Repurposing Metformin and Antifolates for the Treatment of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), one of the most prevalent types of cancers worldwide, continues to maintain high levels of resistance to standard therapy. As clinical data revealed poor response rates, the need for developing new methods has increased to improve the overall wellbeing of patients with HCC. Due to its safety, wide availability and previously reported anti-cancer effects, metformin (MET) serves to be a possible therapeutic agent when combined with other well-known anti-cancer agents. The aim of this study was to investigate the potential anti-cancer effects of MET, an anti-diabetic agent, when combined with two antifolate drugs: trimethoprim (TMP) or methotrexate (MTX), and the underlying mechanisms involved. In this study, single drugs and combinations were investigated using in vitro assays, cytotoxicity assay (MTT), RT-PCR, flow cytometry, scratch wound assay and Seahorse XF analysis, to reveal their potential anti-cancer effects on a human HCC cell line, HepG2. The cytotoxicity assay was performed to determine the IC50 concentration of MET alone and in combination with antifolates. The co-treatment of both drugs increased Bax and p53 apoptotic markers, while decreased the anti-apoptotic marker; Bcl-2. Both combinations increased the percentage of apoptotic cells and halted cancer cell migration, when compared to MET alone. Furthermore, both combinations decreased the MET-induced increase in glycolysis, while also induced mitochondrial damage, altering cancer cell bioenergetics. This study introduces two novel therapeutic combinations, which enhance the anti-proliferative and apoptotic effects of MET on HepG2 cells, and hence, potentially combat the aggressiveness of HCC

A new marketing mix model to rescue the hospitality industry: Evidence from Egypt after the Arab Spring

After January 25th 2011 Egypt witnessed political, economic and social instability leading to drastic consequences in the hospitality and tourism industry. Thus unstable situation reflected on the deteriorated occupancy percentages that led to declined profit margins, higher employee layoffs and degraded quality of product and services. The objectives of this research is to examine how the Egyptian hospitality properties manage this dilemma through their marketing practices, and to propose a new marketing mix model that adds new layers of depth to the traditional marketing mix model. A methodological framework was designed to help in the assessment process of management practices pertaining to marketing initiatives during times of crisis. Results indicated the presence of tactical elements that assembled the traditional marketing mix model in the investigated hotels. However, these elements are not effectively used and the interaction between them not appears very clear. Results also indicated that the new proposed model would help in providing a framework for the Egyptian hospitality industry to maintain their competitive position during crisis time and avoiding undesired situations for labour force and decline of companies׳ revenues.The authors are grateful to the FEMISE (Forum Euro-Mediterranean of Institutes of Economics). This research received financial assistance of the European union in the contest of the FEMISE programme (projectFEM41-04). We are grateful to the anonymous referees of the journal. Earlier version published at the FEMISE International Conference13–14 February2016.The views expressed in this paper are those of the author and do not necessarily represent the MSA University

Machine learning approach for credit score analysis : a case study of predicting mortgage loan defaults

Dissertation submitted in partial fulfilment of the requirements for the degree of Statistics and Information Management specialized in Risk Analysis and ManagementTo effectively manage credit score analysis, financial institutions instigated techniques and models that are mainly designed for the purpose of improving the process assessing creditworthiness during the credit evaluation process. The foremost objective is to discriminate their clients – borrowers – to fall either in the non-defaulter group, that is more likely to pay their financial obligations, or the defaulter one which has a higher probability of failing to pay their debts. In this paper, we devote to use machine learning models in the prediction of mortgage defaults. This study employs various single classification machine learning methodologies including Logistic Regression, Classification and Regression Trees, Random Forest, K-Nearest Neighbors, and Support Vector Machine. To further improve the predictive power, a meta-algorithm ensemble approach – stacking – will be introduced to combine the outputs – probabilities – of the afore mentioned methods. The sample for this study is solely based on the publicly provided dataset by Freddie Mac. By modelling this approach, we achieve an improvement in the model predictability performance. We then compare the performance of each model, and the meta-learner, by plotting the ROC Curve and computing the AUC rate. This study is an extension of various preceding studies that used different techniques to further enhance the model predictivity. Finally, our results are compared with work from different authors.Para gerir com eficácia a análise de risco de crédito, as instituições financeiras desenvolveram técnicas e modelos que foram projetados principalmente para melhorar o processo de avaliação da qualidade de crédito durante o processo de avaliação de crédito. O objetivo final é classifica os seus clientes - tomadores de empréstimos - entre aqueles que tem maior probabilidade de pagar suas obrigações financeiras, e os potenciais incumpridores que têm maior probabilidade de entrar em default. Neste artigo, nos dedicamos a usar modelos de aprendizado de máquina na previsão de defaults de hipoteca. Este estudo emprega várias metodologias de aprendizado de máquina de classificação única, incluindo Regressão Logística, Classification and Regression Trees, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Para melhorar ainda mais o poder preditivo, a abordagem do conjunto de meta-algoritmos - stacking - será introduzida para combinar as saídas - probabilidades - dos métodos acima mencionados. A amostra deste estudo é baseada exclusivamente no conjunto de dados fornecido publicamente pela Freddie Mac. Ao modelar essa abordagem, alcançamos uma melhoria no desempenho do modelo de previsibilidade. Em seguida, comparamos o desempenho de cada modelo e o meta-aprendiz, plotando a Curva ROC e calculando a taxa de AUC. Este estudo é uma extensão de vários estudos anteriores que usaram diferentes técnicas para melhorar ainda mais o modelo preditivo. Finalmente, nossos resultados são comparados com trabalhos de diferentes autores

