Menoufia University

OBJECTIVES: To evaluate Ki67 immunoexpression pattern in Saudi breast cancer (BC) patients and investigate any possible predictive or prognostic value for Ki67. METHODS: This is a retrospective study designed to quantitatively assess the Ki67 proliferative index (PI) in retrieved paraffin blocks of 115 Saudi BC patients diagnosed between January 2005 and March 2015 at the Department of Pathology, King Fahd Hospital, Al Madinah Al Munawarah, Kingdom of Saudi Arabia. The Ki67 PI was correlated with individual and combined immunoprofile data of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2/neu) with their clinicopathological parameters. RESULTS: Ki67 immunoreactivity was highly expressed (greater than 25% of the tumor cells were positive) in 85 (73.9%) patients. The Ki67 PI was significantly associated with poor prognostic clinicopathological parameters including old age (p less than 0.02), high tumor grade (p less than 0.01), lymph node metastasis (p less than 0.001), and Her-2/neu positivity (p less than 0.009). However, the association with ER positivity, PR positivity, tumor size, and lymphovascular invasion were not statistically significant. The Ki67 PI was significantly associated with BC molecular subtypes that were Her2/neu positive (luminal B and HER-2) subtypes compared with the Her2/neu negative (luminal A) subtype (p less than 0.04). CONCLUSION: The Ki67 PI is significantly higher in Saudi BC patients comparing with the reported literature. Ki67 PI was highest in the HER-2 and luminal-B molecular subtypes. Along with other prognostic indicators, Ki67 PI may be useful in predicting prognosis and management of Saudi BC patients.

IMPORTANCE: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. OBJECTIVE: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. EVIDENCE REVIEW: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). FINDINGS: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. CONCLUSIONS AND RELEVANCE: The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.

Drought stress, being the inevitable factor that exists in various environments without recognizing borders and no clear warning thereby hampering plant biomass production, quality, and energy. It is the key important environmental stress that occurs due to temperature dynamics, light intensity, and low rainfall. Despite this, its cumulative, not obvious impact and multidimensional nature severely affects the plant morphological, physiological, biochemical and molecular attributes with adverse impact on photosynthetic capacity. Coping with water scarcity, plants evolve various complex resistance and adaptation mechanisms including physiological and biochemical responses, which differ with species level. The sophisticated adaptation mechanisms and regularity network that improves the water stress tolerance and adaptation in plants are briefly discussed. Growth pattern and structural dynamics, reduction in transpiration loss through altering stomatal conductance and distribution, leaf rolling, root to shoot ratio dynamics, root length increment, accumulation of compatible solutes, enhancement in transpiration efficiency, osmotic and hormonal regulation, and delayed senescence are the strategies that are adopted by plants under water deficit. Approaches for drought stress alleviations are breeding strategies, molecular and genomics perspectives with special emphasis on the omics technology alteration i.e., metabolomics, proteomics, genomics, transcriptomics, glyomics and phenomics that improve the stress tolerance in plants. For drought stress induction, seed priming, growth hormones, osmoprotectants, silicon (Si), selenium (Se) and potassium application are worth using under drought stress conditions in plants. In addition, drought adaptation through microbes, hydrogel, nanoparticles applications and metabolic engineering techniques that regulate the antioxidant enzymes activity for adaptation to drought stress in plants, enhancing plant tolerance through maintenance in cell homeostasis and ameliorates the adverse effects of water stress are of great potential in agriculture.

Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework; namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. Materials and Methods: Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case. Results: Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. Conclusions: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work.

Enzymes play vital roles in diverse industrial sectors and are essential components of many industrial products. Immobilized enzymes possess higher resistance to environmental changes and can be recovered/recycled easily when compared to the free forms. The primary benefit of immobilization is protecting the enzymes from the harsh environmental conditions (e.g., elevated temperatures, extreme pH values, etc.). The immobilized enzymes can be utilized in various large-scale industries, e.g., medical, food, detergent, textile, and pharmaceutical industries, besides being used in water treatment plants. According to the required application, a suitable enzyme immobilization technique and suitable carrier materials are chosen. Enzyme immobilization techniques involve covalent binding, encapsulation, entrapment, adsorption, etc. This review mainly covers enzyme immobilization by various techniques and their usage in different industrial applications starting from 1992 until 2022. It also focuses on the multiscale operation of immobilized enzymes to maximize yields of certain products. Lastly, the severe consequence of the COVID-19 pandemic on global enzyme production is briefly discussed.

