A total of 40 subjects will be recruited for participation in this study. 20 subjects (10 males and 10 females) will be randomized to the active group (those receiving re-infusion of autologous blood) and 20 subjects (10 males and 10 females) will be randomized to the placebo group (receiving NS infusion).
Autologous blood transfusion is a major problem in a wide range of competitive sports. Methods with increased sensitivity, specificity, and feasibility are needed to identify athletes who cheat in this manner and compromise their health and the integrity of their sports in general. Complete blood counts (CBC) offer routine high-resolution assessment of the current hematologic status of individuals, providing estimates of a number of blood characteristics, such as the total hemoglobin concentration in the blood (HGB) and the volume fraction of cells in the blood (HCT). These CBC components are homeostatically controlled by the carefully regulated dynamic processes of red blood cell (RBC) production in and release from the bone marrow, RBC maturation in the peripheral circulation over the course of the \~100-day RBC lifespan, and clearance and recycling of senescent cells. Any significant perturbation to the circulating population of RBCs, like autologous transfusion, will immediately trigger compensatory modulation of one or more of these dynamic processes. The investigators believe quantification of these underlying dynamic processes will enable us to detect autologous transfusion. These dynamic RBC processes cannot currently be measured directly, but novel mathematical modeling enables their inference from routine complete blood and reticulocyte counts. The investigators propose to test the ability of modeled RBC dynamics to identify instances of autologous blood transfusion.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
BASIC_SCIENCE
Masking
DOUBLE
Enrollment
40
Participants will receive autologous transfusion on Day 21
Participants will receive saline on Day 21
University of Utah Center for Clinical & Translational Science
Salt Lake City, Utah, United States
Hematocrit
This will be used within a mathematical model to define the volume (v) and hemoglobin (h) dynamics of a typical RBC as deterministic functions (f) and random fluctuations in the rates of these changes over time (ζ) (Patel, Patel, \& Higgins, 2015).
Time frame: 8 weeks
Hemoglobin
This will be used within a mathematical model to define the volume (v) and hemoglobin (h) dynamics of a typical RBC as deterministic functions (f) and random fluctuations in the rates of these changes over time (ζ) (Patel, Patel, \& Higgins, 2015).
Time frame: 8 weeks
Reticulocyte count
This will be used within a mathematical model to define the volume (v) and hemoglobin (h) dynamics of a typical RBC as deterministic functions (f) and random fluctuations in the rates of these changes over time (ζ) (Patel, Patel, \& Higgins, 2015).
Time frame: 8 weeks
Mean corpuscular volume
This will be used within a mathematical model to define the volume (v) and hemoglobin (h) dynamics of a typical RBC as deterministic functions (f) and random fluctuations in the rates of these changes over time (ζ) (Patel, Patel, \& Higgins, 2015).
Time frame: 8 weeks
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