Chemical Vapor Deposition Grown Monolayer Graphene Microsensors with RF Ring Oscillator Backend Circuit

This work presents the integration of a backend RF ring oscillator readout circuit to transduce structural changes in CVD-grown monolayer graphene into an electrical signal and the implementation of it to detect physical changes such as radiation and flexural strain. The novelty in this work lies in the following (1) the ability of the sensor platform to overcome environmental effects, such as light photons and temperature changes, through the readout circuit, and (2) it opens the door for the scalability of CVD-grown graphene-based for sensors and devices. Thus, the introduced sensors solve several downsides in the state-of-the-art graphene-based radiation and strain devices, such as dependency on high atomic number, fading signal problems, dependency on electron excitation to generate a signal, difficulties in fabrication of single crystals, structural instabilities due to fabrication, and toxicity of high atomic number sensing elements. In our first implementation, we introduce a new radiation detection approach by measuring the change in resistance in correlation with the incident irradiation dose. This approach solves several of the problems reported in the literature by eliminating the necessity of structural stability or fabrication imperfections, avoiding bulk volumes regarding the sensing element\u27s geometry, and avoiding fading signal problems. Unlike traditional radiation sensors, cooling is not needed as the resolution is determined mainly by the level of structural damage, instead of the generated carriers due to incident radiation, with no toxicity problems as carbon-based materials are to be used. Sensitivity in gamma radiation detection of 7.86 was measured in response to cumulative gamma radiation dose ranging from 0 to 1 kGy which is suitable in food industry applications and homeland security. Senstivity of the platform to Beta was 27 times lower than gamma due to lower energy of gamma irradiation than that of beta irradiation. The new approach helps in minimizing background environmental effects (e.g., due to light and temperature), leading to an insignificant error in the output change in frequency of the order of 0.46% when operated in light versus dark conditions. The uncertainty in readings due to background light was calculated to be in the order of 1.34 Ω, which confirms the high stability and selectivity of the proposed sensor under different background effects. Our second implementation used the same platform on a flexible substrate as a new approach to detect flexural strain. This was achieved by dependency on the structure deformation method to overcome the limitations of the other mechanisms, such as low flexural strain sensitivity and lower gauge factors at low strain levels. Unlike traditional metal-foil strain sensors, the simple fabrication avoids structural damage in the monolayer graphene sheet. The sensor platform is also marked by having high flexibility and high conductivity combined with a high signal-to-noise ratio, with no need for calibration merged with high flexural sensitivity as monolayer graphene hinders creation of conductivity channels through straining. Our flexural strain sensor has a gauge factor of 64.36, corresponding to a change in frequency of 7.42%, achieving a sensitivity of around three times higher than sensors in literature working in the same strain range

Susceptibility of Economic Dipteran Fruit Flies to Entomopathogenic Nematodes

The present review article demonstrates laboratory and field evaluations of entomopathogenic nematodes (EPNs) against different developmental stages of fruit flies. The virulence of the EPNs  differed clearly  even on the same insect species and / or by the same nematode species. Such differences might be attributed  to some reasons such as the method of treatment as well as the concentrations of the tested nematodes. Fruit flies are among the most important insect pests infesting vegetables and fruits causing considerable losses in the yields worldwide. In laboratory studies, the tested nematodes proved to be highly virulent to larvae as  percentage of  mortality may reach 100 %.  As for treated pupae, at different ages, the results are variable and controversially; some studies revealed their moderate or high susceptibility to nematode infection and others indicated low susceptibility or resistance to infection .Treated adults, or those emerged from treated larvae or pupae,  are also susceptible to infection.  In semi-field and field trials, EPNs proved to be successful for reducing the populations of some fruit flies with up to 85 % at concentrations not less than 100 infective juveniles (IJs) / cm2 of soil. However, the field applications of commercial EPNs have been recommended to be 2.5 – 5 x 109 IJs / ha (25-50 IJs/cm2 of soil)

