TY  - INPR
A1  - Jhutty, Suneet Singh
A1  - Boehme, Julia D.
A1  - Jeron, Andreas
A1  - Volckmar, Julia
A1  - Schultz, Kristin
A1  - Schreiber, Jens
A1  - Schughart, Klaus
A1  - Zhou, Kai
A1  - Steinheimer, Jan
A1  - Stöcker, Horst
A1  - Stegemann-Koniszewski, Sabine
A1  - Bruder, Dunja
A1  - Hernández-Vargas, Esteban A.
T1  - Tracking influenza a virus infection in the lung from hematological data with machine learning
T2  - bioRxiv
N2  - The tracking of pathogen burden and host responses with minimal-invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory Influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e. cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing purposes of the models. We show here that lung viral load, neutrophil counts, cytokines like IFN-γ and IL-6, and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models shows that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path towards improved tracking and monitoring of influenza infections and possibly other respiratory infections based on minimal-invasively obtained hematological parameters.
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/73024
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-730247
IS  - 2022.02.23.481638
ER  -