Zagazig University

This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at and .

Abstract Removal of heavy metal ions from wastewater is of prime importance for a clean environment and human health. Different reported methods were devoted to heavy metal ions removal from various wastewater sources. These methods could be classified into adsorption-, membrane-, chemical-, electric-, and photocatalytic-based treatments. This paper comprehensively and critically reviews and discusses these methods in terms of used agents/adsorbents, removal efficiency, operating conditions, and the pros and cons of each method. Besides, the key findings of the previous studies reported in the literature are summarized. Generally, it is noticed that most of the recent studies have focused on adsorption techniques. The major obstacles of the adsorption methods are the ability to remove different ion types concurrently, high retention time, and cycling stability of adsorbents. Even though the chemical and membrane methods are practical, the large-volume sludge formation and post-treatment requirements are vital issues that need to be solved for chemical techniques. Fouling and scaling inhibition could lead to further improvement in membrane separation. However, pre-treatment and periodic cleaning of membranes incur additional costs. Electrical-based methods were also reported to be efficient; however, industrial-scale separation is needed in addition to tackling the issue of large-volume sludge formation. Electric- and photocatalytic-based methods are still less mature. More attention should be drawn to using real wastewaters rather than synthetic ones when investigating heavy metals removal. Future research studies should focus on eco-friendly, cost-effective, and sustainable materials and methods.

For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease-one target-one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation.

Medicinal plants have been used from ancient times for human healthcare as in the form of traditional medicines, spices, and other food components. Garlic (Allium sativum L.) is an aromatic herbaceous plant that is consumed worldwide as food and traditional remedy for various diseases. It has been reported to possess several biological properties including anticarcinogenic, antioxidant, antidiabetic, renoprotective, anti-atherosclerotic, antibacterial, antifungal, and antihypertensive activities in traditional medicines. A. sativum is rich in several sulfur-containing phytoconstituents such as alliin, allicin, ajoenes, vinyldithiins, and flavonoids such as quercetin. Extracts and isolated compounds of A. sativum have been evaluated for various biological activities including antibacterial, antiviral, antifungal, antiprotozoal, antioxidant, anti-inflammatory, and anticancer activities among others. This review examines the phytochemical composition, pharmacokinetics, and pharmacological activities of A. sativum extracts as well as its main active constituent, allicin.

Isolated hydrogen atoms absorbed on graphene are predicted to induce magnetic moments. Here we demonstrate that the adsorption of a single hydrogen atom on graphene induces a magnetic moment characterized by a ~20-millielectron volt spin-split state at the Fermi energy. Our scanning tunneling microscopy (STM) experiments, complemented by first-principles calculations, show that such a spin-polarized state is essentially localized on the carbon sublattice opposite to the one where the hydrogen atom is chemisorbed. This atomically modulated spin texture, which extends several nanometers away from the hydrogen atom, drives the direct coupling between the magnetic moments at unusually long distances. By using the STM tip to manipulate hydrogen atoms with atomic precision, it is possible to tailor the magnetism of selected graphene regions.

Flavonoids are a class of natural substances present in plants, fruits, vegetables, wine, bulbs, bark, stems, roots, and tea. Several attempts are being made to isolate such natural products, which are popular for their health benefits. Flavonoids are now seen as an essential component in a number of cosmetic, pharmaceutical, and medicinal formulations. Quercetin is the major polyphenolic flavonoid found in food products, including berries, apples, cauliflower, tea, cabbage, nuts, and onions that have traditionally been treated as anticancer and antiviral, and used for the treatment of allergic, metabolic, and inflammatory disorders, eye and cardiovascular diseases, and arthritis. Pharmacologically, quercetin has been examined against various microorganisms and parasites, including pathogenic bacteria, viruses, and Plasmodium, Babesia, and Theileria parasites. Additionally, it has shown beneficial effects against Alzheimer’s disease (AD), and this activity is due to its inhibitory effect against acetylcholinesterase. It has also been documented to possess antioxidant, antifungal, anti-carcinogenic, hepatoprotective, and cytotoxic activity. Quercetin has been documented to accumulate in the lungs, liver, kidneys, and small intestines, with lower levels seen in the brain, heart, and spleen, and it is extracted through the renal, fecal, and respiratory systems. The current review examines the pharmacokinetics, as well as the toxic and biological activities of quercetin.