The Impact of Providing Chatbot Content on Developing the English Communication Skills among Al-Azhar Kindergarten Teachers

This research aimed at investigating the impact of using Chatbot content on improving the English communication skills of Al-Azhar Al-Sharif kindergarten teachers. The researcher used a quasi-experimental design to explain the difference occurred in the teachers` performance before and after the intervention of the Chatbot content.  This design required the researcher to develop a performance observation checklist; the main tool of this research. In addition, the researcher constructed the Chatbot content; the e-training program. Having the research design completed, thirty-three (33) female Azhari kindergarten teachers participated in this experiment. The performance observation checklist was used for evaluating the teachers` performance before and after the intervention of the Chatbot content,  and for measuring the teachers` retention of the acquired skills. At the end of the research, a statistical analysis of the results was applied. The results showed a statistically significant difference at the level of (0.05) on the performance observation checklist`s mean scores of the sample teachers' pre and post application of the Chatbot content in favor of the post-application. The second statistical analysis of the performance observation checklist showed that there is no significant difference between the sample teachers` mean scores in the post and follow-up application of the Chatbot content. These results proved the validity of the research main assumption that Chatbot content can improve the English communication skills of kindergarten teachers

Embedded Tuning Device for EFI Engine with Carbon Monoxide Adjusting Potentiometer

Engine tuning was always a major concern for cars manufacturers and cars users and many researches have been performed to make the engine tuning cope up with the modern world requirements for controlled pollution laws, economic fuel usage and to make the engine in a good condition for longer time. A transition state between carburetors which have no mechanical ability to correct lean or rich fuel mixtures until the 1980s and the current Air to fuel (A/F) ratio control method using oxygen sensors implemented in the car exhaust was the carbon monoxide potentiometer which was used to keep the A/F ratio at a certain level using a potentiometer to achieve an ideal fuel consumption and engine performance, but this method requires a frequent check by the car user and a mechanic assistant which makes the engine tuning for these cars (old cars which normally require tuning more frequent than the new cars) a difficult process and has a limited flexibility for the car user to make his own choice on the car performance as well. By using microcontrollers which can work perfectly with the engine control unit (ECU), this potentiometer can be replaced and can allow the user to do the tuning through different buttons which will make the tuning process simpler, more accurate and more flexible with more tuning options for the car driver, beside some more functions that will enhance the engine performance and fuel consumption in general and the ability to change any time from the old system (CO potentiometer) to the new embedded tuning device

Apsorpcija urana u izoliranim i referentnim bakterijskim vrstama

In the present study, uranium absorption capacity of Bacillus pantothenticus and Bacillus megaterium, previously isolated from the environmental air surrounding the 60Co gamma source, is reported. Pseudomonas putida and Pseudomonas chlororaphis were used as reference species. Concerning uranium uptake, the local species were more efficient than the reference ones. The maximum uptake was achieved by B. megaterium and B. pantothenticus at 20 microgram U mL-1 and by B. pantothenticus at 30 microgram U mL-1. The transmission electron microscope examination indicated that uranium was absorbed onto the cell surface of the studied isolates. Furthermore, the increase in biomass concentration has shown an increase in the total amount of uranium removed. Dead cells exhibited uranium uptake to the same or greater extent than living cells. B. pantothenticus, P. putida, and P. chlororaphis achieved maximum uptake at pH 4.0, whereas B. megaterium it was pH 6.0. Temperature had an important role in uranium absorption of all the studied species except B. pantothenticus,. Metabolic inhibitors did not affect the uptake.U radu je proučavan kapacitet apsorpcije urana bakterija Bacillus pantothenticus i Bacillus megaterium izoliranih iz zraka izloženog izvoru gama-zračenja iz 60Co. Te bakterije su se pokazale učinkovitije u usporedbi s referentnim vrstama Pseudomonas putida i Pseudomonas chlororaphis. Maksimalna postignuta koncentracija bila je 10, 20, odnosno 30 µgU mL-1. Nadalje, povećanje koncentracije u biomasi pratilo je povećanje ukupne količine uklonjenog urana. Mrtve stanice su apsorbirale uran u istoj ili većoj mjeri nego žive stanice. Maksimum apsorpcije B. pantothenticus, P. putida i P. chlororaphis postignut je pri pH 4,0, a B. megaterium pri pH 6,0. Kod svih ispitivanih vrsta osim B. pantothenticus, temperatura je značajno utjecala na apsorpciju dok inhibitori metaboličkih reakcija nisu utjecali. Pretraživanje transmisijskim elektronskim mikroskopom ukazalo je da se uran apsorbirao na površinu stanice

Spectrophotometric methods of analysis

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