Background: Nasopharyngeal carcinoma (NPC) is one of the most challenging tumors because of their relative inaccessibility and that their spread can occur without significant symptoms with few signs, but Radiotherapy (RT) has a role in treatment of it.
Objectives: To show that RT is still the modality of choice in the treatment of NPC, to study modes of presentations, commonest histopathological types and their percentages, to show differences in the sensitivities of these types to RT and to find out a 5 year survival rate(5YSR) and its relation with lymph node involvement.
Methods: This is a retrospective study of 44 patients with NPC who were treated with routine RT from 1988-2007 at the institute of radiology and nuclear medicine. All patients were treated with megavoltage x-ray with a total dose to the primary lesion was 60-70 Grays (1 Gray = 100 Rads) so we gave 6000-7000 Rads in 6-8 weeks and 50 Grays were applied to the cervical lymphatic chain bilaterally.
Results: 10 out of 44 patients treated have survived more than 5 years (with a 5YSR of 22.7%). In this series of cases, the 5- year overall survival rate is: 60% with stage I, 33.3% with stage II, 28.5% with stage III and 13.7% with stage IV. But, it should be noted that most of them were advanced with stages III and IV accounting for 36 patients i.e 81.8%.
Conclusion: Radiotherapy (RT) is the modality of choice in the treatment of NPC and we must irradiate areas of probable spread with the primary lesion because spread can occur without significant signs and symptoms .The most common histopathological type is undifferentiated carcinoma which is more sensitive to RT than squamous cell carcinoma (scc) or other types of carcinoma.
Also we see that stages III and IV NPC (advanced) comprises high number of the total and the 5-YSR decreases as the patient advances from stage I to stage IV, therefore, early detection and diagnosis is very important.
Combining ultrasonic irradiation and the Fenton process as a sono-Fenton process, the chemical oxygen demand (COD) in refinery wastewater was successfully eliminated using response surface methodology (RSM) with central composite design (CCD). The impact of two main influential operational parameters (iron dosage and reaction time) on the COD removal from wastewater generated by an Iraqi petroleum refinery facility was explored. Removal of 85.81% was attained under the optimal conditions of 21 minutes and 0.289 mM of concentration. Additionally, the results revealed that the concentration of has the highest effect on the COD elimination, followed by reaction time. The high R2 value (96.40%) validated the strong fit of the mo
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As we live in the era of the fourth technological revolution, it has become necessary to use artificial intelligence to generate electric power through sustainable solar energy, especially in Iraq and what it has gone through in terms of crises and what it suffers from a severe shortage of electric power because of the wars and calamities it went through. During that period of time, its impact is still evident in all aspects of daily life experienced by Iraqis because of the remnants of wars, siege, terrorism, wrong policies ruling before and later, regional interventions and their consequences, such as the destruction of electric power stations and the population increase, which must be followed by an increase in electric power stations,
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KE Sharquie, JR Al-Rawi, AA Noaimi, HM Al-Hassany, Journal of drugs in dermatology: JDD, 2012 - Cited by 47
Background: Cyclin D1 proto-oncogene is an important regulator of (G1 to S) phase progression in many different cell types. The Aims of this study were to evaluate the immunohistochemical expression of Cyclin D1 in mucoepidermoid and adenoid cystic carcinoma of the salivary glands and to correlate the immunoexpression of this protein with the clinicopathological findings. Materials and methods Retrospectively, twelve of archival formalin fixed paraffin embedded tissue samples of salivary Mucoepidermoid and fourteen blocks of adenoid cystic carcinomas obtained from the archives of the department of oral pathology / college of dentistry / Baghdad university, Al-Shaheed Ghazi hospital, were included in this study. Five micrometer sections o
... Show MoreThis study aimed to identify the changes in total protein in saliva and sera samples of patients with oral squamous cell carcinoma in comparison to those of healthy controls. These changes were followed using electrophoresis (PAGE). Meanwhile, determinations of albumin, globulin and albumin to globulin ratio were carried out on sera samples only.Two groups were the participants in the present study, 18 patients with Oral Squamous Cell Carcinoma (OSCC), and 20 ages and gender matched healthy controls.
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Vol. 6, Issue 1 (2025)
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
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Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a
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The study included selection six species of the fungi related to Pleurotus genus were evaluated for their ability to production of Pleurotin, one of them, Pleurotus ostreatus (P.11) was isolated and identified in the present study. Pleurotin was extracted with screening by Thin Layer Chromatography (TLC) and quantification High Performance Liquid Chromatography (HPLC). Cytotoxicity of Pleurotin extracted from P. ostreatus (P.11) grown in different sugar sources (galactose, mannitol, sucrose, dextrose and lactose) liquid media was test against three selected cancer cell lines, CaSki, MCF-7 and A549 addition to Human Non Cancer Fibroblast Cell Line (MRC-5). Pleurotin of P. ostreatus (P.11) grown in galactose induced the significant highest
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