Hepatocellular carcinoma (HCC) is the commonest primary malignant cancer of the liver in the world. Given that the burden of chronic liver disease is expected to rise owing to increasing rates of alcoholism, hepatitis B and C prevalence and obesity-related fatty liver disease, it is expected that the incidence of HCC will also increase in the foreseeable future. This article summarizes the international epidemiology, the risk factors and the pathogenesis of HCC, including the roles of viral hepatitis, toxins, such as alcohol and aflatoxin, and insulin resistance.

Quercetin (Que) and its derivatives are naturally occurring phytochemicals with promising bioactive effects. The antidiabetic, anti-inflammatory, antioxidant, antimicrobial, anti-Alzheimer's, antiarthritic, cardiovascular, and wound-healing effects of Que have been extensively investigated, as well as its anticancer activity against different cancer cell lines has been recently reported. Que and its derivatives are found predominantly in the Western diet, and people might benefit from their protective effect just by taking them via diets or as a food supplement. Bioavailability-related drug-delivery systems of Que have also been markedly exploited, and Que nanoparticles appear as a promising platform to enhance their bioavailability. The present review aims to provide a brief overview of the therapeutic effects, new insights, and upcoming perspectives of Que.

Abstract Herein, a simple approach based on tailoring the surface charge of nanoparticles, NPs, during the preparation to boost the electrostatic attraction between NPs and the organic pollutant was investigated. In this study, chargeable titania nanoparticles (TiΟ 2 NPs) were synthesized via a hydrothermal route under different pH conditions (pH = 1.6, 7.0 and 10). The prepared TiΟ 2 NPs were fully characterized via various techniques including; transmission electron microscopy (TEM), X-ray diffraction (XRD), N 2 adsorption/desorption, X-ray photoelectron spectroscopy (XPS), Ultraviolet–visible spectroscopy (UV-Vis) and dynamic light scattering (DLS). The influence of the preparation pH on the particle size, surface area and band gap was investigated and showed pH-dependent behavior. The results revealed that upon increasing the pH value, the particle size decreases and lead to larger surface area with less particles agglomeration. Additionally, the effect of pH on the surface charge was monitored by XPS to determine the amount of hydroxyl groups on the TiO 2 NPs surface. Furthermore, the photocatalytic activity of the prepared TiΟ 2 NPs towards methylene blue (MB) photodegradation was manifested. The variation in the preparation pH affected the point of zero charge (pH PZC ) of TiO 2 NPs, subsequently, different photocatalytic activities based on electrostatic interactions were observed. The optimum efficiency obtained was 97% at a degradation rate of 0.018 min −1 using TiO 2 NPs prepared at pH 10.

Pollination plays a significant role in the agriculture sector and serves as a basic pillar for crop production. Plants depend on vectors to move pollen, which can include water, wind, and animal pollinators like bats, moths, hoverflies, birds, bees, butterflies, wasps, thrips, and beetles. Cultivated plants are typically pollinated by animals. Animal-based pollination contributes to 30% of global food production, and bee-pollinated crops contribute to approximately one-third of the total human dietary supply. Bees are considered significant pollinators due to their effectiveness and wide availability. Bee pollination provides excellent value to crop quality and quantity, improving global economic and dietary outcomes. This review highlights the role played by bee pollination, which influences the economy, and enlists the different types of bees and other insects associated with pollination.

With the rapid growth of Internet of Things (IoT) applications, the classic centralized cloud computing paradigm faces several challenges such as high latency, low capacity and network failure. To address these challenges, fog computing brings the cloud closer to IoT devices. The fog provides IoT data processing and storage locally at IoT devices instead of sending them to the cloud. In contrast to the cloud, the fog provides services with faster response and greater quality. Therefore, fog computing may be considered the best choice to enable the IoT to provide efficient and secure services for many IoT users. This paper presents the state-of-the-art of fog computing and its integration with the IoT by highlighting the benefits and implementation challenges. This review will also focus on the architecture of the fog and emerging IoT applications that will be improved by using the fog model. Finally, open issues and future research directions regarding fog computing and the IoT are discussed.

Cancer remains one of the most lethal diseases worldwide. There is an urgent need for new drugs with novel modes of action and thus considerable research has been conducted for new anticancer drugs from natural sources, especially plants, microbes and marine organisms. Marine populations represent reservoirs of novel bioactive metabolites with diverse groups of chemical structures. This review highlights the impact of marine organisms, with particular emphasis on marine plants, algae, bacteria, actinomycetes, fungi, sponges and soft corals. Anti-cancer effects of marine natural products in in vitro and in vivo studies were first introduced; their activity in the prevention of tumor formation and the related compound-induced apoptosis and cytotoxicities were tackled. The possible molecular mechanisms behind the biological effects are also presented. The review highlights the diversity of marine organisms, novel chemical structures, and chemical property space. Finally, therapeutic strategies and the present use of marine-derived components, its future direction and limitations are discussed.

Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019. First, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-of-the-art outlier detection methods and further discuss them in detail in terms of their performance. Second, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.

Post-translational histone modifications are known to be altered in cancer cells, and loss of selected histone acetylation and methylation marks has recently been shown to predict patient outcome in human carcinoma. Immunohistochemistry was used to detect a series of histone lysine acetylation (H3K9ac, H3K18ac, H4K12ac, and H4K16ac), lysine methylation (H3K4me2 and H4K20me3), and arginine methylation (H4R3me2) marks in a well-characterized series of human breast carcinomas (n = 880). Tissue staining intensities were assessed using blinded semiquantitative scoring. Validation studies were done using immunofluorescence staining and Western blotting. Our analyses revealed low or absent H4K16ac in the majority of breast cancer cases (78.9%), suggesting that this alteration may represent an early sign of breast cancer. There was a highly significant correlation between histone modifications status, tumor biomarker phenotype, and clinical outcome, where high relative levels of global histone acetylation and methylation were associated with a favorable prognosis and detected almost exclusively in luminal-like breast tumors (93%). Moderate to low levels of lysine acetylation (H3K9ac, H3K18ac, and H4K12ac), lysine (H3K4me2 and H4K20me3), and arginine methylation (H4R3me2) were observed in carcinomas of poorer prognostic subtypes, including basal carcinomas and HER-2-positive tumors. Clustering analysis identified three groups of histone displaying distinct pattern in breast cancer, which have distinct relationships to known prognostic factors and clinical outcome. This study identifies the presence of variations in global levels of histone marks in different grades, morphologic types, and phenotype classes of invasive breast cancer and shows that these differences have clinical significance.

This study explored the correlates of climate anxiety in a diverse range of national contexts. We analysed cross-sectional data gathered in 32 countries (N = 12,246). Our results show that climate anxiety is positively related to rate of exposure to information about climate change impacts, the amount of attention people pay to climate change information, and perceived descriptive norms about emotional responding to climate change. Climate anxiety was also positively linked to pro-environmental behaviours and negatively linked to mental wellbeing. Notably, climate anxiety had a significant inverse association with mental wellbeing in 31 out of 32 countries. In contrast, it had a significant association with pro-environmental behaviour in 24 countries, and with environmental activism in 12 countries. Our findings highlight contextual boundaries to engagement in environmental action as an antidote to climate anxiety, and the broad international significance of considering negative climate-related emotions as a plausible threat to wellbeing.

PURPOSE: Triple-negative (TN; estrogen receptor, progesterone receptor, and HER-2 negative) cancer and basal-like breast cancer (BLBC) are associated with poor outcome and lack the benefit of targeted therapy. It is widely perceived that BLBC and TN tumors are synonymous and BLBC can be defined using a TN definition without the need for the expression of basal markers. EXPERIMENTAL DESIGN: We have used two well-defined cohorts of breast cancers with a large panel of biomarkers, BRCA1 mutation status, and follow-up data to compare the clinicopathologic and immunohistochemical features of TN tumors expressing one or more of the specific basal markers (CK5/6, CK17, CK14, and epidermal growth factor receptor; BLBC) with those TN tumors that express none of these markers (TN3BKE-). RESULTS: Here, we show that although the morphologic features of BLBC are not significantly different from that of TN3BKE- tumors, BLBC showed distinct clinical and immunophenotypic differences. BLBC showed a statistically significant association with the expression of the hypoxia-associated factor (CA9), neuroendocrine markers, and other markers of poor prognosis such as p53. A difference in the expression of cell cycle-associated proteins and biomarkers involved in the immunologic portrait of tumors was seen. Compared with TN3BKE- tumors, BLBC was positively associated with BRCA1 mutation status and showed a unique pattern of distant metastasis, better response to chemotherapy, and shorter survival. CONCLUSION: TN breast cancers encompass a remarkably heterogeneous group of tumors. Expression of basal markers identifies a biologically and clinically distinct subgroup of TN tumors, justifying the use of basal markers (in TN tumors) to define BLBC.

