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Nagy M.M.,Ain Shams University | Tawfik H.E.,Ain Shams University | Hashem A.A.R.,Ain Shams University | Hashem A.A.R.,Future University Egypt | Abu-Seida A.M.,Cairo University
Journal of Endodontics | Year: 2014

Introduction Regenerative endodontics is a promising alternative treatment for immature teeth with necrotic pulps. The present study was performed to assess the regenerative potential of young permanent immature teeth with necrotic pulp after the following treatment protocols: (1) a mineral trioxide aggregate (MTA) apical plug, (2) the regenerative endodontic protocol (blood clot scaffold), and (3) the regenerative endodontic protocol with a blood clot and an injectable scaffold impregnated with basic fibroblast growth factor. Methods Immature necrotic permanent maxillary central incisors (n = 36) of patients 9-13 years old were divided into 3 groups according to the treatment protocol: the MTA group (MTA apical plug), the REG group (regenerative endodontic protocol [blood clot]), and the FGF group (regenerative endodontic protocol [blood clot + injectable scaffold]). Follow-up was done up to 18 months. Standardized radiographs were digitally evaluated for an increase in root length and thickness, a decrease in the apical diameter, and a change in periapical bone density. Results After a follow-up period of 18 months, most of the cases showed radiographic evidence of periapical healing. Groups 2 and 3 showed a progressive increase in root length and width and a decrease in apical diameter. Conclusions The regenerative endodontic procedure allowed the continued development of roots in teeth with necrotic pulps. The use of artificial hydrogel scaffold and basic fibroblast growth factor was not essential for repair. Copyright © 2014 American Association of Endodontists.


El-Messery S.M.,Mansoura University | Hassan G.S.,King Saud University | Hassan G.S.,Mansoura University | Al-Omary F.A.M.,King Saud University | El-Subbagh H.I.,Future University Egypt
European Journal of Medicinal Chemistry | Year: 2012

A novel series of 2-acetamido and 2 or 3-propanamido derivatives of 4- or 5-substituted-thiazoles was designed and synthesized. Structure elucidation of the new synthesized compounds was attained by the use of 1H & 13C NMR, and Mass spectrometry. Compounds were subjected to NCI in vitro assessment for their antitumor activity, at a single dose of 10 μM of test compounds. Compounds bearing straight chain substituent or 4-phenyl function proved to be more active than their branched or 4-methyl congeners. Compounds 37, 41 and 42 exhibited broad spectrum antitumor activity. Compounds 23 and 27 proved lethal while compounds 18, 21, 32 and 37 showed remarkable GI values of 75.5, 69.3, 96.2 and 92.7% to the Leukemia CCRF-CEM cell line, respectively. © 2012 Elsevier Masson SAS. All rights reserved.


Hone K.S.,Brunel University | El Said G.R.,Future University Egypt
Computers and Education | Year: 2016

Massive Open Online Courses (MOOCs) hold the potential to open up educational opportunities to a global audience. However, evidence suggests that only a small proportion of MOOC participants go on to complete their courses and relatively little is understood about the MOOC design and implementation factors that influence retention. This paper reports a survey study of 379 participants enrolled at university in Cairo who were encouraged to take a MOOC of their own choice as part of their development. 122 participants (32.2%) went onto to complete an entire course. There were no significant differences in completion rates by gender, level of study (undergraduate or postgraduate) or MOOC platform. A post-MOOC survey of students' perceptions found that MOOC Course Content was a significant predictor of MOOC retention, with the relationship mediated by the effect of content on the Perceived Effectiveness of the course. Interaction with the instructor of the MOOC was also found to be significant predictor of MOOC retention. Overall these constructs explained 79% of the variance in MOOC retention. © 2016 The Authors. Published by Elsevier Ltd.


Taher A.T.,Cairo University | Georgey H.H.,Cairo University | El-Subbagh H.I.,Future University Egypt
European Journal of Medicinal Chemistry | Year: 2012

The synthesis of some new heterodiazole and their annulated imidazo[2,1-b]1,3,4-oxa/thiadiazolone 6a-d, 7a-d; 1,3,4-oxa or thiadiazole[3,2-a]pyrimidine diamine 8a-d and 1,3,4-oxa or thiadiazole-3- piperidino-1-propamide 11a,b derivatives have been described. The obtained compounds were evaluated for their in-vitro antitumor activity. A single dose (10 μM) of the test compounds were used in the full National Cancer Institute (NCI) 60 cell lines panel assay. Compounds 6c and 6d displayed appreciable anticancer activity against leukemia, non-small cell lung, CNS and showed moderate activity against colon, melanoma, and breast cancer cells lines. Compound 6c possessed remarkable broad-spectrum antitumor activity which almost 4 fold more active than the known drug 5-FU with GI 50, TGI, and LC 50 values of 6.0, 17.4, and 55.1 μM, respectively. © 2011 Elsevier Masson SAS. All rights reserved.


El-Dahshan E.A.-S.,Ain Shams University | El-Dahshan E.A.-S.,Egyptian arning University | Mohsen H.M.,Future University Egypt | Revett K.,University of Westminster | And 2 more authors.
Expert Systems with Applications | Year: 2014

Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested. © 2014 Elsevier Ltd. All rights reserved.

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