Possible Role of Sonic Hedgehog and Epithelial-Mesenchymal Transition in Renal Cell Cancer Progression

Hosny M. Behnsawy,Katsumi Shigemura,Fatma Y. Meligy,Fukashi Yamamichi,Masuo Yamashita,Wen-Chin Haung,Xiangyan Li,Hideaki Miyake,Kazushi Tanaka,Masato Kawabata,Toshiro Shirakawa,Masato Fujisawa 대한비뇨의학회 2013 Investigative and Clinical Urology Vol.54 No.8

Purpose: Sonic hedgehog (Shh) signaling and epithelial-mesenchymal transition (EMT) are both known to relate to cancer progression. The purpose of this study was to investigate the role of Shh signaling and EMT in renal cell carcinoma (RCC). Materials and Methods: Cell proliferation was assayed in RCC cell lines in the presence or absence of a Shh signaling stimulator, recombinant Shh (r-Shh) protein, or a Shh signaling inhibitor, cyclopamine. Real-time reverse transcription-polymerase chain reaction (RT-PCR) was performed to study the expression of EMT markers (E-cadherin, N-cadherin, and vimentin) and osteonectin. The expression of Ki-67, Gli-1, osteonectin, and EMT markers in nephrectomy specimens from RCC patients was also measured by immunohistochemical (IHC) staining. Results: RCC cells showed enhanced cell proliferation by r-Shh protein, whereas cell proliferation was suppressed by the addition of cyclopamine in RenCa cells. Real-time RT-PCR showed that r-Shh suppressed the expression of E-cadherin and that this suppression was partly blocked by cyclopamine alone in RenCa cells. In the IHC results, osteonectin significantly correlated with vein sinus invasion (p=0.0218), and the expression of vimentin significantly correlated with lymphatic invasion (p=0.0392). Conclusions: Shh signaling and EMT play roles in RCC progression, and the Shh signaling inhibitor cyclopamine might be a possible molecular targeted therapeutic strategy for RCC.

Remittance Concentration and Volatility: Evidence from 72 Developing Countries

Hosny Amr 한국국제경제학회 2020 International Economic Journal Vol.34 No.4

This paper contributes to the literature by introducing the role of geographic concentration of the source of remittances. Specifically, using data over 2010–2015 for 72 developing countries, we study the impact of (i) large remittances and (ii) the geographic concentration of the source of remittances on economic volatilities. Results suggest that while (i) large remittances can be stabilizing on average, (ii) high remittance concentration from source countries can aggravate economic volatilities in recipient countries. Results are robust to global shocks affecting both source and recipient countries, and volatility in the remittance-sending country.

A custom building deterioration model

Hosny, O.A.,Elhakeem, A.A.,Hegazy, T. Techno-Press 2011 Structural Engineering and Mechanics, An Int'l Jou Vol.37 No.6

Developing accurate prediction models for deterioration behavior represents a challenging but essential task in comprehensive Infrastructure Management Systems. The challenge may be a result of the lack of historical data, impact of unforeseen parameters, and/or the past repair/maintenance practices. These realities contribute heavily to the noticeable variability in deterioration behavior even among similar components. This paper introduces a novel approach to predict the deterioration of any infrastructure component. The approach is general as it fits any component, however the prediction is custom for a specific item to consider the inherent impacts of expected and unexpected parameters that affect its unique deterioration behavior.

Construction Claims Prediction and Decision Awareness Framework using Artificial Neural Networks and Backward Optimization

Hosny, Ossama A.,Elbarkouky, Mohamed M.G.,Elhakeem, Ahmed Korea Institute of Construction Engineering and Ma 2015 Journal of construction engineering and project ma Vol.5 No.1

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

Simulation of Tempcore Process for 500 MPa Steel Bars

Sally Hosny,Mohamed A.‑H. Gepreel,Mona G. Ibrahim,Ahmed R. Bassuony 대한금속·재료학회 2021 METALS AND MATERIALS International Vol.27 No.9

Tempcore process is an environmental friendly, simple and energy efficient technology for producing high strength reinforcingsteel rebars without requiring costly alloying addition. The mechanical properties of Tempcore treated steel rebarhave been previously investigated using various models, although they are still restricted to specific steel compositions,bar sizes, and/or process parameters. In this study, a methodology is developed to predict the internal microstructure andoverall mechanical properties (i.e., hardness, ultimate tensile strength, and yield strength) of Tempcore treated bars for anysteel compositions, bar sizes, process parameters, and simulation assumptions. Three sequential models are proposed: (1)thermal model to predict thermal profiles of bars using computational fluid dynamics CFD simulation, (2) metallurgicalmodel to estimate the internal microstructure change across the bar section using both; the JMatPro® and a derived equationthat calculates the martensite volume fraction (Vm%) of a functionally graded steel bar, and (3) Regression models basedon the rule of mixture to predict mechanical properties. The validation results show a good agreement between calculatedand experimental results; the mean absolute percentage errors are 2.8% for hardness, 2.8% for ultimate stress and 3.8% foryield stress. Eventually, the proposed methodology presented a sustainable, easy, fast, and cost-efficient solution to attainthe required mechanical properties of a steel bar treated by Tempcore process.

