Menoufia University

About: Menoufia University is a education organization based out in Shibīn al Kawm, Egypt. It is known for research contribution in the topics: Population & Medicine. The organization has 6763 authors who have published 9113 publications receiving 90333 citations. The organization is also known as: Menoufia University.

Guidelines for the use and interpretation of assays for monitoring autophagy

Abstract: In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.

COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images.

Abstract: 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.

Triple-negative breast cancer: distinguishing between basal and nonbasal subtypes.

Abstract: 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.

Biological activity of bee propolis in health and disease.

Abstract: Propolis is a natural product derived from plant resins collected by honeybees. It is used by bees as glue, a general-purpose sealer, and as draught-extruder for beehives. Propolis has been used in folk medicine for centuries. It is known that propolis possesses anti-microbial, antioxidative, anti-ulcer and anti-tumor activities. Therefore, propolis has attracted much attention in recent years as a useful or potential substance used in medicine and cosmetics products. Furthermore, it is now extensively used in foods and beverages with the claim that it can maintain or improve human health. The chemical composition of propolis is quite complicated. More than 300 compounds such as polyphenols, phenolic aldehydes, sequiterpene quinines, coumarins, amino acids, steroids and inorganic compounds have been identified in propolis samples. The contents depend on the collecting location, time and plant source. Consequently, biological activities of propolis gathered from different phytogeographical areas and time periods vary greatly. In this review, the activity of bee propolis will be presented with special emphasis on the antitumor activity.