In plants, copper is an essential micronutrient required for photosynthesis. Two of the most abundant copper proteins, plastocyanin and copper/zinc superoxide dismutase, are found in chloroplasts. Whereas plastocyanin is essential for photo-autotrophic growth, copper/zinc superoxide dismutase is dispensable and in plastids can be replaced by an iron superoxide dismutase when copper is limiting. The down-regulation of copper/zinc superoxide dismutase expression in response to low copper involves a microRNA, miR398. Interestingly, in Arabidopsis and other plants, three additional microRNA families, miR397, miR408, and miR857, are predicted to target the transcripts for the copper protein plantacyanin and members of the laccase copper protein family. We confirmed the predicted targets of miR397, miR408, and miR857 experimentally by cleavage site analysis. To study the spatial expression pattern of these microRNAs and the effect of copper on their expression, we analyzed Arabidopsis grown hydroponically on different copper regimes. On low amounts of copper the plants accumulated miR397, miR408, and miR857. The microRNA expression pattern was negatively correlated with the accumulation of transcripts for plantacyanin and laccases. Furthermore, the expression of other laccases that are not predicted targets for known microRNAs was similarly regulated in response to copper. For some of these laccases, the regulation was disrupted in a microRNA maturation mutant (hen1-1), suggesting the presence of other copper-regulated microRNAs. Thus, in Arabidopsis, microRNA-mediated down-regulation is a general mechanism to regulate nonessential copper proteins. We propose that this mechanism allows plants to save copper for the most essential functions during limited copper supply.

The rapid advances in performance and miniaturization of electronics and high power devices resulted in huge heat flux values that need to be dissipated effectively. The average heat flux in computer chips is expected to reach 2–4.5 MW/m2 with local hot spots 12–45 MW/m2 while in IGBT modules, the heat flux at the chip level can reach 6.5–50 MW/m2. Flow boiling in microchannels is one of the most promising cooling methods for these and similar devices due to the capability of achieving very high heat transfer rates with small variations in the surface temperature. However, several fundamental issues are still not understood and this hinders the transition from laboratory research to commercial applications. The present paper starts with a discussion of the possible applications of flow boiling in microchannels in order to highlight the challenges in the thermal management for each application. In this part, the different integrated systems using microchannels were also compared. The comparison demonstrated that miniature cooling systems with a liquid pump were found to be more efficient than miniature vapour compression refrigeration systems. The paper then presents experimental research on flow boiling in single tubes and rectangular multichannels to discuss the following fundamental issues: (1) the definition of microchannel, (2) flow patterns and heat transfer mechanisms, (3) flow instability and reversal and their effect on heat transfer rates, (4) effect of channel surface characteristics and (5) prediction of critical heat flux. Areas where more research is needed were clearly mentioned. In addition, correlations for the prediction of the flow pattern transition boundaries and heat transfer coefficients in small to mini/micro diameter tubes were developed recently by the authors and presented in this paper.

In recent years, there is a growing interest towards the green synthesis of metal nanoparticles, particularly from plants; however, yet no published study on the synthesis of ZnO.NPs using the Deverra tortuosa extract. Through this study, zinc oxide nanoparticles (ZnO.NPs) have been synthesized based on using the environmentally benign extract of the aerial parts of D. tortuosa as a reducing and capping agent. ZnO.NPs synthesis was confirmed using UV-Visible (UV-Vis) spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD) and High Resolution-Transmission Electron Microscope (HR-TEM). The qualitative and quantitative analyses of plant extract were done. The potential anticancer activity was in vitro investigated against two cancer cell lines (human colon adenocarcinoma "Caco-2" and human lung adenocarcinoma "A549") compared to their activities on the human lung fibroblast cell line (WI38) using the MTT assay. Both the aqueous extract and ZnO.NPs showed a remarkable selective cytotoxicity against the two examined cancer cell lines.

