Author

R. P. Dickson

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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Bioengineering· 1 Microbiology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:differential expression· 1 Approach:experimental assay· 1 Organism:human· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Assay:metabolomics· 1 Assay:metagenomics· 1

Frequent Co-Authors

K. R. Ward· 2 shared A. Pennington· 1 shared B. M. McCracken· 1 shared C. I. Colmenaro· 1 shared D. C. Leander· 1 shared E. Bisco· 1 shared G. B. Huffnagle· 1 shared H. Zhu· 1 shared

Activity by Year

2019-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersPathology

K. R. Ward

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A Novel Swine Model of the Acute Respiratory Distress Syndrome Using Clinically-Relevant Injury Exposures

M. H. Tiba, B. M. McCracken, D. C. Leander, C. I. Colmenaro, J. A. Nemzek, M. W. Sjoding, K. E. Konopka, T. L. Flott, J. S. VanEpps, R. Daniels, K. R. Ward, K. A. Stringer, R. P. Dickson

This paper develops a swine model of ARDS using clinically relevant injury exposures and shows it recapitulates human ARDS features.

bioRxiv · Jan 24, 2021Pathology

Application of an ecology-based analytic approach to discriminate signal and noise in low-biomass microbiome studies: whole lung tissue is the preferred sampling method for amplicon-based characterization of murine lung microbiota

J. M. Baker, K. J. Hinkle, R. A. McDonald, N. R. Falkowski, G. B. Huffnagle, R. P. Dickson

This study applies an ecology-based approach to identify signal from noise in low-biomass microbiome studies and finds whole lung tissue is better than BAL fluid for characterizing murine lung microbiota.

bioRxiv · Sep 8, 2020Microbiology

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable 2-dimensional gas chromatography device

M. Zhou, R. Sharma, H. Zhu, J. Li, S. Wang, E. Bisco, J. Massey, A. Pennington, M. Sjoding, R. P. Dickson, P. Park, R. Hyzy, L. Napolitano, K. R. Ward, X. Fan

This paper develops a portable 2D gas chromatography device with machine learning to diagnose ARDS from breath samples, achieving 87.1% accuracy.

bioRxiv · Feb 26, 2019Bioengineering