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Article: Understanding the molecular basis of resilience to Alzheimer's disease.

Montine, Kathleen S / Berson, Eloïse / Phongpreecha, Thanaphong / Huang, Zhi / Aghaeepour, Nima / Zou, James Y / MacCoss, Michael J / Montine, Thomas J

Frontiers in neuroscience

2023 Volume 17, Page(s) 1311157

Abstract: The cellular and molecular distinction between brain aging and neurodegenerative disease begins to blur in the oldest old. Approximately 15-25% of observations in humans do not fit predicted clinical manifestations, likely the result of suppressed damage ...

Abstract	The cellular and molecular distinction between brain aging and neurodegenerative disease begins to blur in the oldest old. Approximately 15-25% of observations in humans do not fit predicted clinical manifestations, likely the result of suppressed damage despite usually adequate stressors and of resilience, the suppression of neurological dysfunction despite usually adequate degeneration. Factors during life may predict the clinico-pathologic state of resilience: cardiovascular health and mental health, more so than educational attainment, are predictive of a continuous measure of resilience to Alzheimer's disease (AD) and AD-related dementias (ADRDs). In resilience to AD alone (RAD), core features include synaptic and axonal processes, especially in the hippocampus. Future focus on larger and more diverse cohorts and additional regions offer emerging opportunities to understand this counterforce to neurodegeneration. The focus of this review is the molecular basis of resilience to AD.
Language	English
Publishing date	2023-12-19
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2023.1311157
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Article: Human cerebrospinal fluid single exosomes in Parkinson's and Alzheimer's diseases.

Yakabi, Koya / Berson, Eloise / Montine, Kathleen S / Bendall, Sean C / MacCoss, Michael J / Poston, Kathleen L / Montine, Thomas J

bioRxiv : the preprint server for biology

2023

Abstract: Exosomes are proposed to be important in the pathogenesis of prevalent neurodegenerative diseases. We report the first application of solid-state technology to perform multiplex analysis of single exosomes in human cerebrospinal fluid (CSF) obtained from ...

Abstract	Exosomes are proposed to be important in the pathogenesis of prevalent neurodegenerative diseases. We report the first application of solid-state technology to perform multiplex analysis of single exosomes in human cerebrospinal fluid (CSF) obtained from the lumbar sac of people diagnosed with Alzheimer's disease dementia (ADD, n=30) or Parkinson's disease dementia (PDD, n=30), as well as age-matched health controls (HCN, n=30). Single events were captured with mouse monoclonal antibodies to one of three different tetraspanins (CD9, CD63, or CD81) or with mouse (M) IgG control, and then probed with fluorescently labeled antibodies to prion protein (PrP) or CD47 to mark neuronal or presynaptic origin, as well as ADD- and PDD-related proteins: amyloid beta (Aβ), tau, α-synuclein, and Apolipoprotein (Apo) E. Data were collected only from captured events that were within the size range of 50 to 200 nm. Exosomes were present at approximately 100 billion per mL human CSF and were similarly abundant for CD9+ and CD81+ events, but CD63+ were only 22% to 25% of CD9+ (P<0.0001) or CD81+ (P<0.0001) events. Approximately 24% of CSF exosomes were PrP+, while only 2% were CD47+. The vast majority of exosomes were surface ApoE+, and the number of PrP-ApoE+ (P<0.001) and PrP+ApoE+ (P<0.01) exosomes were significantly reduced in ADD vs. HCN for CD9+ events only. Aβ, tau, and α-synuclein were not detected on the exosome surface or in permeabilized cargo. These data provide new insights into single exosome molecular features and highlight reduction in the CSF concentration of ApoE+ exosomes in patients with ADD.
Language	English
Publishing date	2023-12-23
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.22.573124
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Article: Intra- and post-pandemic impact of the COVID-19 outbreak on Stanford Health Care.

Phongpreecha, Thanaphong / Berson, Eloise / Xue, Lei / Shome, Sayane / Saarunya, Geetha / Fralick, Jennifer / Ruiz-Tagle, Bernardita Guridi / Foody, Andrew / Chin, Alexander L / Lim, Michael / Arthofer, Rudolph / Albini, Christopher / Montine, Kathleen / Folkins, Ann K / Kong, Christina S / Aghaeepour, Nima / Montine, Thomas / Kerr, Alison

Academic pathology

2024 Volume 11, Issue 2, Page(s) 100113

Abstract: Stanford Health Care, which provides about 7% of overall healthcare to approximately 9 million people in the San Francisco Bay Area, has undergone significant changes due to the opening of a second hospital in late 2019 and, more importantly, the COVID- ... ...

