فصلنامه مهندسی پزشکی زیستی
سال یازدهم شماره 3 (پاییز 1396)

Anterior cruciate ligament (ACL) rupture is one of the most costly knee injuries, usually occurring to young athletes, often leading to functional instability, inability to return to previous levels of physical activity, and premature osteoarthritis (OA). The main function of ACL is controlling anterior tibia translation as well as axial tibia rotation. Therefore, patients with ACL deficiency (ACLD) have to use different compensatory mechanisms and kinematic changes to maintain their stability during different activities. The study aims to measure the reliability of knee kinematics and ground reaction force during stair negotiation. Fifteen participants with unilateral ACLD ascended 4-step staircase, where 8-10 reflective markers was inserted on each segment of lower extremity. Five-camera VICON system and 10-camera VICON system were used in the first and second phase of study, respectively. Intra-class correlation coefficient (ICC) and standard error of measurement (SEM) was calculated for each parameter in the knee events during stair climbing. The results showed high consistency of kinematic parameters and GRF components was handled through reliability and repeatability calculations. ICC (2,5) showed similar values in injured and healthy contralateral leg with the range of (0.59-0.98) for all knee joint rotation and GRF components.

Despite the advancements made in the tissue engineering, one of the obstacles in producing thick tissues is the means of oxygen transport to the deep layered cells of the engineered tissue and creating the network of veins inside the tissue. One way to overcome this problem is to create a microfluidic network of channels inside the porous scaffold. These channels can both enhance the oxygenation and produce a mold for the natural vessels created by the angiogenesis cells. In this paper the dissolved oxygen distribution inside a 2D scaffold, which contains bifurcation based microfluidic channels, has been simulated by the means of computational fluid dynamics. To achieve this, the liquid flow and oxygen transport equations have been solved with considerations to the boundary conditions and suitable parameters. The oxygen transport has been found for the static scaffold, and the scaffolds made from the 0 order to third order of bifurcation with a bifurcation angle of 45 degrees. The results have shown that the scaffold with the second order of bifurcation has a better oxygen distribution and also more free area for the cell proliferation, which is consistent with the references. Next, the bifurcation angle was reduced to 35 degrees for the second order scaffold which resulted in an increase in the non-hypoxic area. Generally, by designing optimized angle of bifurcation based channels, a significant area can be oxygenated, while there will be sufficient surface available for cell proliferations.

The use of genomic nucleotide sequences as biochemical signals in machine learning methods is possible by converting these sequences into numerical codes. This conversion results in an unrealistic increase in the dimension of the data and encounters some data analysis operations such as visualization and feature extraction with constraints. Therefore, one should use the dimensionality reduction technics in order to return the data to its real dimension. In this study, a deep autoencoder neural network has been used to reduce the dimension of binding site sequence data on the human genome. In order to determine whether the information of real data is preserved in compressed data, we perform a two-class classification using a support vector machine. The results show that information is almost entirely preserved in compression. Then, compressed data is used for visualization as well as feature selection by analysis of variance. The results show that the first, the tenth and eighth positions in the sequences are the most informative positions. While the majority of the previous works deal with gene expression data of microarrays and compare a few dimension reduction algorithms, this paper for the first time uses an autoencoder on nucleotide sequence data and provides a comprehensive comparison between the performance of the dimension reduction technics and machine learning algorithms.

In the most contact theories such as Hertz, DMT and JKR, which are the most practical contacts models, biological particles are considered as a spherical elastic particle, which is not the best assumption. In this assumption, the history of loadings are not considered in that the history of strains and stresses will not analyzed properly. Therefore, in the first part of this paper, three models of elastic in spherical geometry have been developed to the viscoelastic models. By simulations and comparing the results with the experimental data of MCF-10A (breast-cancer cell), which is derived by Atomic Force Microscopy, it is revealed that viscoelastic models are more accurate than elastic models in the force-indentation curves. Then, according to the fact that most bacteria's geometry is cylindrical, contact theory for a sphere and cylinder have been developed and simulated for three groups of nanobacteria (Epidermidis, SallyVirus, and Aureus). By comparing simulations results with experimental data we observe that elastic models are not reasonable and contacts radius in viscoelastic model are smaller than they were for elastic models.

Wireless body area networks (WBANs) are specific kinds of wireless sensor network which have been widely used in many areas, especially for health monitoring in the areas of health services and healthcare. Among the most important challenges concerning these networks are performance, high throughput and increasing network lifespan. One of the possible ways to increase network lifetime is to use energy harvesting possibility. In energy harvesting WBANs, the energy of the sensor nodes does not end, but upon reaching a threshold, the node goes into a power-consuming mode and becomes blocked and does not participate in the network operation until it reaches the required energy. In this paper, an energy-efficient routing protocol for energy harvesting WBANS is proposed. In this protocol, the medical sensor nodes on the patient's body have the ability of energy harvesting while the routing method and transmitter node are used to transfer data from sensor nodes to the sink. This method uses single hob routing when the distance between the sensor node and the sink is less than that of the sensor node to the transmitter node. Also, if emergency data is detected, a single hob routing is used to send data, otherwise, multi hob routing is used to do so. Also, to increase throughput during energy harvesting, the sensor node does not block, but sends the data to its nearest neighbor. This protocol has been able to improve network throughput and lifetime. We used energy harvesting to increase network lifetime and routing techniques to reduce energy consumption.

The complexities and the effects of inter-subject variations on the encoding of sounds are features of the brainstem processing. Examining such data based on linear analysis is not reliable, encouraging to take into account non-linear methods which are effective ways of explaining such non-stationary signals. The purpose of this study is to explore the behavior of the brainstem in response to complex auditory stimuli /da/ using Multifractal Detrended Fluctuation Analysis modified by Singular Value Decomposition (SVD), Adaptive Detrending (AD) and Empirical Mode Decomposition (EMD). Auditory brainstem responses to synthetic /da/ stimuli were recorded for 40 normal subjects with a mean age of 22.7 years. MFDFA is carried out on the s-ABR time series data to evaluate the variation of their complexity and multiscaling. To utilize optimal Detrending of s-ABR time series, AD, SVD and EMD algorithms are applied on time series. By computing the fluctuation function and evaluating scaling behavior, scaling exponents such as generalized Hurst exponent and multifractal spectrum are determined. Given results in this method indicate that underlying signal has non-stationary nature in small scales, but property of system is controlled by trend in large scales. There is a crossover at msec on the behavior of fluctuation function corresponding to dominant sinusoidal trend in all samples. The average of Hurst exponent is at 68% confidence interval in small scales msec. The -dependency of demonstrate that underlying data sets have multifractality nature and are almost due to long-range correlations. The width of singularity spectrum which is a measure of the signal complexity of underlying data in average equates to at confidence interval.

Variety of brain region function represent that interactions between different frequency bands, employ general mechanisms of neural communications. Moreover, a method which recently used for information encoding in the brain is phase synchronization that is a process by which two or more cyclic signals tends to oscillate with a repeating sequence of relative phase angle. Some evidence demonstrated the important role of phase synchronization in cognitive tasks. In this paper we investigated the role of phase synchronization in a new visual discrimination task. For this purpose we collected electroencephalography signals from fifteen subjects during a color discrimination task. The machine learning algorithm, support vector machine (SVM), was used to find out whether this criterion can distinguish two different colors in the mentioned task. The results show that classification accuracy of 75% is achieved using phase synchronization feature. Also efficient frequency bands and contribution of effective electrodes were shown.