Retained soft tissue foreign bodies following injuries are frequently seen in the Emergency and Plastic Surgery practice. The patients with such presentations require a watchful and detailed clinical as- sessment to overcome the anticipant possibility of missing them. However, the diagnosis based on the clinical evaluation is usually challenging and needs to be supported by imaging modalities that are suboptimal and may fail in identifying some types of foreign bodies. Owing to that, serious complications such as chronic pain, infection, and delayed wound healing can be faced that necessitate a prompt intervention to halt those detrimental consequences. The classical method of removal is a surgical exploration which is not free of risks.

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

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

Background: During pregnancy many physiological, anatomical and biochemical changes take place that affect almost all body systems. In the oral pregnant women have serious changes such as more sever dental caries. This study was conducted to measure dental caries severity and selected salivary variables (salivary flow rate, PH and viscosity)and to find the relation of dental caries with these salivary variables. Subjects, materials and methods: The study group consisted of 60 pregnant women that were divided into three equal groups according to trimester (20 pregnant women in each trimester).They were selected randomly from the Maternal and Child Health Care Centers in Baghdad city, the age range was 20-25 years. In addition to 20 unmarried

This study has contributed to identify convenience of the land Usifiya of Mahmudiya district baghdad province for the cultivation of sensitive and salt resisting crops and growing pasture .The nine pedons (tracks) of the study have spread over three transects to cover the whole area the north, middle and south .
Depending on Visual Space tupe (TM) for 2007 which is super classified, samples are distributed on all classes of land cover
It is detected the presence of nine series soils namely
MM5- MW96- DM 115 for 1st transect
MF12- DM46 - DM96 for 2nd transect
DM56- DF56 - MW5 for 3rd transect
Soils are classified to the level of sub great group according to (soil Survey Staff) to :
*Typic Torri Fluvents , Typic Haplos

Background: Lateral cephalometric radiography is commonly used as a standard tool in orthodontic assessment and treatment planning. This study aimed to determine the tongue and surrounding space area in a sample of Iraqi adults with class I dental and skeletal pattern. Materials and methods: The study included thirty healthy subjects (15 males and 15 females) with an age ranged between 23-34 years and class I dental and skeletal pattern with no history of any sleep related disorders. The assessed cephalometric measurement included length and height of the tongue and position of hyoid bone from cervical line. Descriptive statistics were obtained for the data. Genders difference was evaluated by independent sample t-test. Results: There wer

Background: In the traditional protocol, the patient should wait after extraction up to six months to place the dental implant in healed bone, this waiting time accompanied by varying degrees of alveolar bone changes. In order to overcome these problems, immediate implant placement in the fresh extraction socket was introduced. The Aim of this study was to evaluate the outcome of the immediate implant placement utilizing Resonance Frequency Analysis (RFA) to quantify implant stability and osseointegration. Materials and Methods: A total of (23) patients participated in the study, receiving (44) implants placed in the sockets of teeth indicated for extraction. Clinical and radiographic preoperative assessment was accomplished for each patie

Samples of the ovary and uterus of local breed cats used to investigate the histological, histometrical and hormonal features. The paraffin embedding technique was used for processing of tissue that stained by hematoxyline and eosin stain, and massons trichrom stain. Ovary of at proestrus or oestrus phases composed of outer cortex that covered by cuboidal germinal epithelium and inner medulla. Tunica albuginea composed of a thin layer of characterized by fusiform stromal cells. The cortex content groups of Oogonial cells, numerous primordial follicles, little primary, secondary and tertiary follicles in addition for 1-2 follicular cysts and mature corpus letium. In pregnant cat the thickness of ovarian cortex was significantly incre