IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING
Year:2019 | Volume:13 | Issue:1 |Papers Count:7

The large surface area of the lung with its thin air-blood barrier is exposed to particles in the inhaled air. In this condition, if the inhaled pollutant aerosols are toxic, the particle-lung interaction may cause serious hazards and injuries on human’ s health. On the otherhand, these interactions are also used for drug delivery to human’ s body. In either case, an accurate estimation of dose and sites of deposition in the respiratory tract is fundamental for understanding mechanobiology of these deseases. Obtaining in vivo data of particle transportation in the human lung experimentally is often diffi cult. But, computational fl uid-particle dynamics (CFPD) has provided the possibility to gain aerosol transportion data in realistic airway geometries. Aerosols deposition in the human lung mainly occurs due to combination of inertial impaction, gravitational sedimentation and diffusion. For particles with aerodynamic size of 0. 5 to 5 micron and in inhalation state of lung, the main mechanisms of particle deposition in distal parts of human’ s respiratory system are sedimentation, due to gravity and convective transfer due to wall movement. In this study, deposition of particles in distal part of human respiratory system, specifically 18th generation, has been modeled for two gravity conditions, normal and absent gravity, by assuming isotropic displacements on the walls and with the rate of 1 (mg/sec) for particle input. By analyzing the results, it was determined that the amount of particle deposition in distal airways reduces a great amount by omitting the effect of gravitational force because, particles smaller than 5 micron can penetrate into that airways. Particles with the diameter of 5 micron deposit under the effect of inertial impact, whereas this mechanism occurs mostly in airways with large and medium diameters and also, by sedimentation which occurs in the distal lung.

Noise removal is one of the most important steps in digital image processing. Cone beam computed tomography (CBCT) is increasingly utilized in maxillofacial and dental imaging. Compared to conventional CT, CBCT images have diffrent noise and artifacts due to much less applied dose and their reconstruction algorithm. Therefore, the use of noise reduction techniques in these images is necessary to increase the signal-to-noise ratio. In this paper, the independent component analysis (ICA) method has been used to seperate noise from CBCT images and three different ICA algorithms, NG-FICA, ERICA and FastICA were investigated. In addition, two powerful noise reduction method, 2D discrete wavelet thresholding and optimized anisotropic diffusion filter is used to evaluate the results. Our proposed method has been validated on 12 different images in the presence of Gaussian and Spectral noise and the results are evaluated using processing time criteria, PSNR, MSE and SSIM. The results show that the ICA methods have advantage in noise reduction from CBCT images compared to the other noise reduction methods and among the three studied ICA algorithms, the NG-FICA algorithm has better performance in terms of processing time, preserving image quality and noise reduction.

Intervertebral disc (IVD) provides flexibility and shock absorption for the spine in the load transmission procedure. Disc degeneration may occur as a result of aging and inappropriate types of loading. Assessing biomechanical parameters of intact IVD in comparison to the degenerated disc with different grades of degeneration can facilitate the detection procedure and planning for suitable therapeutic treatment. In the present study, a real three-dimensional model of cercival IVD (-with adjacent vertebrae is constructed by using computed tomography (CT-scan) images. In order to accurately define mechanical properties, the disc and the vertebrae are modelled as poroviscoelastic and poroelastic materials, respectively. A porous medium approach is adopted to consider the considerable water content of both media alongside the solid matrix. For the solid phase of the IVD, the related viscoelastic parameters are extracted from an experimental test on a sheep lumbar intervertebral disc and stress vs. time data are fitted to the generalized Maxwell model with two Maxwell arms. By employing the finite element method, time-dependent response of the intact IVD and three different levels of the degenerated IVD (mild, moderate and severe) are studied in a relaxation test. Results indicate that during relaxation procedure, intradiscal fluid velocity decreases as a result of disc degeneration. This may oppositely affect the flexibility of IVD in the load bearing. It is also observed that stress relaxation of the severe degenerated IVD almost increases up to 16% relative to the intact IVD. Assessing the amount of disc bulging under load application shows enhancement for the degenerated disc compared to the intact disc.

