Abstract: Red Blood Cells (RBCs) or erythrocytes tend to form chain-like aggregates under low shear rate called rouleaux. This is a reversible process and rouleaux disaggregate in high shear rates. Therefore, RBCs aggregation occurs in the microcirculation where low shear rates are present but does not occur under normal physiological conditions in large arteries. Numerical modeling of RBCs interactions is fundamental in analytical models of a blood flow in microcirculation. Population Balance Modeling (PBM) is particularly useful for studying problems where particles agglomerate and break in a two phase flow systems to find flow characteristics. In this method, the elementary particles lose their individual identity due to continuous destructions and recreations by break-up and agglomeration. The aim of this study is to find RBCs aggregation in a dynamic situation. Simplified PBM was used previously to find the aggregation rate on a static observation of the RBCs aggregation in a drop of blood under the microscope. To find aggregation rate in a dynamic situation we propose an experimental set up testing RBCs sedimentation. In this test, RBCs interact and aggregate to form rouleaux. In this configuration, disaggregation can be neglected due to low shear stress. A high-speed camera is used to acquire video-microscopic pictures of the process. The sizes of the aggregates and velocity of sedimentation are extracted using an image processing techniques. Based on the data collection from 5 healthy human blood samples, the aggregation rate was estimated as 2.7x103(±0.3 x103) 1/s.
Abstract: Red blood cells (RBCs) are among the most
commonly and intensively studied type of blood cells in cell biology.
Anemia is a lack of RBCs is characterized by its level compared to
the normal hemoglobin level. In this study, a system based image
processing methodology was developed to localize and extract RBCs
from microscopic images. Also, the machine learning approach is
adopted to classify the localized anemic RBCs images. Several
textural and geometrical features are calculated for each extracted
RBCs. The training set of features was analyzed using principal
component analysis (PCA). With the proposed method, RBCs were
isolated in 4.3secondsfrom an image containing 18 to 27 cells. The
reasons behind using PCA are its low computation complexity and
suitability to find the most discriminating features which can lead to
accurate classification decisions. Our classifier algorithm yielded
accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor
(K-NN) algorithm, support vector machine (SVM), and neural
network RBFNN, respectively. Classification was evaluated in highly
sensitivity, specificity, and kappa statistical parameters. In
conclusion, the classification results were obtained within short time
period, and the results became better when PCA was used.
Abstract: A numerical method is developed for simulating
the motion of particles with arbitrary shapes in an effectively
infinite or bounded viscous flow. The particle translational and
angular motions are numerically investigated using a fluid-structure
interaction (FSI) method based on the Arbitrary-Lagrangian-Eulerian
(ALE) approach and the dynamic mesh method (smoothing and
remeshing) in FLUENT ( ANSYS Inc., USA). Also, the effects of
arbitrary shapes on the dynamics are studied using the FSI method
which could be applied to the motions and deformations of a single
blood cell and multiple blood cells, and the primary thrombogenesis
caused by platelet aggregation. It is expected that, combined with a
sophisticated large-scale computational technique, the simulation
method will be useful for understanding the overall properties of blood
flow from blood cellular level (microscopic) to the resulting
rheological properties of blood as a mass (macroscopic).