Genoprotective and Genotoxic Effects of Thymoquinone on Doxorubicin-Induced Damage in Isolated Human Leukocytes

Purpose: To investigate the potential genoprotective effects of thymoquinone (TQ) on the cytotoxicity and genotoxicity-induced by doxorubicin (DXR), a key chemotherapeutic drug.Methods: Isolated human peripheral leukocytes were treated with varying concentrations of TQ (5.0, 10.0, or 20.0 µM) alone or in combination with DXR (0.15 ìg/mL). Comet assays and apoptotic cell studies were performed to evaluate the effect of TQ on the cytotoxicity and genotoxicity-induced by DXR.Results: TQ treatment, alone, (5.0, 10, or 20 µM) increased DNA damage index (DI) in a concentrationdependent manner (0.64 ± 0.09, 0.84 ± 0.07, and 0.93 ± 0.06, respectively). DXR (0.15 µg/mL) increased DI (1.67 ± 0.09) compared with no treatment (0.34 ± 0.03). However, when TQ was administered with DXR, DI was significantly reduced (0.96 ± 0.04, 0.80 ± 0.05, and 0.79 ± 0.04) compared with DXR alone (1.67 ± 0.09). Similarly, apoptotic cells decreased (10.8, 11.8 and 14.2 %) compared with that induced by DXR alone (27.6 %).Conclusion: TQ can be used as a genoprotective agent against  DXR-induced genotoxicity. The dual behavior of TQ observed in this study is dose-dependent and therefore its mechanism of action needs to be clarified in future studies.Keywords: Thymoquinone, Genotoxicity, Genoprotection, Doxorubicin, Apoptotic, Oxidative stress, DNA damage inde

Lightning search algorithm: a comprehensive survey

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA’s applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper

The Arithmetic Optimization Algorithm

Abstract 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 ( ), Division (), Subtraction (), and Addition ()). 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

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm

Nature-inspired optimization algorithms for text document clustering—a comprehensive analysis

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems

Neuropsychological function is related to irritable bowel syndrome in women with premenstrual syndrome and dysmenorrhea

Background There is increasing evidence demonstrating the co-occurrence of primary dysmenorrhea (PD), premenstrual syndrome (PMS), and irritable bowel syndrome (IBS) in women. This study aimed to investigate whether women who have symptoms of IBS in addition to PD and PMS also report more severe or frequent menstruation-associated symptoms and psychological complications compared to women with PD and PMS alone. Methods The study group included 182 female University students aged 18–25 years. IBS was diagnosed using the Rome III criteria. The severity of PMS and PD was determined using a 10-point visual analog scale and PSST (Premenstrual Syndrome Screening Tool), respectively. Neuropsychological functions including cognitive function, depression score, anxiety score, stress, insomnia, daytime sleepiness, quality of life and personality were assessed using standard questionnaires. Results Of the 182 young females, 31 (17.0%) had IBS. Average days of bleeding during the menstrual cycle and mean pain severity on the PSST scale were significantly greater in the group with IBS compared to the non-IBS group (p < 0.01). The non-IBS individuals scored more favorably than the women with IBS with respect to severity of depression, insomnia, daytime sleepiness (p < 0.05). The PSST scores were significantly correlated with scores for depression (r = 0.29; p < 0.001), anxiety (r = 0.28; p < 0.001), stress (r = 0.32; p < 0.001), insomnia (r = 0.34; p < 0.001) and daytime sleepiness (r = 0.31; p < 0.001); while, they were negatively correlated with cognitive abilities (r = − 0.20; p = 0.006) and quality of life (r = − 0.42; p < 0.001). Linear regression analysis showed that the PSST scores were possibly significant factors in determining the scores for depression, anxiety, stress, quality of life, insomnia and daytime sleepiness (p < 0.05). Conclusion IBS is related to psychological comorbidities, in particular depression, sleep problems and menstrual-associated disorders. IBS may exacerbate the features of PMS which should be taken into account in the management of PMS

The effect of stress on the pharmacokinetics and pharmacodynamics of glibenclamide in diabetic rats

Optimal management of the diabetic patient includes normalization of glucose concentration. Attainment of this goal is difficult because stress has long been shown to have a major effect on metabolic activity. The aim of this study was to assess the effect of stress on the pharmacokinetics and dynamics of glibenclamide in normal and diabetic rats. In this respect, administration of glibenclamide (1.4 mg/kg, p.o.) significantly reduced the blood glucose level estimated after an intravenous challenge dose (4 ml/kg) of 50% dextrose. Peak drug effect occurred at about 2 h in the control on diabetic group and this effect was clearly evident over a 6 h period in the diabetic group. The stressed diabetic group showed consistently higher blood glucose level at all time points than the nonstressed diabetic controls. Stress was also associated with significant reductions in glibenclamide Cp-max and AUC(0-infinity) and an increase in the T-max. These results suggest that the response to glibenclamide in diabetics may be strongly modified by stress through a number of mechanisms. Changes in the bioavailability of the drug and activation of sympathetic nervous system and the hypothalamic-pituitary-adrenocortical axis are potential candidates. Further clinical and experimental studies in relevant models may, however, be needed to characterize fully and relate this effect to rational pharmacotherapy of type II diabetes

Survey on Twitter Sentiment Analysis: Architecture, Classifications, and Challenges

Sentiment analysis, also called opinion mining, is an extensive research field due to the rapid growth in social media. It has widespread applications in almost all areas of today’s life, from business services to political field. Sentiment analysis refers to the opinion or feelings of a person toward a particular topic. It gives results or a subjective impression, not facts, and can be expressed as a polarity to negative, positive, or neutral. Sentiments can be analyzed by utilizing statistics, natural language programming, or machine learning techniques. These techniques are implemented on data previously collected from social networking sites or blogs, the most famous of which is Twitter. Twitter is one of the essential sources of people’s opinions on various topics. This source permits individuals to state their views and offer different perspectives on any field. Many organizations have become interested in analyzing people’s feelings through social networks, especially in political and economic domains. The main task is to classify the level of messages or tweets to their polarity. In this research, we will look at the most important approaches used in sentiment analysis and how they process the collected data. An explanation of the feature extraction methods will be presented. We will also clarify the levels of sentiment analysis and the challenges facing sentiment analysis

Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing

This paper introduces IAO, a new swarm intelligence approach for addressing the challenge of task scheduling in cloud computing. The proposed method uses conventional Aquila Optimizer (AO) and Particle Swarm Optimizer (PSO) as a hybrid method based on a novel transition mechanism. The proposed hybrid method, IAO, combined the AO and PSO to avoid the weaknesses they face; these weaknesses are trapped in the local search area and have low solution diversity. The proposed transition mechanism is proposed to acquire proper changes between the search operators in order to keep the improvements; it changes between them when any algorithm gets stuck or the solutions diversity decreases. Several scenarios are conducted and tested to validate the suggested method’s ability to address the task scheduling problem; these scenarios contain various tasks (i.e., 600, 1000, and 2000). The obtained results are compared with other well-known methods in terms of Max, Mean, Min of the Expected Complete Time (ECT), Friedman ranking test, and Wilcoxon signed-rank test. The proposed IAO method achieved better results and promising compared to other comparative methods; it is an excellent scheduling approach for solving any related scheduling problem

Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters

Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301e-04 and 5.9135e-07. Similarly for double diode are 7.4396e-04 and 3.1849e-05, respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505±1.0971e-17, which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505±9.6235e-14 and 0.0084505±2.5235e-9). The results show the proposed model's superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563