Here we present the results of experiments involving cynomolgus macaques, in which a model of traumatic spinal cord injury (TSCI) was created by using a balloon catheter inserted into the epidural space. Prior to the creation of the lesion, we inserted an EMG recording device to facilitate measurement of tail movement and muscle activity before and after TSCI. This model is unique in that the impairment is limited to the tail: the subjects do not experience limb weakness, bladder impairment, or bowel dysfunction. In addition, 4 of the 6 subjects received a combination treatment comprising thyrotropin releasing hormone, selenium, and vitamin E after induction of experimental TSCI. The subjects tolerated the implantation of the recording devi

     Psoriasis is a common, persistent papulosquamous skin illness that affects people of all ages and has a high societal and personal burden. It is associated with various serious medical conditions like depression, psoriatic arthritis, and cardiometabolic syndrome. Interleukin-36a (IL-36a) is a key molecule in inflammation, coordinating the activation of critical pro-inflammatory cytokines and immune cells. The current study involved 84 Iraqi psoriatic patients (PoS) and 67 healthy controls. Real-time PCR and sequencing techniques were used to evaluate the gene expression and polymorphism of two SNPs (rs2305152 A/C and rs895497 G/A) of the IL-36a gene. The findings revealed that the IL-36a gene was up-regulated in psoriatic patien

Analyzing plantar pressure trajectories is crucial for assessing foot behavior in dynamic gait stability. We propose the identification of foot symmetry and the detection of deformities by analyzing the trajectories of the center of pressure (CoP) and peak pressure (PP). First, using a foot pressure mapping system, plantar pressure data are acquired during a normal gait cycle. After the data have been acquired, post processing extracts both the CoP and PP trajectories over the spatiotemporal domain of foot motion for each foot independently. For this purpose, we used the optical flow technique which accurately estimates the direction of foot motion. The extracted trajectories of each foot are then segmented into, the medial and lateral regi

Vibration is a source of energy that can be beneficial or harmful based on the application. Vibration can affect the function of any structure; however, Ceramic matrix composite (CMC) is one of these structures. Whereby less studies have been concentrated on study its function specially when electromagnetic wave (microwave) exposed on its surface to perform its designed function. To address this concept, SiC composite has been fabricated which is designed to have a transparent characteristics to microwave. External vibration had been applied on its surface to monitor how much influence could nanoscale amplitude vibration damage the microwave interaction. The source of vibration was applied from piezoelectric and the vibration was monitored

One of the most important human diseases that need to be considered in terms of development of the medical engineering devices is cardiovascular disease which is a significant cause of death globally recently. Valvular heart disease is normally treated by restoring or altering heart valves with an artificial one. But the new prosthetic valve designs necessitate testing for durability estimate and failure method. It is significant to simulate the circulation system by the building of a pulse duplicator system. This study is stated by clarifying the parameter and implementation steps of the pulse duplicator system in which the different researchers have utilized the system and tried to explain the design steps of using this system wit

In this paper, the dynamics of scavenger species predation of both susceptible and infected prey at different rates with prey refuge is mathematically proposed and studied. It is supposed that the disease was spread by direct contact between susceptible prey with infected prey described by Holling type-II infection function. The existence, uniqueness, and boundedness of the solution are investigated. The stability constraints of all equilibrium points are determined. In addition to establishing some sufficient conditions for global stability of them by using suitable Lyapunov functions. Finally, these theoretical results are shown and verified with numerical simulations.

The aim of this paper is to describe an epidemic model when two SI-Type of diseases are transmitted vertically as well as horizontally through one population. The population contains two subclasses: susceptible and infectious, while the infectious are divided into three subgroups: Those infected by AIDS disease, HCV disease, and by both diseases. A nonlinear mathematical model for AIDS and HCV diseases is Suggested and analyzed. Both local and global stability for each feasible equilibrium point are determined theoretically by using the stability theory of differential equations, Routh-Hurwitz and Gershgorin theorem. Moreover, the numerical simulation was carried out on the model parameters in order to determine their impact on the disease

The present study aimed to assess the impact of seed Rhizobia treatment and potyvirus inoculation on bacterial nodulation and nitrogen fixation in cowpeas. The plants were infected with the virus two weeks post-germination. Nodules were present on the roots of plants treated with Rhizobia; however, almost no nodules were detected on untreated plants. The average number of nodules per plant on virus-inoculated plants was significantly lower than the average number per noninoculated plant. The virus caused a substantial decrease in the weight of nodules also. The study revealed that the presence of Rhizobia resulted in a significant rise in nitrogen content in the foliage. Specifically, the nitrogen percentage increased from 1.29% in plants n

We propose a new object tracking model for two degrees of freedom mechanism. Our model uses a reverse projection from a camera plane to a world plane. Here, the model takes advantage of optic flow technique by re-projecting the flow vectors from the image space into world space. A pan-tilt (PT) mounting system is used to verify the performance of our model and maintain the tracked object within a region of interest (ROI). This system contains two servo motors to enable a webcam rotating along PT axes. The PT rotation angles are estimated based on a rigid transformation of the the optic flow vectors in which an idealized translation matrix followed by two rotational matrices around PT axes are used. Our model was tested and evaluated

كانت االمثمة ومازالت الػسيمة الػحيجة واالىع في ايزاح القػاعج الشحػية قجيسا وحجيثا ، فالسثال ىػ الصخيقة التي بػاسصتيا يفيع الستعمع ما يخيج ان يػصمو السعمع ، فالذخح الكثيخ والدخد ال يػصل الى بيان القاعجة مالع يعدز باالمثمة . وليحا فان اختيار السثال السشاسب في السكان السشاسب ال يتع بصخيقة عذػائية اذا يشبغي ان يكػن السثال ذا معاييخ ومػاصفات تشاسب السقام الحي قيمت فيو . مغ ىشا جاء ىحا البحث ليبيغ ما السػاصفات الت

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental

Stroke is the second largest cause of death worldwide and one of the most common causes of disability. However, several approaches have been proposed to deal with stroke patient rehabilitation like robotic devices and virtual reality systems, researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. In this study, the electroencephalography (EEG) dataset from post-stroke patients were investigated to identify the effects of the motor imagery (MI)-based BCI therapy by investigating sensorimotor areas using frequency and time-domain features and to select particular methods that help in enhancing the MI-based BCI systems for stroke patients using EEG signal processing. Therefore, to detect

This paper investigates a new approach to the rapid control of an upper limb exoskeleton actuator. We used a mathematical model and motion measurements of a human arm to estimate joint torque as a means to control the exoskeleton’s actuator. The proposed arm model is based on a two-pendulum configuration and is used to obtain instantaneous joint torques which are then passed into control law to regulate the actuator torque. Nine subjects volunteered to take part in the experimental protocol, in which inertial measurement units (IMUs) and a digital goniometer were used to measure and estimate the torque profiles. To validate the control law, a Simscape model was developed to simulate the arm model and control law in which measurem

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated

Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio

In this paper, a random transistor-transistor logic signal generator and a synchronization circuit are designed and implemented in lab-scale measurement device independent–quantum key distribution systems. The random operation of the weak coherent sources and the system’s synchronization signals were tested by a time to digital convertor.

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p