Publications by authors named "B Renee Dugger"

91 Publications

Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future.

J Neuropathol Exp Neurol 2022 Jan;81(1):2-15

From the Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, California, USA.

Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) plaques in the brain. The disease was first described in 1906 by Alois Alzheimer, and since then, there have been many advancements in technologies that have aided in unlocking the secrets of this devastating disease. Such advancements include improving microscopy and staining techniques, refining diagnostic criteria for the disease, and increased appreciation for disease heterogeneity both in neuroanatomic location of abnormalities as well as overlap with other brain diseases; for example, Lewy body disease and vascular dementia. Despite numerous advancements, there is still much to achieve as there is not a cure for AD and postmortem histological analyses is still the gold standard for appreciating AD neuropathologic changes. Recent technological advances such as in-vivo biomarkers and machine learning algorithms permit great strides in disease understanding, and pave the way for potential new therapies and precision medicine approaches. Here, we review the history of human AD neuropathology research to include the notable advancements in understanding common co-pathologies in the setting of AD, and microscopy and staining methods. We also discuss future approaches with a specific focus on deep phenotyping using machine learning.
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http://dx.doi.org/10.1093/jnen/nlab122DOI Listing
January 2022

A Semi-supervised Learning for Segmentation of Gigapixel Histopathology Images from Brain Tissues.

Annu Int Conf IEEE Eng Med Biol Soc 2021 11;2021:1920-1923

Automated segmentation of grey matter (GM) and white matter (WM) in gigapixel histopathology images is advantageous to analyzing distributions of disease pathologies, further aiding in neuropathologic deep phenotyping. Although supervised deep learning methods have shown good performance, its requirement of a large amount of labeled data may not be cost-effective for large scale projects. In the case of GM/WM segmentation, trained experts need to carefully trace the delineation in gigapixel images. To minimize manual labeling, we consider semi-surprised learning (SSL) and deploy one state-of-the-art SSL method (FixMatch) on WSIs. Then we propose a two-stage scheme to further improve the performance of SSL: the first stage is a self-supervised module to train an encoder to learn the visual representations of unlabeled data, subsequently, this well-trained encoder will be an initialization of consistency loss-based SSL in the second stage. We test our method on Amyloid-β stained histopathology images and the results outperform FixMatch with the mean IoU score at around 2% by using 6,000 labeled tiles while over 10% by using only 600 labeled tiles from 2 WSIs.Clinical relevance- this work minimizes the required labeling efforts by trained personnel. An improved GM/WM segmentation method could further aid in the study of brain diseases, such as Alzheimer's disease.
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http://dx.doi.org/10.1109/EMBC46164.2021.9629715DOI Listing
November 2021

Incorporation of uncertainty to improve projections of tidal wetland elevation and carbon accumulation with sea-level rise.

PLoS One 2021 20;16(10):e0256707. Epub 2021 Oct 20.

U.S. Geological Survey, Western Ecological Research Center, Davis, California, United States of America.

Understanding the rates and patterns of tidal wetland elevation changes relative to sea-level is essential for understanding the extent of potential wetland loss over the coming years. Using an enhanced and more flexible modeling framework of an ecosystem model (WARMER-2), we explored sea-level rise (SLR) impacts on wetland elevations and carbon sequestration rates through 2100 by considering plant community transitions, salinity effects on productivity, and changes in sediment availability. We incorporated local experimental results for plant productivity relative to inundation and salinity into a species transition model, as well as site-level estimates of organic matter decomposition. The revised modeling framework includes an improved calibration scheme that more accurately reconstructs soil profiles and incorporates parameter uncertainty through Monte Carlo simulations. Using WARMER-2, we evaluated elevation change in three tidal wetlands in the San Francisco Bay Estuary, CA, USA along an estuarine tidal and salinity gradient with varying scenarios of SLR, salinization, and changes in sediment availability. We also tested the sensitivity of marsh elevation and carbon accumulation rates to different plant productivity functions. Wetland elevation at all three sites was sensitive to changes in sediment availability, but sites with greater initial elevations or space for upland transgression persisted longer under higher SLR rates than sites at lower elevations. Using a multi-species wetland vegetation transition model for organic matter contribution to accretion, WARMER-2 projected increased elevations relative to sea levels (resilience) and higher rates of carbon accumulation when compared with projections assuming no future change in vegetation with SLR. A threshold analysis revealed that all three wetland sites were likely to eventually transition to an unvegetated state with SLR rates above 7 mm/yr. Our results show the utility in incorporating additional estuary-specific parameters to bolster confidence in model projections. The new WARMER-2 modeling framework is widely applicable to other tidal wetland ecosystems and can assist in teasing apart important drivers of wetland elevation change under SLR.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0256707PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8528310PMC
November 2021

