Biomedical Engineering articles list

Two-phase flow in microfluidic-chip design of hydrodynamic filtration for cell particle sorting

As one of the flow-based passive sorting, the hydrodynamic filtration using a microfluidic-chip has shown to effectively separate into different sizes of subpopulations from cell or particle suspensions. Its model framework involving two-phase Newtonian or generalized Newtonian fluid (GNF) was developed, by performing the complete analysis of laminar flow and complicated networks of main and multiple branch channels. To predict rigorously what occurs in flow fields, we estimated pressure drop, velocity profile, and the ratio of the flow fraction at each branch point, in which the analytical model was validated with numerical flow simulations. As a model fluid of the GNF, polysaccharide solution based on Carreau type was examined. The objective parameters aiming practical channel design include the number of the branches and the length of narrow section of each branch for arbitrary conditions. The flow fraction and the number of branches are distinctly affected by the viscosity ratio between feed and side flows. As the side flow becomes more viscous, the flow fraction increases but the number of branches decreases, which enables a compact chip designed with fewer branches being operated under the same throughput. Hence, our rational design analysis indicates the significance of constitutive properties of each stream.

Myung-Suk Chun

Sorting of human mesenchymal stem cells by applying optimally designed microfluidic chip filtration

Human bone marrow-derived mesenchymal stem cells (hMSCs) consist of heterogeneous subpopulations with different multipotent properties: small and large cells with high and low multipotency, respectively. Accordingly, sorting out a target subpopulation from the others is very important to increase the effectiveness of cell-based therapy. We performed flow-based sorting of hMSCs by using optimally designed microfluidic chips based on the hydrodynamic filtration (HDF) principle. The chip was designed with the parameters rigorously determined by the complete analysis of laminar flow for flow fraction and complicated networks of main and multi-branched channels for hMSCs sorting into three subpopulations: small (<25>40 μm) cells. By focusing with a proper ratio between main and side flows, cells migrate toward the sidewall due to a virtual boundary of fluid layers and enter the branch channels. This opens the possibility of sorting stem cells rapidly without damage. Over 86% recovery was achieved for each population of cells with complete purity in small cells, but the sorting efficiency of cells is slightly lower than that of rigid model particles, due to the effect of cell deformation. Finally, we confirmed that our method could successfully fractionate the three subpopulations of hMSCs by analyzing the surface marker expressions of cells from each outlet.

Myung-Suk Chun

Efficient detection of escherichia coli o157:h7 using a reusable microfluidic chip embedded with antimicrobial peptide-labeled beads

The ability of antimicrobial peptides (AMPs) for effective binding to multiple target microbes has drawn lots of attention as an alternative to antibodies for detecting whole bacteria. We investigated pathogenic Escherichia coli (E. coli) detection by applying a microfluidic based biosensing device embedded with AMP-labeled beads. According to a new channel design, our device is reusable by the repeated operation of detection and regeneration modes, and the binding rate is more enhanced due to even distribution of the bacterial suspension inside the chamber by implementing influx side channels. We observed higher binding affinity of pathogenic E. coli O157:H7 for AMP-labeled beads than nonpathogenic E. coli DH5α, and the fluorescence intensity of pathogenic E. coli was about 3.4 times higher than the nonpathogenic one. The flow rate of bacterial suspension should be applied above a certain level for stronger binding and rapid detection by attaining a saturation level of detection within a short time of less than 20 min. A possible improvement in the limit of detection in the level of 10 cells per mL for E. coli O157:H7 implies that the AMP-labeled beads have high potential for the sensitive detection of pathogenic E. coli at an appropriate flow rate.

Myung-Suk Chun

Acoustic feedback cancellation in efficient hearing aids using genetic algorithm

Many people are distracted from the normal lifestyle, because of the hearing loss they have. Most of them do not use the hearing aids due to various discomforts in wearing them. The main and the foremost problem available in it is; the device introduces unpleasant whistling sounds, caused by the changing environmental noise, which is faced by the user daily. This paper describes the development of an algorithm, which focuses on the adaptive feedback cancellation, that improves the listening effort of the user. The genetic algorithm is one of the computational techniques, that is used in enhancing the above features. The performance can also be compared with other comprehensive analysis methods, to evaluate its standards.

Jayanthi G

Estimation of snr based adaptive-feedback equalizers for feedback control in hearing aids

Despite the evolution of modern technology, the users of hearing aids do not realize the persistence of feedback, while wearing the device until the condition becomes worse. The feedback cancellation algorithms, instead of cancelling the acoustic feedback, limits speech intelligibility. The paper presents a novel method for estimation of SNR based adaptive-feedback equalizers (SBAFE) algorithm to develop an optimized hearing aid for the feedback less sound transmission and achieving better speech discrimination. The data gathered for the optimization is visualized and compared with the traditional technology, which provides the subjective and objective quality of the hearing aids.

Jayanthi G

Early diagnosis model of alzheimer’s disease based on hybrid meta heuristic with regression based multi feed forward neural network

Alzheimer Disease is a chronic neurological brain disease. Early diagnosis of Alzheimer illness may the prevent the occurrence of memory cellular injury. Neuropsychological tests are commonly used to diagnose Alzheimer’s disease. The above technique, has a limited specificity and sensitivity. This article suggests solutions to this issue an early diagnosis model of Alzheimer’s disease based on a hybrid meta-heuristic with a multi-feed-forward neural network. The proposed Alzheimer’s disease detection model includes four major phases: pre-processing, feature extraction, feature selection and classification (disease detection). Initially, the collected raw data is pre-processed using the SPMN12 package of MATLAB. Then, from the pre-processed data, the statistical features (mean, median and standard deviation) and DWT are extracted. Then, from the extracted features, the optimal features are selected using the new Hybrid Sine cosine firefly (HSCAFA). This HSCAFA is a conceptual improvement of standard since cosine optimization and firefly optimization algorithm, respectively. Finally, the disease detection is accomplished via the new regression- based multi-faith neighbors’ network (MFNN). The final detected outcome is acquired from regression-based MFNN. The proposed methodology is performed on the PYTHON platform and the performances are evaluated by the matrices such as precision, recall, and accuracy.

Dr. Rajasekhar Butta