We use machine learning, mathematical modeling and experimental data collection to explore the behavior of blood cells (red, white, platelets) in inflammatory scenarios (trauma, surgery, infectious disease, etc.).
These models are used to analyse how disease and inflammation affect blood cell growth and senescence mechanisms (production, vesiculation, clearance, etc.). We also use these approaches to develop clinical diagnostic and prognostic tools.
Below are some key publications in this theme (click image to access paper). A full list of publications can be found here.
Some Upcoming Studies
Analysis of blood production dynamics in patients with SARS-CoV-2 infection.
Identification of inflammatory phenotypes during recovery from elective cardiac surgery.
Agent-based modelling of red blood cell dynamics to predict emerging hematologic disease.
Association of red blood cell distribution width with mortality risk in hospitalized adults with SARS-CoV-2 infection.
JAMA Network Open. 2020.
Data-driven physiologic thresholds for iron deficiency associated with hematologic decline.
American Journal of Hematology. 2020
Diminished reactive hematopoiesis and cardiac inflammation in a mouse model of recurrent myocardial infarction.
Journal of the American College of Cardiology. 2020
Boston Globe: https://www.bostonglobe.com/2020/09/23/nation/mgh-study-says-routine-blood-test-may-predict-covid-19-hospital-death-risk/
Web MD: https://www.webmd.com/lung/news/20200923/blood-test-could-spot-those-at-highest-risk-for-severe-covid-19?#1