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Article ; Online: Data-Driven Modeling of Pregnancy-Related Complications.

Espinosa, Camilo / Becker, Martin / Marić, Ivana / Wong, Ronald J / Shaw, Gary M / Gaudilliere, Brice / Aghaeepour, Nima / Stevenson, David K

Trends in molecular medicine

2021 Volume 27, Issue 8, Page(s) 762–776

Abstract: A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data ... ...

Abstract	A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.
MeSH term(s)	Biomarkers ; Computational Biology/methods ; Data Mining ; Disease Susceptibility ; Female ; Genomics/methods ; Humans ; Machine Learning ; Metabolomics/methods ; Models, Biological ; Pregnancy ; Pregnancy Complications/diagnosis ; Pregnancy Complications/etiology ; Pregnancy Complications/metabolism ; Pregnancy Outcome ; Proteomics/methods ; Reproductive Physiological Phenomena ; Risk Assessment ; Risk Factors
Chemical Substances	Biomarkers
Language	English
Publishing date	2021-02-08
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2036490-8
ISSN	1471-499X ; 1471-4914
ISSN (online)	1471-499X
ISSN	1471-4914
DOI	10.1016/j.molmed.2021.01.007
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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: Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo.

Wolf, Julian / Rasmussen, Ditte K / Sun, Young Joo / Vu, Jennifer T / Wang, Elena / Espinosa, Camilo / Bigini, Fabio / Chang, Robert T / Montague, Artis A / Tang, Peter H / Mruthyunjaya, Prithvi / Aghaeepour, Nima / Dufour, Antoine / Bassuk, Alexander G / Mahajan, Vinit B

Cell

2023 Volume 186, Issue 22, Page(s) 4868–4884.e12

Abstract: Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by ... ...

Abstract	Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson's disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.
MeSH term(s)	Humans ; Aging/metabolism ; Aqueous Humor/chemistry ; Artificial Intelligence ; Biopsy ; Liquid Biopsy ; Proteomics ; Parkinson Disease/diagnosis
Language	English
Publishing date	2023-10-19
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	187009-9
ISSN	1097-4172 ; 0092-8674
ISSN (online)	1097-4172
ISSN	0092-8674
DOI	10.1016/j.cell.2023.09.012
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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: 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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Article ; Online: Prediction of neuropathologic lesions from clinical data.

Phongpreecha, Thanaphong / Cholerton, Brenna / Bukhari, Syed / Chang, Alan L / De Francesco, Davide / Thuraiappah, Melan / Godrich, Dana / Perna, Amalia / Becker, Martin G / Ravindra, Neal G / Espinosa, Camilo / Kim, Yeasul / Berson, Eloise / Mataraso, Samson / Sha, Sharon J / Fox, Edward J / Montine, Kathleen S / Baker, Laura D / Craft, Suzanne /

White, Lon / Poston, Kathleen L / Beecham, Gary / Aghaeepour, Nima / Montine, Thomas J

Alzheimer's & dementia : the journal of the Alzheimer's Association

2023 Volume 19, Issue 7, Page(s) 3005–3018

Abstract: Introduction: Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life.: Methods: This study employed statistical tools and machine learning to predict 17 neuropathologic ... ...

Abstract	Introduction: Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life. Methods: This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites. A similar study was performed for predicting Alzheimer's disease (AD) neuropathologic change without specific comorbidities. Results: Prediction results show high performance for certain lesions that match or exceed that of research annotation. Neurodegenerative comorbidities in addition to AD neuropathologic change resulted in compounded, but disproportionate, effects across cognitive domains as the comorbidity number increased. Discussion: Certain clinical features could be strongly associated with multiple neurodegenerative diseases, others were lesion-specific, and some were divergent between lesions. Our approach could benefit clinical research, and genetic and biomarker research by enriching cohorts for desired lesions.
MeSH term(s)	Humans ; Alzheimer Disease/pathology ; Comorbidity ; Neuropathology ; Biomarkers
Chemical Substances	Biomarkers
Language	English
Publishing date	2023-01-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2211627-8
ISSN	1552-5279 ; 1552-5260
ISSN (online)	1552-5279
ISSN	1552-5260
DOI	10.1002/alz.12921
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Article ; Online: Cross-species comparative analysis of single presynapses.

Scientific reports

2023 Volume 13, Issue 1, Page(s) 13849

Abstract: Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning ... ...

Abstract	Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.
MeSH term(s)	Humans ; Animals ; Mice ; Synaptic Transmission ; Brain ; Cerebral Cortex ; Lipid Metabolism ; Macaca
Language	English
Publishing date	2023-08-24
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-40683-8
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Article ; Online: Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

NPJ digital medicine

2023 Volume 6, Issue 1, Page(s) 171

Abstract: Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for ... ...

Abstract	Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.
Language	English
Publishing date	2023-09-28
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-023-00911-x
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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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Book ; Online: State of the technical and socioeconomic indicators of the main agricultural systems of the flat highlands.

Rodríguez, Gonzalo / González, Carolina / Avila, Flavio / Cubillo, Roger / Espinosa, Camilo / Rodrigues, Gerardo / Pérez, Salomón

2018

Keywords	agricultural production ; indicators
Language	English
Publishing date	2018-07-26T15:29:42Z
Publisher	International Center for Tropical Agriculture
Publishing country	fr
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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