Biochar is gaining significant attention due to its potential for carbon (C) sequestration, improvement of soil health, fertility enhancement, and crop productivity and quality. In this review, we discuss the most common available techniques for biochar production, the main physiochemical properties of biochar, and its effects on soil health, including physical, chemical, and biological parameters of soil quality and fertility, nutrient leaching, salt stress, and crop productivity and quality. In addition, the impacts of biochar addition on salt-affected and heavy metal contaminated soils were also reviewed. An ample body of literature supports the idea that soil amended with biochar has a high potential to increase crop productivity due to the concomitant improvement in soil structure, high nutrient use efficiency (NUE), aeration, porosity, and water-holding capacity (WHC), among other soil amendments. However, the increases in crop productivity in biochar-amended soils are most frequently reported in the coarse-textured and sandy soils compared with the fine-textured and fertile soils. Biochar has a significant effect on soil microbial community composition and abundance. The negative impacts that salt-affected and heavy metal polluted soils have on plant growth and yield and on components of soil quality such as soil aggregation and stability can be ameliorated by the application of biochar. Moreover, most of the positive impacts of biochar application have been observed when biochar was applied with other organic and inorganic amendments and fertilizers. Biochar addition to the soil can decrease the nitrogen (N) leaching and volatilization as well as increase NUE. However, some potential negative effects of biochar on microbial biomass and activity have been reported. There is also evidence that biochar addition can sorb and retain pesticides for long periods of time, which may result in a high weed infestation and control cost.

Hydroxycinnamic acids are the most widely distributed phenolic acids in plants. Broadly speaking, they can be defined as compounds derived from cinnamic acid. They are present at high concentrations in many food products, including fruits, vegetables, tea, cocoa, and wine. A diet rich in hydroxycinnamic acids is thought to be associated with beneficial health effects such as a reduced risk of cardiovascular disease. The impact of hydroxycinnamic acids on health depends on their intake and pharmacokinetic properties. This review discusses their chemistry, biosynthesis, natural sources, dietary intake, and pharmacokinetic properties.

Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

Traditional herbal remedies have been attracting attention as prospective alternative resources of therapy for diverse diseases across many nations. In recent decades, medicinal plants have been gaining wider acceptance due to the perception that these plants, as natural products, have fewer side effects and improved efficacy compared to their synthetic counterparts. Glycyrrhiza glabra L. (Licorice) is a small perennial herb that has been traditionally used to treat many diseases, such as respiratory disorders, hyperdipsia, epilepsy, fever, sexual debility, paralysis, stomach ulcers, rheumatism, skin diseases, hemorrhagic diseases, and jaundice. Moreover, chemical analysis of the G. glabra extracts revealed the presence of several organic acids, liquirtin, rhamnoliquirilin, liquiritigenin, prenyllicoflavone A, glucoliquiritin apioside, 1-metho-xyphaseolin, shinpterocarpin, shinflavanone, licopyranocoumarin, glisoflavone, licoarylcoumarin, glycyrrhizin, isoangustone A, semilicoisoflavone B, licoriphenone, and 1-methoxyficifolinol, kanzonol R and several volatile components. Pharmacological activities of G. glabra have been evaluated against various microorganisms and parasites, including pathogenic bacteria, viruses, and Plasmodium falciparum, and completely eradicated P. yoelii parasites. Additionally, it shows antioxidant, antifungal, anticarcinogenic, anti-inflammatory, and cytotoxic activities. The current review examined the phytochemical composition, pharmacological activities, pharmacokinetics, and toxic activities of G. glabra extracts as well as its phytoconstituents.

Nanoparticles (NPs) are new inspiring clinical targets that have emerged from persistent efforts with unique properties and diverse applications. However, the main methods currently utilized in their production are not environmentally friendly. With the aim of promoting a green approach for the synthesis of NPs, this review describes eco-friendly methods for the preparation of biogenic NPs and the known mechanisms for their biosynthesis. Natural plant extracts contain many different secondary metabolites and biomolecules, including flavonoids, alkaloids, terpenoids, phenolic compounds and enzymes. Secondary metabolites can enable the reduction of metal ions to NPs in eco-friendly one-step synthetic processes. Moreover, the green synthesis of NPs using plant extracts often obviates the need for stabilizing and capping agents and yields biologically active shape- and size-dependent products. Herein, we review the formation of metallic NPs induced by natural extracts and list the plant extracts used in the synthesis of NPs. In addition, the use of bacterial and fungal extracts in the synthesis of NPs is highlighted, and the parameters that influence the rate of particle production, size, and morphology are discussed. Finally, the importance and uniqueness of NP-based products are illustrated, and their commercial applications in various fields are briefly featured.