Background: For many decades, the ECG was the
workhorse of non-invasive cardiac test and today although
other techniques provide more details about the structural
anomalies in congenital heart diseases, ECG is likely to be
part of clinical evaluation of patients with such diseases
because it is inexpensive, easy to perform and in certain
situations may be both sensitive and specific.
Objective: this study carried out to identify the pattern of
ECG study in patients with TOF.
Methods: this is a retrospective study of 200 patients
with TOF, referred to Ibn Al-Bitar cardiac center from
April 1993 to May 1999. The diagnosis of TOF established
by echocrdiographic, catheterization and angiographic
study. For each patient, the ECG tracing had been analyzed
for rhythm, p-wave, P-R interval, QRS axis, duration and
T-wave in V1 and any chamber enlargement.
Results: The ECG analysis revealed that all patients had
sinus rhythm, normal P-R interval and normal p-wave
duration and amplitude, and normal QRS duration. All
studied patients had one criteria of RVH and 95% of them
had two or more of such criteria.
Conclusion: we found that in the absence of RVH
criteria, the diagnosis of TOF is unlikely and the present of
northwest axis should indicate canal type VSD
Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show More
(3)
(2)
Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show More
(3)
(2)
(19)
(19)
Calcifying epithelial odontogenic tumour (CEOT) is a benign odontogenic neoplasm of epithelial origin that secretes an amyloid‐like protein tending towards calcification. This study aims to describe a case series from Iraq of one of the rarest odontogenic tumours.
Clinical and histopathological analysis of Calcifying epithelial odontogenic tumour cases that are archived at the oral pathology laboratory of the college of dentistry (Baghdad University) from 2000 to 2019.
Six cases of CEOT were regi
(1)
(1)
CuO-ZnO-Al2O3 catalyst was prepared in the ratios of 20:30:50 respectively, using the coprecipitation method of Cu, Zn and Al carbonates from their nitrate solutions dissolved in distilled water by adding sodium bicarbonate as precipitant.The catalyst was identified by XRD and quantitatively analysis to determine the percentages of its components using flame atomic absorption technique. Also the surface area was measured by BET method. The activity of this prepared catalyst was examined through the oxidation of ethanol to acetaldehyde which was evaluated by gas chromatography.
Polymers (Silicon elastomer) are used lately as a conductive material in electronic application in addition to be transparent, to light. In this paper we prepared polymer films about (1mm) thick and less which contain Ni-metal powder cured in magnetic vacuum furnaces at temperature 120°C in order to arrange or to be oriented the particles of the Ni- powder through the polymer in such a way to be conductive for electric currents. We found that these films are sensitive to any loads on the surface (force per unit area). Using light loads on a unit electric cell from these films, we get an electric transparent sensor that could be used in sensing applications.
Polycaprolactone polymer is widely used in medical applications due to its biocompatibility. Electro spinning was used to create poly (ε- caprolactone) (PCL) nanocomposite fiber mats containing hydroxyapatite (HA) at concentrations ranging from 0.05 to 0.4% wt. The chemical properties of the fabricated bio composite fibers were evaluated using FTIR and morphologically using field-emission scanning-electron microscopy (FESEM), Porosity, contact angle, as well as mechanical testing(Young Modulus and Tensile strength) of the nanofibers were also studied. The FTIR results showed that all the bonds appeared for the pure PCL fiber and the PCL/HA nano fibers. The FESEM nano fiber showed that the fiber diameter increased from 54.13 to 155.79 (n
... Show More