Probiotics, like lactic acid bacteria, are non-pathogenic microbes that exert health benefits to the host when administered in adequate quantity. Currently, research is being conducted on the molecular events and applications of probiotics. The suggested mechanisms by which probiotics exert their action include; competitive exclusion of pathogens for adhesion sites, improvement of the intestinal mucosal barrier, gut immunomodulation, and neurotransmitter synthesis. This review emphasizes the recent advances in the health benefits of probiotics and the emerging applications of probiotics in the food industry. Due to their capability to modulate gut microbiota and attenuate the immune system, probiotics could be used as an adjuvant in hypertension, hypercholesterolemia, cancer, and gastrointestinal diseases. Considering the functional properties, probiotics are being used in the dairy, beverage, and baking industries. After developing the latest techniques by researchers, probiotics can now survive within harsh processing conditions and withstand GI stresses quite effectively. Thus, the potential of probiotics can efficiently be utilized on a commercial scale in food processing industries.

H3Africa is developing capacity for health-related genomics research in Africa

Recent evidence suggest that resistance to praziquantel (PZQ) may be developing. This would not be surprising in countries like Egypt where the drug has been used aggressively for more that 10 years. The classic phenotype of drug resistance is a significant increase in the 50% effective dose value of isolates retrieved from patients not responding to the drug. In a previous publication, we reported that such phenotypes have been isolated from humans infected with Schistosoma mansoni. Since the action of PZQ may be dependent upon the drug and host factors, most notably the immune system, we analyzed the quantitative effects of PZQ on single worms that differed in their response to PZQ when maintained in mice. Our hypothesis was that the in vitro action of the drug would correlate with it in vivo action. We confirmed this hypothesis and conclude that the in vitro action of the drug is related to its in vivo action. Knowing this relationship will assist in our ability to detect or survey for the PZQ resistant phenotype in human populations.

A commonly held view is that nanocarriers conjugated to polyethylene glycol (PEG) are non-immunogenic. However, many studies have reported that unexpected immune responses have occurred against PEG-conjugated nanocarriers. One unanticipated response is the rapid clearance of PEGylated nanocarriers upon repeat administration, called the accelerated blood clearance (ABC) phenomenon. ABC involves the production of antibodies toward nanocarrier components, including PEG, which reduces the safety and effectiveness of encapsulated therapeutic agents. Another immune response is the hypersensitivity or infusion reaction referred to as complement (C) activation-related pseudoallergy (CARPA). Such immunogenicity and adverse reactivities of PEGylated nanocarriers may be of potential concern for the clinical use of PEGylated therapeutics. Accordingly, screening of the immunogenicity and CARPA reactogenicity of nanocarrier-based therapeutics should be a prerequisite before they can proceed into clinical studies. This review presents PEGylated liposomes, immunogenicity of PEG, the ABC phenomenon, C activation and lipid-induced CARPA from a toxicological point of view, and also addresses the factors that influence these adverse interactions with the immune system.

Clinical research usually involves patients with a certain disease or a condition. The generalizability of clinical research findings is based on multiple factors related to the internal and external validity of the research methods. The main methodological issue that influences the generalizability of clinical research findings is the sampling method. In this educational article, we are explaining the different sampling methods in clinical research.