RESEARCH ARTICLE : (一)-Catechin glycosides from Ulmus davidiana

( Mohammed Hosny ),( Ming Shan Zhang ),( Haiyan Zhang ),( Hyun Wook Chang ),( Mi Hee Woo ),( Jong Keun Son ),( Sunny Kyung Seon Lee ) 영남대학교 약품개발연구소 2014 영남대학교 약품개발연구소 연구업적집 Vol.24 No.0

Extensive chromatographic separation of the n-BuOH soluble fraction obtained from the stem and root barks of U. davidiana resulted in five hitherto unknown compounds together with a known one (-)-catechin 1. Structures of the five compounds were elucidated by chemical and spectroscopic analyses, to be (-)-catechin-7-O-gallate-5-O-(5″″-trans-caffeoyl)-β-D-apiofuranoside-3-O-β-D-apiofuranosyl-(1 → 2)-β-D-glucopyranoside 2, (-)-catechin-7-O-gallate-5-O-(5″″-trans-caffeoyl)-β-D-apiofuranoside-3-O-β-D-glucopyranoside 3, (-)-catechin-7-O-gallate-5-O-β-D-apiofuranoside-3-O-(2″-O-galloyl)-β-D-glucopyranoside 4, (-)-catechin-7-O-gallate-5-O-(5″″-trans-caffeoyl)-β-D-apiofuranoside 5, and (-)-catechin-7-O-gallate-5-O-(5″″-trans-feruloyl)-β-D-apiofuranoside 6.

Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer

Mohamed Hosny Osman,Reham Hosny Mohamed,Hossam Mohamed Sarhan,박은정,백승혁,이강영,강정현 대한암학회 2022 Cancer Research and Treatment Vol.54 No.2

Purpose Machine learning (ML) is a strong candidate for making accurate predictions, as we can use large amount of data with powerful computational algorithms. We developed a ML based model to predict survival of patients with colorectal cancer (CRC) using data from two independent datasets. Materials and Methods A total of 364,316 and 1,572 CRC patients were included from the Surveillance, Epidemiology, and End Results (SEER) and a Korean dataset, respectively. As SEER combines data from 18 cancer registries, internal validation was done using 18-Fold-Cross-Validation then external validation was performed by testing the trained model on the Korean dataset. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), sensitivity and positive predictive values. Results Clinicopathological characteristics were significantly different between the two datasets and the SEER showed a significant lower 5-year survival rate compared to the Korean dataset (60.1% vs. 75.3%, p < 0.001). The ML-based model using the Light gradient boosting algorithm achieved a better performance in predicting 5-year-survival compared to American Joint Committee on Cancer stage (AUROC, 0.804 vs. 0.736; p < 0.001). The most important features which influenced model performance were age, number of examined lymph nodes, and tumor size. Sensitivity and positive predictive values of predicting 5-year-survival for classes including dead or alive were reported as 68.14%, 77.51% and 49.88%, 88.1% respectively in the validation set. Survival probability can be checked using the web-based survival predictor (http://colorectalcancer.pythonanywhere.com). Conclusion ML-based model achieved a much better performance compared to staging in individualized estimation of survival of patients with CRC. Purpose Machine learning (ML) is a strong candidate for making accurate predictions, as we can use large amount of data with powerful computational algorithms. We developed a ML based model to predict survival of patients with colorectal cancer (CRC) using data from two independent datasets.Materials and Methods A total of 364,316 and 1,572 CRC patients were included from the Surveillance, Epidemiology, and End Results (SEER) and a Korean dataset, respectively. As SEER combines data from 18 cancer registries, internal validation was done using 18-Fold-Cross-Validation then external validation was performed by testing the trained model on the Korean dataset. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), sensitivity and positive predictive values.Results Clinicopathological characteristics were significantly different between the two datasets and the SEER showed a significant lower 5-year survival rate compared to the Korean dataset (60.1% vs. 75.3%, p < 0.001). The ML-based model using the Light gradient boosting algorithm achieved a better performance in predicting 5-year-survival compared to American Joint Committee on Cancer stage (AUROC, 0.804 vs. 0.736; p < 0.001). The most important features which influenced model performance were age, number of examined lymph nodes, and tumor size. Sensitivity and positive predictive values of predicting 5-year-survival for classes including dead or alive were reported as 68.14%, 77.51% and 49.88%, 88.1% respectively in the validation set. Survival probability can be checked using the web-based survival predictor (http://colorectalcancer.pythonanywhere.com).Conclusion ML-based model achieved a much better performance compared to staging in individualized estimation of survival of patients with CRC.

