specificity with atrophy feature and 94% accuracy, 0.96sensitivity and 0.92specificity with shape feature. Further, 0.96sensitivity, 1 specificity and 98% accuracyhave beenobtained with the fusion of atrophy and shape feature

scholarly journals Amyloid-beta and tau pathologies are both necessary to induce novel stage-specific microglia subtypes during Alzheimer's disease progression.

2021 ◽  
Author(s):  
Dong Won Kim ◽  
Kevin Tu ◽  
Alice Wei ◽  
Ashley Lau ◽  
Anabel Gonzalez-Gil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Amyloid Β ◽  
Disease Stage ◽  
Dependent Manner ◽  
Early Stage Disease ◽  
Stage Disease ◽  
Human Orthologue ◽  
Late Stages

It is unknown whether specific microglia are selectively induced by amyloid-β(Aβ), tau pathologies, or both in combination. To address this, we use single-cell RNA-sequencing to profile mice bearing both Aβ and tau pathologies during Alzheimer's disease (AD). We identify novel microglia subtypes induced in a disease stage-specific manner. We show that during early-stage disease, interferon signaling induces a subtype of microglia termed EADAM. During late-stage disease, a second microglia subtype termed LADAM is detected. While EADAM and LADAM-like microglia are observed in other neurodegenerative models, the magnitude and composition of subtype markers are distinct from microglia observed with AD-like pathology. The pattern of EADAM- and LADAM-associated gene expression is observed in microglia from human AD, during the early and late stages of disease, respectively. Furthermore, we observe that several siglec genes are selectively expressed in either EADAM or LADAM. Siglecg is expressed in white-matter-associated LADAM, and expression of the human orthologue of Siglecg is progressively elevated in AD-stage-dependent manner but not shown in non-AD tauopathy. Our findings imply that both Aβ and tau pathologies are required for disease stage-specific induction of EADAM and LADAM.

scholarly journals Normal and Abnormal Sharp Wave Ripples in the Hippocampal-Entorhinal Cortex System: Implications for Memory Consolidation, Alzheimer's Disease, and Temporal Lobe Epilepsy

2021 ◽  
Vol 13 ◽  
Author(s):  
Zhi-Hang Zhen ◽  
Mo-Ran Guo ◽  
He-Ming Li ◽  
Ou-Yang Guo ◽  
Jun-Li Zhen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Episodic Memory ◽  
Temporal Lobe Epilepsy ◽  
Entorhinal Cortex ◽  
Temporal Lobe ◽  
Memory Consolidation ◽  
Early Stage ◽  
Early Stage Disease ◽  
Sharp Wave

The appearance of hippocampal sharp wave ripples (SWRs) is an electrophysiological biomarker for episodic memory encoding and behavioral planning. Disturbed SWRs are considered a sign of neural network dysfunction that may provide insights into the structural connectivity changes associated with cognitive impairment in early-stage Alzheimer's disease (AD) and temporal lobe epilepsy (TLE). SWRs originating from hippocampus have been extensively studied during spatial navigation in rodents, and more recent studies have investigated SWRs in the hippocampal-entorhinal cortex (HPC-EC) system during a variety of other memory-guided behaviors. Understanding how SWR disruption impairs memory function, especially episodic memory, could aid in the development of more efficacious therapeutics for AD and TLE. In this review, we first provide an overview of the reciprocal association between AD and TLE, and then focus on the functions of HPC-EC system SWRs in episodic memory consolidation. It is posited that these waveforms reflect rapid network interactions among excitatory projection neurons and local interneurons and that these waves may contribute to synaptic plasticity underlying memory consolidation. Further, SWRs appear altered or ectopic in AD and TLE. These waveforms may thus provide clues to understanding disease pathogenesis and may even serve as biomarkers for early-stage disease progression and treatment response.

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