This study aims to characterize traumatic spinal cord injury (TSCI) neurophysiologically using an intramuscular fine-wire electromyography (EMG) electrode pair. EMG data were collected from an agonist-antagonist pair of tail muscles of Macaca fasicularis, pre- and post-lesion, and for a treatment and control group. The EMG signals were decomposed into multi-resolution subsets using wavelet transforms (WT), then the relative power (RP) was calculated for each individual reconstructed EMG sub-band. Linear mixed models were developed to test three hypotheses: (i) asymmetrical volitional activity of left and right side tail muscles (ii) the effect of the experimental TSCI on the frequency content of the EMG signal, (iii) and the effect

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

Objectives: To determine the impact of an educational program on nurses’ knowledge
and practices concerning neurogenic bladder rehabilitation for spinal cord injured persons
through a follow-up approach each two months post program implementation for six
months.
Methodology: "Follow-up" longitudinal design by using time series approach of data
analysis and the application of pre-post tests approach for the study and the control
groups. The study was carried out at Ibn Al-Kuff hospital for (SCI) in Baghdad governorate
from 5th of July 2010 to 15th of October 2011. To achieve the objectives of the study, a
non-probability (purposive) sample of (60) nurses (males and females) were working in SCI
units were selec

The embryonic development of the Mosquito fish(Gambusia affinis) eye lens was investigated using light microscopy .The results indicated that the embryonic development of the lens does not correspond to that of the non spherical lenses of terrestrial vertebrates .This study showed that a very small cavity in the lens vesicle appears during development ,but it differs from that of the mammalian lens. The most important aspect in this study is that ,the posterior edge of the simple cuboidal epithelium which covers the anterior half of the surface of the lens is situated well beyond the equatorial region of the lens .As a result , the germinal and transitional zones became closer to the posterior pole rather than the anterior pole of the le

Back ground: Zygote produce from once a sperm fertilizes an egg cell. Then, the zygote (unicellular) will begin chain of cellular cleavages to produce multicellular mass, its embryo, the differentiated to different tissues and organism. The development of the embryo is called embryogenesis. Coenzyme Q10, is an antioxidant produced in the body. It boosts cellular energy and may enhance the immune system. CoQ10 is present and measurable in seminal fluid, the concentration of CoQ10 directly correlates with both sperm count and motility. It is beneficial in the prevention and treatment a wide range of health problems. Objectives: The present study was aimed to investigate the possibility of using coenzyme Q10 to improve in vitro fertilization (

Arabian killifish, Aphanius dispar, lives in marine coastal areas of the Middle East, as well as in streams that experience a wide range of salinities and temperatures. It has been used as a mosquito control agent and for studying the toxicities of environmental pollutants. A. dispar's eggshell (chorion) and embryos are highly transparent and are suitable for high resolution microscopic observations, offering excellent visibility of live tissues.

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi