Improved comparability between measurements of mean cortical amyloid plaque burden derived from different PET tracers using multiple regions‐of‐interest and machine learning

Recent rapid technological advancements in cardiac CT have improved image quality and reduced radiation exposure to patients. Furthermore, key insights from large cohort trials have helped delineate cardiovascular disease risk as a function of overall coronary plaque burden and the morphological appearance of individual plaques. The advent of CT-derived fractional flow reserve promises to establish an anatomical and functional test within one modality. Recent data examining the short-term impact of CT-derived fractional flow reserve on downstream care and clinical outcomes have been published. In addition, machine learning is a concept that is being increasingly applied to diagnostic medicine. Over the coming decade, machine learning will begin to be integrated into cardiac CT, and will potentially make a tangible difference to how this modality evolves. The authors have performed an extensive literature review and comprehensive analysis of the recent advances in cardiac CT. They review how recent advances currently impact on clinical care and potential future directions for this imaging modality.

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Progranulin expression correlates with dense-core amyloid plaque burden in Alzheimer disease mouse models

The Journal of Pathology ◽

10.1002/path.2580 ◽

2009 ◽

Vol 219 (2) ◽

pp. 173-181 ◽

Cited By ~ 52

Author(s):

Sandra Pereson ◽

Hans Wils ◽

Gernot Kleinberger ◽

Eileen McGowan ◽

Mado Vandewoestyne ◽

...

Keyword(s):

Alzheimer Disease ◽

Mouse Models ◽

Amyloid Plaque ◽

Dense Core ◽

Plaque Burden

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COPS5 Protein Overexpression Increases Amyloid Plaque Burden, Decreases Spinophilin-immunoreactive Puncta, and Exacerbates Learning and Memory Deficits in the Mouse Brain

Journal of Biological Chemistry ◽

10.1074/jbc.m114.595926 ◽

2015 ◽

Vol 290 (14) ◽

pp. 9299-9309 ◽

Cited By ~ 4

Author(s):

Ruizhi Wang ◽

Hongjie Wang ◽

Ivan Carrera ◽

Shaohua Xu ◽

Madepalli K. Lakshmana

Keyword(s):

Learning And Memory ◽

Mouse Brain ◽

Amyloid Plaque ◽

Memory Deficits ◽

Plaque Burden ◽

Protein Overexpression ◽

Learning And Memory Deficits

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Machine Learning applied to Lunar Data to Characterize Potential Sites for Future Science, Mobile Exploration, Utilization, lunar Bases and Moon Villages.

10.5194/egusphere-egu21-16141 ◽

2021 ◽

Author(s):

Daniël den Heijer ◽

Bernard Foing

Keyword(s):

Machine Learning ◽

Regions Of Interest ◽

Machine Learning Techniques ◽

Identification Algorithm ◽

South Pole ◽

Lava Tubes ◽

Crater Detection ◽

Lunar South Pole ◽

Areas Of Interest ◽

Human Exploration

<p>The lunar south pole is of particular interest to researchers because of its unique geographical features. It contains craters where the near-constant sunlight does not reach the interior. These craters are of enormous importance in the process of human exploration of the moon.This research aims to develop an identification algorithm applied to LROC data to characterize and identify potential regions of interest on the lunar south pole. Such areas of interest include (surroundings of) lava tubes, skylights, crater detection for age estimation, and planning traverses for the Artemis successive missions.Identifying these regions will be done using machine learning techniques such as a deep convolutional neural network that will be trained on labeled data and are then used to identify and characterize new regions of interest.</p>

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Microglial-associated responses to comorbid amyloid pathology and hyperhomocysteinemia in an aged knock-in mouse model of Alzheimer’s disease

Journal of Neuroinflammation ◽

10.1186/s12974-020-01938-7 ◽

2020 ◽

Vol 17 (1) ◽

Author(s):

David J. Braun ◽

Edgardo Dimayuga ◽

Josh M. Morganti ◽

Linda J. Van Eldik

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Mouse Model ◽

Inflammatory Responses ◽

Specific Effect ◽

Amyloid Plaque ◽

Plaque Burden ◽

Amyloid Pathology ◽

Model Of Alzheimer’S Disease ◽

B Vitamin

Abstract Background Elevated blood homocysteine levels, termed hyperhomocysteinemia (HHcy), is a prevalent risk factor for Alzheimer’s disease (AD) in elderly populations. While dietary supplementation of B-vitamins is a generally effective method to lower homocysteine levels, there is little if any benefit to cognition. In the context of amyloid pathology, dietary-induced HHcy is known to enhance amyloid deposition and certain inflammatory responses. Little is known, however, about whether there is a more specific effect on microglia resulting from combined amyloid and HHcy pathologies. Methods The present study used a knock-in mouse model of amyloidosis, aged to 12 months, given 8 weeks of B-vitamin deficiency-induced HHcy to better understand how microglia are affected in this comorbidity context. Results We found that HHcy-inducing diet increased amyloid plaque burden, altered the neuroinflammatory milieu, and upregulated the expression of multiple damage-associated and “homeostatic” microglial genes. Conclusions Taken together, these data indicate complex effects of comorbid pathologies on microglial function that are not driven solely by increased amyloid burden. Given the highly dynamic nature of microglia, their central role in AD pathology, and the frequent occurrence of various comorbidities in AD patients, it is increasingly important to understand how microglia respond to mixed pathological processes.

