Author

A. Pise

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1

Frequent Co-Authors

B. Patterson· 2 shared E. B. Francisco· 2 shared E. Long· 2 shared H. Rodrigues· 2 shared J. Guevara-Coto· 2 shared J. Mora· 2 shared P. Parikh· 2 shared R. Yogendra· 2 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersImmunology

B. Patterson

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2 papers

Recent·Most cited

Persistence of SARS CoV-2 S1 Protein in CD16+ Monocytes in Post-Acute Sequelae of COVID-19 (PASC) Up to 15 Months Post-Infection

B. Patterson, E. B. Francisco, R. Yogendra, E. Long, A. Pise, H. Rodrigues, E. Hall, M. Herrera, P. Parikh, J. Guevara-Coto, T. Triche, P. Scott, S. Hekmati, D. Maglinte, X. Chang, , J. Mora

This study finds SARS-CoV-2 S1 protein in non-classical monocytes in PASC patients up to 15 months post-infection, but not full-length viral RNA.

bioRxiv · Jun 25, 2021Immunology

Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning

B. Patterson, J. Guevara-Coto, R. Yogendra, E. B. Francisco, e. long, A. Pise, H. Rodrigues, P. Parikh, J. Mora, R. A. Mora-Rodriguez

The paper uses machine learning to predict and distinguish phases of COVID-19 severity and chronicity based on immune profiles from different patient groups.

bioRxiv · Dec 16, 2020Immunology