Turbinates play an important role in conditioning of inhaled air and affect the airflow passing the nasal cavity. The purpose of this study is to investigate the effect of removing inferior turbinate on flow field, heat and moisture transfer from mucosa into the inhaled-air in a human nasal cavity and comparison of them before and after the surgery. Turbinectomy was performed virtually on the computational model under the specialist’ s supervision. In this study the airflow assumed to be laminar and unsteady. The nasal wall assumed to be rigid and no slip boundary condition was set. Moreover, the mucous layer assumed to be within fixed thickness in all over nasal cavity surface. The temperature and humidity distribution over the surface of mucusa are found by numerical computation. The results depict that conditioning of the nasal airway deteriorates by removing the inferior turbinate. For a specific air flow rate, both the heat and moisture flux averages decrease after surgery.

Recently, analysis of the human postural stability has gained increasing interest. This is mainly due to the necessity of understanding the self-organization mechanisms in this system activated in response to any motion pattern. The extraction of effective indicators from this system could help clinicians to diagnose patients’ postural disorders and guide the rehabilitation processes. The center of pressure (CoP) signal, as a collective variable, contains information from the human equilibrium system. Through the CoP trajectory production, various control mechanisms are activated at different time intervals, which is equivalent with emerging different basin of attractors in the phase space. The dynamical coordination of this system patterns determines how system switches between these attractors. In this paper, first to quantify the local information of CoP, two indicators are defined; "local correlation dimension (LCD)" and "phase dynamic coordination (PDC)". Then, for a designed experiment, the local behavior pattern of CoP time series is calculated based on the suggested indicators. Next, by designing a model that can generate rich dynamics with multiple attractors, we attempt to follow data behavioral changes. The proposed model is map based. The model parameters are tuned by PCD to follow the pattern of sub-attractors changes with the system LCD. Tracking the behavioral patterns of the posture system is one of the prominent results of this research. The proposed model not only can follow the local behavior of system, but also follows the global dynamics. Accordingly, the similarity of the decreasing-increasing trend of the correlation dimension variations for the model output and data demonstrates the variations of system’ s degrees of freedom in the test trials. The proposed model is the first behavioral model for the posture system, which can be used to quantify the variation of information in other biological systems based on the proposed methods.

The esophageal carcinoma is the eight most predominate malignancy in the world and the sixth deadliest cancer. 80% of esophageal cancers occur in squamous cells. In Iran, this type of cancer is more prevalent in Golestan province. Before the onset of this type of cancer, histological precursor lesions emerge in the epithelial tissue of esophageal mucosa that their progression and penetration into the underlying layers of epithelium lead to cancer. This disease starts from a pre-clinical phase in most patients. In most cases, the disease progresses to the same clinical stage in the absence of appropriate therapeutic interventions. In the literature of this cancer, there is no model for the progression of these lesions (dysplasia) at the mesoscopic level. In this study, by using microscopic images of normal and low-grade dysplasia biopsy samples, we proposed a dynamical model based on the globally coupled logistic maps. The model was designed and its parameters were set based on the assumptions of the esophageal epithelium structure, functionality and using the information about the fractal geometry of this tissue. The model performance was evaluated by computation the pattern of Lyapunov exponent variations across the epithelium thickness. In this model, the decreasing trend of this index for normal tissue had a reasonable accuracy and sensitivity to diagnose it from the low-grade dysplasia. Besides, the model results show that it can be a direct relationship between the structural complexity of this biological system and its timeliness uncertainty.

Spiral wave is a particular spatiotemporal pattern, observed in a wide range of complex systems such as neuronal network. Appearance of these waves is related to the network structure as well as the dynamics of its blocks. In this paper, we propose a new modified Hindmarsh-Rose neuron model. The proposed model uses a hyperbolic memductance function as the monotonically differentiable magnetic flux. An external electromagnetic excitation is also considered in the model. Firstly, we study the dynamics of the proposed neuron model through bifurcation diagram and Lyapunov spectrum, in two cases of no excitation and periodic excitation. The bifurcation diagram shows the property of antimonotonicity, which has not been observed in the previous models. Then a square network is constructed and we investigate the spatiotemporal pattrens. By varying the parameters values, spiral waves are observed in specific ranges. The formation of these waves depends on the interaction of all parameters simultaneously.