Clinicopathological Correlation: Dopamine and Amyloid PET Imaging with Neuropathology in Three Subjects Clinically Diagnosed with Alzheimer's Disease or Dementia with Lewy Bodies.

J Alzheimers Dis 2021 ;80(4):1603-1612

Department of Neurology, Mayo Clinic College of Medicine, Scottsdale, AZ, USA.

Background: Imaging biomarkers have the potential to distinguish between different brain pathologies based on the type of ligand used with PET. AV-45 PET (florbetapir, Amyvid™) is selective for the neuritic plaque amyloid of Alzheimer's disease (AD), while AV-133 PET (florbenazine) is selective for VMAT2, which is a dopaminergic marker.

Objective: To report the clinical, AV-133 PET, AV-45 PET, and neuropathological findings of three clinically diagnosed dementia patients who were part of the Avid Radiopharmaceuticals AV133-B03 study as well as the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND).

Methods: Three subjects who had PET imaging with both AV-133 and AV-45 as well as a standardized neuropathological assessment were included. The final clinical, PET scan, and neuropathological diagnoses were compared.

Results: The clinical and neuropathological diagnoses were made blinded to PET scan results. The first subject had a clinical diagnosis of dementia with Lewy bodies (DLB); AV-133 PET showed bilateral striatal dopaminergic degeneration, and AV-45 PET was positive for amyloid. The final clinicopathological diagnosis was DLB and AD. The second subject was diagnosed clinically with probable AD; AV-45 PET was positive for amyloid, while striatal AV-133 PET was normal. The final clinicopathological diagnosis was DLB and AD. The third subject had a clinical diagnosis of DLB. Her AV-45 PET was positive for amyloid and striatal AV-133 showed dopaminergic degeneration. The final clinicopathological diagnosis was multiple system atrophy and AD.

Conclusion: PET imaging using AV-133 for the assessment of striatal VMAT2 density may help distinguish between AD and DLB. However, some cases of DLB with less-pronounced nigrostriatal dopaminergic neuronal loss may be missed.
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http://dx.doi.org/10.3233/JAD-200323DOI Listing
September 2021

Lipidomic Analysis of Postmortem Prefrontal Cortex Phospholipids Reveals Changes in Choline Plasmalogen Containing Docosahexaenoic Acid and Stearic Acid Between Cases With and Without Alzheimer's Disease.

Neuromolecular Med 2021 03 21;23(1):161-175. Epub 2021 Jan 21.

Department of Food Science and Technology, College of Agriculture and Environmental Sciences, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA.

Alzheimer's disease (AD) is a progressive and incurable brain disorder that has been associated with structural changes in brain phospholipids (PLs), including diacyl species and ether-linked PLs known as plasmalogens. Most studies have characterized total changes in brain PL pools (e.g., choline plasmalogens), particularly in prefrontal cortex, but detailed and quantitative information on the molecular PL species impacted by the disease is limited. In this study, we used a comprehensive mass-spectrometry method to quantify diacyl and plasmalogen species, alkyl synthetic precursors of plasmalogens, and lysophospholipid degradation products of diacyl and plasmalogen PLs, in postmortem samples of prefrontal cortex from 21 AD patients and 20 age-matched controls. Total PLs were also quantified with gas-chromatography analysis of bound fatty acids following thin layer chromatography isolation. There was a significant 27% reduction in the concentration (nmol/g wet weight) of choline plasmalogen containing stearic acid (alkenyl group) and docosahexaenoic acid in AD compared to controls. Stearic acid concentration in total PLs was reduced by 26%. Our findings suggest specific changes in PLs containing stearic acid and docosahexaenoic acid in AD prefrontal cortex, highlighting structural and turnover PL pathways that could be targeted.
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http://dx.doi.org/10.1007/s12017-020-08636-wDOI Listing
March 2021
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