Pleural Space Elastance and Its Relation to Success Rates of Pleurodesis in Malignant Pleural Effusion

( Hossam Hosny Masoud ),( Mahmoud Mohamed El-zorkany ),( Azza Anwar Ahmed M. Sc. ),( Hebatallah Hany Assal ) 대한결핵 및 호흡기학회 2021 Tuberculosis and Respiratory Diseases Vol.84 No.1

Background: Pleurodesis fails in 10%-40% of patients with recurrent malignant pleural effusions malignant pleural effusion and dyspnea. This study aimed to assess the values of pleural elastance (P_EL) after the aspiration of 500 mL of pleural fluid and their relation to the pleurodesis outcome, and to compare the pleurodesis outcome with the chemical characteristics of pleural fluid. Methods: A prospective study was conducted in Kasr El-Aini Hospital, Cairo University, during the period from March 2019 to January 2020. The study population consisted of 40 patients with malignant pleural effusion. The measurement of PEL after the aspiration of 500 mL of fluid was done with “P_EL 0.5” (cm H₂O/L), and the characteristics of the pleural fluid were chemically and cytologically analyzed. Pleurodesis was done and the patients were evaluated one month later. The PEL values were compared with pleurodesis outcomes. Results: After 4-week of follow-up, the success rate of pleurodesis was 65%. The P_EL 0.5 was significantly higher in failed pleurodesis than it was in successful pleurodesis. A cutoff point of P_EL 0.5 >14.5 cm H₂O/L was associated with pleurodesis failure with a sensitivity and specificity of 93% and 100%, respectively. The patients with failed pleurodesis had significantly lower pH levels in fluid than those in the successful group (p<0.001). Conclusion: PEL measurement was a significant predictor in differentiating between failed and successful pleurodesis. The increase in acidity of the malignant pleural fluid can be used as a predictor for pleurodesis failure in patients with malignant pleural effusion.

The Role of Curcuma longa Against Doxorubicin (Adriamycin)-Induced Toxicity in Rats

Ragaa Hosny Mohamad,Amal Mohamad El-Bastawesy,Zekry Khalid Zekry,Hussain A. Al-Mehdar,Mohamad Gamil Abdel Monaam Al-said,Soaad Shaker Aly,Sabry Mohamed Sharawy,Mahmuod M. El-Merzabani 한국식품영양과학회 2009 Journal of medicinal food Vol.12 No.2

The major component, called curcumin, of turmeric (Curcuma longa L.) (Family Zingiberaceae) powder is responsible for its biological actions. The present study aimed to prove the protective effect of turmeric extract against doxorubicin (DOX)-induced cardiac, hepatic, and renal toxicity as evaluated in rats. Body weight and urine volume of the animal groups under investigation were recorded daily throughout the experimental period. Also, the cardiac, hepatic, and renal toxicities were determined by estimating the changes in serum activities of the enzymes lactate dehydrogenase (LDH) and creatine kinase (CK), serum levels of alanine aminotransferase, aspartate aminotransferase, nitric oxide, albumin, and calcium, and kidney and liver tissue activities of superoxide dismutase and glutathione peroxidase, as well as the contents of glutathione and malondialdehyde. Hyperlipidemia was also determined, and protein and albumin changes in urine were estimated. Biochemical and histopathological findings demonstrate that turmeric extract has multiple therapeutic activities that are beneficially protective, and it has an ameliorative effect against DOX-induced cardiac toxicity and hepatotoxicity and blocks DOX-induced nephrosis. Similarly, turmeric extract inhibited the DOX-induced increase in plasma cholesterol, LDH, and CK. The present findings conclude that the turmeric extract has multiple therapeutic activities that block the cardiac, hepatic, and renal toxicities induced by DOX, and it also possibly acts as a free radical scavenger.

Silymarin Modulates Cisplatin-Induced Oxidative Stress and Hepatotoxicity in Rats

( Heba Hosny Mansour ),( Hafez Farouk Hafez ),( Nadia Mohamed Fahmy ) 생화학분자생물학회 2006 BMB Reports Vol.39 No.6