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Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma

Behavioural Neurology ◽

10.1155/2020/1712604 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Zi-Qi Pan ◽

Shu-Jun Zhang ◽

Xiang-Lian Wang ◽

Yu-Xin Jiao ◽

Jian-Jian Qiu

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Cox Regression ◽

Clinical Risk Factors ◽

Cox Regression Analysis ◽

Clinical Risk ◽

Independent Test ◽

Independent Test Dataset ◽

Multiple Regions ◽

Radiomics Signature

Background and Objective. Although radiotherapy has become one of the main treatment methods for cancer, there is no noninvasive method to predict the radiotherapeutic response of individual glioblastoma (GBM) patients before surgery. The purpose of this study is to develop and validate a machine learning-based radiomics signature to predict the radiotherapeutic response of GBM patients. Methods. The MRI images, genetic data, and clinical data of 152 patients with GBM were analyzed. 122 patients from the TCIA dataset (training set: n = 82 ; validation set: n = 40 ) and 30 patients from local hospitals were used as an independent test dataset. Radiomics features were extracted from multiple regions of multiparameter MRI. Kaplan-Meier survival analysis was used to verify the ability of the imaging signature to predict the response of GBM patients to radiotherapy before an operation. Multivariate Cox regression including radiomics signature and preoperative clinical risk factors was used to further improve the ability to predict the overall survival (OS) of individual GBM patients, which was presented in the form of a nomogram. Results. The radiomics signature was built by eight selected features. The C -index of the radiomics signature in the TCIA and independent test cohorts was 0.703 ( P < 0.001 ) and 0.757 ( P = 0.001 ), respectively. Multivariate Cox regression analysis confirmed that the radiomics signature (HR: 0.290, P < 0.001 ), age (HR: 1.023, P = 0.01 ), and KPS (HR: 0.968, P < 0.001 ) were independent risk factors for OS in GBM patients before surgery. When the radiomics signature and preoperative clinical risk factors were combined, the radiomics nomogram further improved the performance of OS prediction in individual patients ( C ‐ index = 0.764 and 0.758 in the TCIA and test cohorts, respectively). Conclusion. This study developed a radiomics signature that can predict the response of individual GBM patients to radiotherapy and may be a new supplement for precise GBM radiotherapy.

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Content-Based Image Retrieval Based on Relative Locations of Multiple Regions of Interest Using Selective Regions Matching

Content-Based Image Retrieval ◽

10.1007/978-981-10-6759-4_9 ◽

2017 ◽

pp. 183-203

Author(s):

Vipin Tyagi

Keyword(s):

Image Retrieval ◽

Regions Of Interest ◽

Content Based Image Retrieval ◽

Multiple Regions

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IL-1β-driven amyloid plaque clearance is associated with an expansion of transcriptionally reprogrammed microglia

Journal of Neuroinflammation ◽

10.1186/s12974-019-1645-7 ◽

2019 ◽

Vol 16 (1) ◽

Cited By ~ 4

Author(s):

Fátima Rivera-Escalera ◽

Jonathan J. Pinney ◽

Laura Owlett ◽

Hoda Ahmed ◽

Juilee Thakar ◽

...

Keyword(s):

Flow Cytometry ◽

Immune Responses ◽

Large Scale ◽

Myeloid Cells ◽

Amyloid Plaque ◽

Plaque Burden ◽

Cytokine Signaling ◽

Expression Of Genes ◽

Adenoviral Transduction ◽

Plaque Clearance

Abstract Background Neuroinflammation is thought to contribute to the pathogenesis of Alzheimer’s disease (AD), yet numerous studies have demonstrated a beneficial role for neuroinflammation in amyloid plaque clearance. We have previously shown that sustained expression of IL-1β in the hippocampus of APP/PS1 mice decreases amyloid plaque burden independent of recruited CCR2+ myeloid cells, suggesting resident microglia as the main phagocytic effectors of IL-1β-induced plaque clearance. To date, however, the mechanisms of IL-1β-induced plaque clearance remain poorly understood. Methods To determine whether microglia are involved in IL-1β-induced plaque clearance, APP/PS1 mice induced to express mature human IL-1β in the hippocampus via adenoviral transduction were treated with the Aβ fluorescent probe methoxy-X04 (MX04) and microglial internalization of fibrillar Aβ (fAβ) was analyzed by flow cytometry and immunohistochemistry. To assess microglial proliferation, APP/PS1 mice transduced with IL-1β or control were injected intraperitoneally with BrdU and hippocampal tissue was analyzed by flow cytometry. RNAseq analysis was conducted on microglia FACS sorted from the hippocampus of control or IL-1β-treated APP/PS1 mice. These microglia were also sorted based on MX04 labeling (MX04+ and MX04− microglia). Results Resident microglia (CD45loCD11b+) constituted > 70% of the MX04+ cells in both Phe- and IL-1β-treated conditions, and < 15% of MX04+ cells were recruited myeloid cells (CD45hiCD11b+). However, IL-1β treatment did not augment the percentage of MX04+ microglia nor the quantity of fAβ internalized by individual microglia. Instead, IL-1β increased the total number of MX04+ microglia in the hippocampus due to IL-1β-induced proliferation. In addition, transcriptomic analyses revealed that IL-1β treatment was associated with large-scale changes in the expression of genes related to immune responses, proliferation, and cytokine signaling. Conclusions These studies show that IL-1β overexpression early in amyloid pathogenesis induces a change in the microglial gene expression profile and an expansion of microglial cells that facilitates Aβ plaque clearance.

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