Abstract	Stanford Health Care, which provides about 7% of overall healthcare to approximately 9 million people in the San Francisco Bay Area, has undergone significant changes due to the opening of a second hospital in late 2019 and, more importantly, the COVID-19 pandemic. We examine the impact of these events on anatomic pathology (AP) cases, aiming to enhance operational efficiency in response to evolving healthcare demands. We extracted historical census, admission, lab tests, operation, and AP data since 2015. An approximately 45% increase in the volume of laboratory tests (P < 0.0001) and a 17% increase in AP cases (P < 0.0001) occurred post-pandemic. These increases were associated with progressively increasing (P < 0.0001) hospital census. Census increase stemmed from higher admission through the emergency department (ED), and longer lengths of stay mostly for transfer patients, likely due to the greater capability of the new ED and changes in regional and local practice patterns post-pandemic. Higher census led to overcapacity, which has an inverted U relationship that peaked at 103% capacity for AP cases and 114% capacity for laboratory tests. Overcapacity led to a lower capability to perform clinical activities, particularly those related to surgical procedures. We conclude by suggesting parameters for optimal operations in the post-pandemic era.
Language	English
Publishing date	2024-03-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	2819382-9
ISSN	2374-2895
ISSN	2374-2895
DOI	10.1016/j.acpath.2024.100113
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Book ; Online: Intuitive Facial Animation Editing Based On A Generative RNN Framework

Berson, Eloïse / Soladié, Catherine / Stoiber, Nicolas

2020

Abstract: For the last decades, the concern of producing convincing facial animation has garnered great interest, that has only been accelerating with the recent explosion of 3D content in both entertainment and professional activities. The use of motion capture ... ...

Abstract	For the last decades, the concern of producing convincing facial animation has garnered great interest, that has only been accelerating with the recent explosion of 3D content in both entertainment and professional activities. The use of motion capture and retargeting has arguably become the dominant solution to address this demand. Yet, despite high level of quality and automation performance-based animation pipelines still require manual cleaning and editing to refine raw results, which is a time- and skill-demanding process. In this paper, we look to leverage machine learning to make facial animation editing faster and more accessible to non-experts. Inspired by recent image inpainting methods, we design a generative recurrent neural network that generates realistic motion into designated segments of an existing facial animation, optionally following user-provided guiding constraints. Our system handles different supervised or unsupervised editing scenarios such as motion filling during occlusions, expression corrections, semantic content modifications, and noise filtering. We demonstrate the usability of our system on several animation editing use cases.
Keywords	Computer Science - Graphics ; I.3.7
Subject code	629
Publishing date	2020-10-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Real-Time Cleaning and Refinement of Facial Animation Signals

Berson, Eloïse / Soladié, Catherine / Stoiber, Nicolas

2020

Abstract: With the increasing demand for real-time animated 3D content in the entertainment industry and beyond, performance-based animation has garnered interest among both academic and industrial communities. While recent solutions for motion-capture animation ... ...

Abstract	With the increasing demand for real-time animated 3D content in the entertainment industry and beyond, performance-based animation has garnered interest among both academic and industrial communities. While recent solutions for motion-capture animation have achieved impressive results, handmade post-processing is often needed, as the generated animations often contain artifacts. Existing real-time motion capture solutions have opted for standard signal processing methods to strengthen temporal coherence of the resulting animations and remove inaccuracies. While these methods produce smooth results, they inherently filter-out part of the dynamics of facial motion, such as high frequency transient movements. In this work, we propose a real-time animation refining system that preserves -- or even restores -- the natural dynamics of facial motions. To do so, we leverage an off-the-shelf recurrent neural network architecture that learns proper facial dynamics patterns on clean animation data. We parametrize our system using the temporal derivatives of the signal, enabling our network to process animations at any framerate. Qualitative results show that our system is able to retrieve natural motion signals from noisy or degraded input animation. Comment: ICGSP 2020: Proceedings of the 2020 The 4th International Conference on Graphics and Signal Processing
Keywords	Computer Science - Graphics ; Computer Science - Machine Learning ; I.3.7
Subject code	629
Publishing date	2020-08-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: RETRACTED: 60 Predicting chorioamnionitis using AI-based methods: a retrospective cohort study.

American journal of obstetrics and gynecology

2024 Volume 230, Issue 1S, Page(s) S46

Abstract: This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). This meeting abstract has been retracted at the request of the authors. The team determined further analysis ... ...

Abstract	This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). This meeting abstract has been retracted at the request of the authors. The team determined further analysis is warranted before the formal presentation of the results.
Language	English
Publishing date	2024-01-18
Publishing country	United States
Document type	Retraction of Publication
ZDB-ID	80016-8
ISSN	1097-6868 ; 0002-9378
ISSN (online)	1097-6868
ISSN	0002-9378
DOI	10.1016/j.ajog.2023.11.081
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Article: Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends.

Ghanem, Marc / Espinosa, Camilo / Chung, Philip / Reincke, Momsen / Harrison, Natasha / Phongpreecha, Thanaphong / Shome, Sayane / Saarunya, Geetha / Berson, Eloise / James, Tomin / Xie, Feng / Shu, Chi-Hung / Hazra, Debapriya / Mataraso, Samson / Kim, Yeasul / Seong, David / Chakraborty, Dipro / Studer, Manuel / Xue, Lei /

Marić, Ivana / Chang, Alan L / Tjoa, Erico / Gaudillière, Brice / Tawfik, Vivianne L / Mackey, Sean / Aghaeepour, Nima

Heliyon

2024 Volume 10, Issue 7, Page(s) e29050

Abstract: Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, ... ...

