Flavonoids and Other Polyphenols Act as Epigenetic Modifiers in Breast Cancer.

Breast cancer is a common cancer that occurs due to different epigenetic alterations and genetic mutations. Various epidemiological studies have demonstrated an inverse correlation between breast cancer incidence and flavonoid intake. The anti-cancer action of flavonoids, a class of polyphenolic compounds that are present in plants, as secondary metabolites has been a major topic of research for many years. Our review analysis demonstrates that flavonoids exhibit anti-cancer activity against breast cancer occurring in different ethnic populations. Breast cancer subtype and menopausal status are the key factors in inducing the flavonoid\u27s anti-cancer action in breast cancer. The dose is another key factor, with research showing that approximately 10 mg/day of isoflavones is required to inhibit breast cancer occurrence. In addition, flavonoids also influence the epigenetic machinery in breast cancer, with research demonstrating that epigallocatechin, genistein, and resveratrol all inhibited DNA methyltransferase and altered chromatin modification in breast cancer. These flavonoids can induce the expression of different tumor suppressor genes that may contribute to decreasing breast cancer progression and metastasis. Additional studies are required to confirm the contribution of epigenetic modifications by flavonoids to breast cancer prevention

Confounding variables can degrade generalization performance of radiological deep learning models

Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis in real-world clinical settings, we must verify their ability to generalize across a variety of hospital systems. A cross-sectional design was used to train and evaluate pneumonia screening CNNs on 158,323 chest x-rays from NIH (n=112,120 from 30,805 patients), Mount Sinai (42,396 from 12,904 patients), and Indiana (n=3,807 from 3,683 patients). In 3 / 5 natural comparisons, performance on chest x-rays from outside hospitals was significantly lower than on held-out x-rays from the original hospital systems. CNNs were able to detect where an x-ray was acquired (hospital system, hospital department) with extremely high accuracy and calibrate predictions accordingly. The performance of CNNs in diagnosing diseases on x-rays may reflect not only their ability to identify disease-specific imaging findings on x-rays, but also their ability to exploit confounding information. Estimates of CNN performance based on test data from hospital systems used for model training may overstate their likely real-world performance

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MotivationElectronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.ResultsWe present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.Availability and implementationPatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online

Correction to: Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits

Erratum for Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits. [BMC Med Genomics. 2019

Quantifying changes in climate and surface elevation of polar ice sheets during the last glacial-interglacial transition

Thesis (Ph.D.)--University of Washington, 2022This dissertation describes three research projects investigating changes in polar climate and the ice sheets during the last deglaciation. The first project, Chapter 2, reconstructs the past 20,000 years of Greenland temperature and precipitation to learn about their relationship and influences on the ice sheet. The reconstruction method, paleoclimate data assimilation, uses oxygen-isotope ratios of ice and accumulation rates from long ice-core records and extends this information to all locations across Greenland using spatial relationships derived from a transient climate-model simulation. Evaluations against out-of-sample proxy records indicate that the reconstructions capture the climate history at locations without ice-core records. The reconstructions show that the relationship between precipitation and temperature is frequency dependent and spatially variable, suggesting that thermodynamic scaling methods commonly used in ice-sheet modeling are overly simplistic. Overall, the results demonstrate that paleoclimate data assimilation is a useful tool for reconstructing the spatial and temporal patterns of past climate on timescales relevant to ice sheets. To learn how these climate reconstructions relate to the behavior of the ice sheet, we must also reconstruct the history of the ice sheet. Most observational data of the past ice sheet geometry, however, is at the margins of the ice sheet, while the ice core climate records are located in the interior. The second project, Chapter 3, investigates a common paleoaltimetry method that reconstructs elevation from temperature records. This method suggests the climate and elevation signals contained within an ice-core temperature record can be disentangled by comparing two proxy locations with similar climates. The difference between the records is assumed to be due to elevation, which is estimated by scaling the temperature difference by a lapse rate. I investigate the errors associated with this approach using the Antarctic ice sheet during the Last Glacial Maximum as a case study. Using an ensemble of climate simulations from global circulation models (GCMs), I extract modeled temperatures at locations of real ice cores. The errors are on the order of hundreds of meters and result from spatial heterogeneity in non-adiabatic temperature change, which itself stems in part from elevation-induced atmospheric circulation change. These findings suggest that caution is needed when interpreting temperature-based paleoaltimetry results for ice sheets. The third project, Chapter 4, seeks to learn about the elevation and climate signals contained within the WAIS Divide ice core temperature record by investigating whether they are consistent with accumulation rate reconstructions and annual layer thickness data at the ice core site. The difference in temperature change between West and East Antarctic ice core sites during the last deglacial period is about 6 °C. If this were due to differential elevation change at the sites, then the WAIS Divide ice core site would have been about 400 m higher during the Last Glacial Maximum. Using an ice-flow model, I determine that this elevation change is not consistent with published accumulation rate reconstructions and the annual layer thickness data from the WAIS Divide ice core site. Three factors may explain this inconsistency: the spatial heterogeneity in non-adiabatic temperature changes during the deglaciation, assumptions in the accumulation rate reconstructions, and assumptions in the ice-flow model. Future investigations into these factors may lead to a more consistent understanding of Antarctic climate and interior ice sheet changes during the last deglaciation

Multimodal Deep Learning to Enhance the Practice of Radiology

Machine learning and deep learning have demonstrated extraordinary potential in many disciplines and medical research studies. Deep learning algorithms for image recognition have rapidly evolved over the past decade, and there is interest in applying these new algorithms to pathology and radiology. This thesis reconstructs the translational gap for deep learning in clinical radiology. I provide practical tools for collecting more data and testing human enhancement and demonstrate symbiotic implementations for clinical practice. I then raise new theoretical issues to the eventual deployment of these technologies; specifically, how to incorporate these technologies with existing medical information and train models that are suitable for widespread deployment. Deep learning requires an abundance of data and algorithms are commonly tested in isolation. The lack of clinical validation beclouds the question of when and how to deploy these technologies. The Computer Aided Note and Diagnosis Interface (CANDI) uses a browser- based platform for distributed generation of annotation data and performs randomized controlled trials for Computer Aided Diagnosis (CAD) utilities: 1) similar image search, 2) diagnosis prediction, and 3) pathology localization. The most salient strength of computers is their rapid processing speed, which is particularly useful in the context of managing acute neurologic diseases. The second study develops a Convolutional Neural Network (CNN) that scans CT radiographs for evidence of critical pathology with below human accuracy but much more rapidly. This CNN can triage radiographs to expedite expert interpretation of time-sensitive cases. Finally, I use statistical approaches to investigate how covariates are encoded by deep learning’s “black box” and question the impact of this behavior. Using a single-site dataset with extensive metadata descriptors, I show how models can be improved by encoding biological and hospital process factors associated with hip fracture, at least when tested in isolation. But simulations of clinicians combining image-model predictions with overlapping patient data reveal limitations for CAD applications. Using multi-site dataset combinations, we show that heterogeneous patient populations and technical differences in image acquisition can benefit a model’s performance on internal test data but subvert a model's external validity. We use clinical trial design to train a model that is less stellar on internal testing but more robust for worldwide deployment

Interview with Lillie Badgeley and Myra Daniel

IN PROCESSING Interviews with extension members and agents throughout the country documenting the history and development of the extension movement in the U.S. The interviews describe homemaking, child bearing and family management in the small towns and rural areas where they live. They also discuss the role of extension homemakers groups in their lives