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Your search keyword '"Ricks, Keersten M."' showing total 6 results
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6 results on '"Ricks, Keersten M."'

1. SARS-CoV-2 Aerosol and Intranasal Exposure Models in Ferrets

Author

Zumbrun, Elizabeth E., primary, Zak, Samantha E., additional, Lee, Eric D., additional, Bowling, Philip A., additional, Ruiz, Sara I., additional, Zeng, Xiankun, additional, Koehler, Jeffrey W., additional, Delp, Korey L., additional, Bakken, Russel R., additional, Hentschel, Shannon S., additional, Bloomfield, Holly A., additional, Ricks, Keersten M., additional, Clements, Tamara L., additional, Babka, April M., additional, Dye, John M., additional, and Herbert, Andrew S., additional

Published
2023
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2. Exposure Route Influences Disease Severity in the COVID-19 Cynomolgus Macaque Model

Author

Bixler, Sandra L., primary, Stefan, Christopher P., additional, Jay, Alexandra N., additional, Rossi, Franco D., additional, Ricks, Keersten M., additional, Shoemaker, Charles J., additional, Moreau, Alicia M., additional, Zeng, Xiankun, additional, Hooper, Jay W., additional, Dyer, David N., additional, Frick, Ondraya M., additional, Koehler, Jeffrey W., additional, Kearney, Brian J., additional, DiPinto, Nina, additional, Liu, Jun, additional, Tostenson, Samantha D., additional, Clements, Tamara L., additional, Smith, Jeffrey M., additional, Johnson, Joshua A., additional, Berrier, Kerry L., additional, Esham, Heather L., additional, Delp, Korey L., additional, Coyne, Susan R., additional, Bloomfield, Holly A., additional, Kuehnert, Paul A., additional, Akers, Kristen, additional, Gibson, Kathleen M., additional, Minogue, Timothy D., additional, Nalca, Aysegul, additional, and Pitt, Margaret L. M., additional

Published
2022
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3. A SARS-CoV-2 Spike Ferritin Nanoparticle Vaccine Is Protective and Promotes a Strong Immunological Response in the Cynomolgus Macaque Coronavirus Disease 2019 (COVID-19) Model

Author

Johnston, Sara C., primary, Ricks, Keersten M., additional, Lakhal-Naouar, Ines, additional, Jay, Alexandra, additional, Subra, Caroline, additional, Raymond, Jo Lynne, additional, King, Hannah A. D., additional, Rossi, Franco, additional, Clements, Tamara L., additional, Fetterer, David, additional, Tostenson, Samantha, additional, Cincotta, Camila Macedo, additional, Hack, Holly R., additional, Kuklis, Caitlin, additional, Soman, Sandrine, additional, King, Jocelyn, additional, Peachman, Kristina K., additional, Kim, Dohoon, additional, Chen, Wei-Hung, additional, Sankhala, Rajeshwer S., additional, Martinez, Elizabeth J., additional, Hajduczki, Agnes, additional, Chang, William C., additional, Choe, Misook, additional, Thomas, Paul V., additional, Peterson, Caroline E., additional, Anderson, Alexander, additional, Swafford, Isabella, additional, Currier, Jeffrey R., additional, Paquin-Proulx, Dominic, additional, Jagodzinski, Linda L., additional, Matyas, Gary R., additional, Rao, Mangala, additional, Gromowski, Gregory D., additional, Peel, Sheila A., additional, White, Lauren, additional, Smith, Jeffrey M., additional, Hooper, Jay W., additional, Michael, Nelson L., additional, Modjarrad, Kayvon, additional, Joyce, M. Gordon, additional, Nalca, Aysegul, additional, Bolton, Diane L., additional, and Pitt, Margaret L. M., additional

Published
2022
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4. Antibody Response to SARS-CoV-2 Infection and Vaccination in COVID-19-Naïve and Experienced Individuals

Author

Uprichard, Susan L., primary, O’Brien, Amornrat, additional, Evdokimova, Monika, additional, Rowe, Cynthia L., additional, Joyce, Cara, additional, Hackbart, Matthew, additional, Cruz-Pulido, Yazmin E., additional, Cohen, Courtney A., additional, Rock, Michelle L., additional, Dye, John M., additional, Kuehnert, Paul, additional, Ricks, Keersten M., additional, Casper, Marybeth, additional, Linhart, Lori, additional, Anderson, Katrina, additional, Kirk, Laura, additional, Maggiore, Jack A., additional, Herbert, Andrew S., additional, Clark, Nina M., additional, Reid, Gail E., additional, and Baker, Susan C., additional

Published
2022
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5. Transcriptomic Analysis Reveals Host miRNAs Correlated with Immune Gene Dysregulation during Fatal Disease Progression in the Ebola Virus Cynomolgus Macaque Disease Model

Author

Stefan, Christopher P., primary, Arnold, Catherine E., additional, Shoemaker, Charles J., additional, Zumbrun, Elizabeth E., additional, Altamura, Louis A., additional, Douglas, Christina E., additional, Taylor-Howell, Cheryl L., additional, Graham, Amanda S., additional, Delp, Korey L., additional, Blancett, Candace D., additional, Ricks, Keersten M., additional, Olschner, Scott P., additional, Shamblin, Joshua D., additional, Wollen, Suzanne E., additional, Zelko, Justine M., additional, Bloomfield, Holly A., additional, Sprague, Thomas R., additional, Esham, Heather L., additional, and Minogue, Timothy D., additional

Published
2021
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6. Application of a Machine Learning-Based Classification Approach for Developing Host Protein Diagnostic Models for Infectious Disease.

Author

Scherr TF, Douglas CE, Schaecher KE, Schoepp RJ, Ricks KM, and Shoemaker CJ

Abstract

In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive at an informative diagnostic outcome. This report describes a machine learning (ML)-based classification workflow that is intended as a template for researchers seeking to apply ML approaches for developing host-based infectious disease biomarker classifiers. As an example, we built a classification model that could accurately distinguish between three disease etiology classes: bacterial, viral, and normal in human sera using host protein biomarkers of known diagnostic utility. After collecting protein data from known disease samples, we trained a series of increasingly complex Auto-ML models until arriving at an optimized classifier that could differentiate viral, bacterial, and non-disease samples. Even when limited to a relatively small training set size, the model had robust diagnostic characteristics and performed well when faced with a blinded sample set. We present here a flexible approach for applying an Auto-ML-based workflow for the identification of host biomarker classifiers with diagnostic utility for infectious disease, and which can readily be adapted for multiple biomarker classes and disease states.

Published
2024
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