Abstract	Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field. Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test. Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning". Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.
Language	English
Publishing date	2024-04-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e29050
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Article ; Online: Quantitative estimate of cognitive resilience and its medical and genetic associations.

Phongpreecha, Thanaphong / Godrich, Dana / Berson, Eloise / Espinosa, Camilo / Kim, Yeasul / Cholerton, Brenna / Chang, Alan L / Mataraso, Samson / Bukhari, Syed A / Perna, Amalia / Yakabi, Koya / Montine, Kathleen S / Poston, Kathleen L / Mormino, Elizabeth / White, Lon / Beecham, Gary / Aghaeepour, Nima / Montine, Thomas J

Alzheimer's research & therapy

2023 Volume 15, Issue 1, Page(s) 192

Abstract: Background: We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer's disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain ... ...

Abstract	Background: We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer's disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain cognitively normal. However, CR traditionally is considered a binary trait, capturing only the most extreme examples, and is often inconsistently defined. Methods: This study addressed existing discrepancies and shortcomings of the current CR definition by proposing a framework for defining CR as a continuous variable for each neuropsychological test. The linear equations clarified CR's relationship to closely related terms, including cognitive function, reserve, compensation, and damage. Primarily, resilience is defined as a function of cognitive performance and damage from neuropathologic damage. As such, the study utilized data from 844 individuals (age = 79 ± 12, 44% female) in the National Alzheimer's Coordinating Center cohort that met our inclusion criteria of comprehensive lesion rankings for 17 neuropathologic features and complete neuropsychological test results. Machine learning models and GWAS then were used to identify medical and genetic factors that are associated with CR. Results: CR varied across five cognitive assessments and was greater in female participants, associated with longer survival, and weakly associated with educational attainment or APOE ε4 allele. In contrast, damage was strongly associated with APOE ε4 allele (P value < 0.0001). Major predictors of CR were cardiovascular health and social interactions, as well as the absence of behavioral symptoms. Conclusions: Our framework explicitly decoupled the effects of CR from neuropathologic damage. Characterizations and genetic association study of these two components suggest that the underlying CR mechanism has minimal overlap with the disease mechanism. Moreover, the identified medical features associated with CR suggest modifiable features to counteract clinical expression of damage and maintain cognitive function in older individuals.
MeSH term(s)	Humans ; Female ; Aged ; Aged, 80 and over ; Male ; Cognitive Dysfunction/diagnosis ; Apolipoprotein E4/genetics ; Neurodegenerative Diseases ; Alzheimer Disease/pathology ; Cognition
Chemical Substances	Apolipoprotein E4
Language	English
Publishing date	2023-11-06
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2506521-X
ISSN	1758-9193 ; 1758-9193
ISSN (online)	1758-9193
ISSN	1758-9193
DOI	10.1186/s13195-023-01329-z
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Article ; Online: In-silico generation of high-dimensional immune response data in patients using a deep neural network.

Fallahzadeh, Ramin / Bidoki, Neda H / Stelzer, Ina A / Becker, Martin / Marić, Ivana / Chang, Alan L / Culos, Anthony / Phongpreecha, Thanaphong / Xenochristou, Maria / De Francesco, Davide / Espinosa, Camilo / Berson, Eloise / Verdonk, Franck / Angst, Martin S / Gaudilliere, Brice / Aghaeepour, Nima

Cytometry. Part A : the journal of the International Society for Analytical Cytology

2022 Volume 103, Issue 5, Page(s) 392–404

Abstract: Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to ... ...

Abstract	Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness.
MeSH term(s)	Humans ; Neural Networks, Computer ; Machine Learning ; Proteomics
Language	English
Publishing date	2022-12-27
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2099868-5
ISSN	1552-4930 ; 0196-4763 ; 1552-4922
ISSN (online)	1552-4930
ISSN	0196-4763 ; 1552-4922
DOI	10.1002/cyto.a.24709
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Article ; Online: Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature.

Berson, Eloise / Sreenivas, Anjali / Phongpreecha, Thanaphong / Perna, Amalia / Grandi, Fiorella C / Xue, Lei / Ravindra, Neal G / Payrovnaziri, Neelufar / Mataraso, Samson / Kim, Yeasul / Espinosa, Camilo / Chang, Alan L / Becker, Martin / Montine, Kathleen S / Fox, Edward J / Chang, Howard Y / Corces, M Ryan / Aghaeepour, Nima / Montine, Thomas J

Nature communications

2023 Volume 14, Issue 1, Page(s) 4947

Abstract: Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and ... ...

Abstract	Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.
MeSH term(s)	Humans ; Alzheimer Disease/genetics ; Chromatin/genetics ; Biological Assay ; Cell Cycle ; Epigenesis, Genetic
Chemical Substances	Chromatin
Language	English
Publishing date	2023-08-16
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-40611-4
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