<p class="Body">Emergency physicians, like other specialists, are faced with different patients and various situations each day. They have to use ancillary diagnostic tools like laboratory tests and imaging studies to be able to manage them. In most cases, numerous tests are available. Tests with the least error and the most accuracy are more desirable. The power of a test to separate patients from healthy ones determines its accuracy and diagnostic value. Therefore, a test with 100% accuracy should be the first choice. This does not happen in reality as the accuracy of a test varies for different diseases and in different situations. For example, the value of D-dimer for diagnosing pulmonary embolism varies based on pre-test probability. It shows high accuracy in low risk patient and low accuracy in high risk ones. The characteristics of a test that reflects the aforementioned abilities are accuracy, sensitivity, specificity, positive and negative predictive values and positive and negative likelihood ratios. In this educational review, we will simply define and calculate the accuracy, sensitivity, and specificity of a hypothetical test.

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

BACKGROUND: e-learning was underutilized in the past especially in developing countries. However, the current crisis of the COVID-19 pandemic forced the entire world to rely on it for education. OBJECTIVES: To estimate the university medical staff perceptions, evaluate their experiences, recognize their barriers, challenges of e-learning during the COVID-19 pandemic, and investigate factors influencing the acceptance and use of e-learning as a tool teaching within higher education. METHODS: Data was collected using an electronic questionnaire with a validated Technology Acceptance Model (TAM) for exploring factors that affect the acceptance and use of e-learning as a teaching tool among medical staff members, Zagazig University, Egypt. RESULTS: The majority (88%) of the staff members agreed that the technological skills of giving the online courses increase the educational value of the experience of the college staff. The rate of participant agreement on perceived usefulness, perceived ease of use, and acceptance of e-learning was (77.1%, 76.5%, and 80.9% respectively). The highest barriers to e-learning were insufficient/ unstable internet connectivity (40%), inadequate computer labs (36%), lack of computers/ laptops (32%), and technical problems (32%). Younger age, teaching experience less than 10 years, and being a male are the most important indicators affecting e-learning acceptance. CONCLUSION: This study highlights the challenges and factors influencing the acceptance, and use of e-learning as a tool for teaching within higher education. Thus, it will help to develop a strategic plan for the successful implementation of e-learning and view technology as a positive step towards evolution and change.

Abstract The chemical regeneration process has been extensively applied to reactivate biochar, supporting its reusability and leading to significant operating cost reduction. However, no recent review discusses the effectiveness of biochar chemical regeneration. Thus, this article comprehensively reviews the chemical regeneration of biochar contaminated with organic and inorganic pollutants. Performance of the chemical regeneration depends on adsorption mechanism, functional groups, adsorbent pore structure, and changes in active adsorbent sites. Secondary contamination is one of the challenges facing the sustainable adaptation of the chemical regeneration process in the industry. The paper discusses these challenges and draws a roadmap for future research to support sustainable wastewater treatment by biochar.

The increasing demand for orthopedic implants has driven the search for materials that combine strength, biocompatibility, and long lifetime. Compared to stainless steel and Co-Cr-based alloys, titanium (Ti) and its alloys are favored for biomedical implants because of their high strength, corrosion resistance, and biocompatibility. This comprehensive review delivers a wide overview of the field of titanium-based biomaterials for orthopedic implants applications, focusing on their types, mechanical and chemical resistance, surface modifications, innovations in fabrication techniques, titanium matrix composites, and machine learning advancements. Titanium alloys of different crystalline phases, including α, near-α, (α + β), β, and shape memory alloys, offer diverse options for orthopedic applications. Strengthening properties, wear, fatigue, and corrosion resistance are crucial factors influencing the performance and reliability of titanium implants. Moreover, this review discussed the challenges to titanium-based biomaterial durability through surface modifications to enhance their biofunction, wear resistance, corrosion resistance, and antibacterial properties. Recent developments in fabrication techniques for titanium-based biomaterials are also discussed. Eventually, this review investigated how machine learning (ML) revolutionized titanium orthopedic implants by providing insights into the behavior of new alloys, aiding in manufacturing optimization, allowing for real-time quality control, and advancing the development of personalized, biocompatible, and reliable implants.

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.