To see if HHV-6 may be a cause of infertility, researchers looked at 18 men and 10 women who had unexplained critical fertility and had at least one prior pregnancy. HHV-6 DNA was discovered in both infertile and fertile peripheral blood mononuclear cells (PBMC) (12 and 14%, respectively); endometrial epithelial cells from 4/10 (40%) infertile women were positive for HHV-6 DNA; this viral DNA was not found in the endometrium of fertile women. When endometrial epithelial cells were cultivated, they produced viral early and late proteins, suggesting the existence of an infectious virus. Endometrial HHV-6 infection creates an aberrant NK cell and cytokine profile, resulting in a uterine domain that is not favorable to conception, according to the findings. To corroborate the findings, studies of extra fertile and barren women should be done. Semen samples were taken from 18 guys who visited the Government General Hospital Guntur’s infertility department because they were having reproductive issues with their partners. Herpes virus DNA has been discovered in the sperm of symptomatic fertile and infertile male patients on rare instances. Furthermore, researchers must investigate the role of viral diseases in male infertility.

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Education in multiple forms and diverse geographical contexts delivers quality in all aspects of learning in which stakeholders such as students, instructors, and educational institutions play an important role. Quality assurance in higher education ensures the smooth functioning of the teaching and learning process by supporting the attainment of the desired quality levels of learning outcomes. This further leads to educational sustainability, as education has been acknowledged as a strategic constituent of sustainability-focused strategies in many educational contexts. Hence, it has become very important for educational institutions to maintain quality standards through the implementation of appropriate strategies, as quality is the lifeline of both Traditional Learning and E-Learning, and a lack of a suitable assessment standard affects the quality of learning. This research study attempts to address the existing gaps observed following a review of the related literature. This study collected qualitative data using an observation method through the observations and review of online software used at the Saudi Electronic University, namely Blackboard Learning Management System (LMS), Tawkeed Quality Management E-System, and Blue Survey software. In addition to this, the expertise of the research team members was also utilized for this research study in designing E-Learning quality dimensions. The purpose of this study was to propose an E-Learning Quality Assessment Standard that will help third-level educational institutions to assess their current teaching and learning practices of E-Learning and support them in enhancing the overall students’ experiences toward E-Learning within their institutions. As a research outcome, a conceptual quality assessment standard titled “SPECIFIERS” was proposed to offer a helping hand during the E-Learning quality assessment process to ensure sustainable education development of global educational institutions.

This work aims to build a binary breast cancer classifier algorithm based on the blood test and anthropometric data (age, body mass index, glucose, insulin, homeostasis model assessment, leptin, adiponectin, resistin, and monocyte chemotactic protein-1) of 116 subjects. For this study, a performance comparison of the following machine learning models was performed: decision tree, random forest, K-nearest neighbors, artificial neural networks, vector machines of support, and logistical regression. The methodologies used in the data were as follows: k‐fold cross‐validation (k = 10); splitting data into 80% training and 20% testing. For the first, the mean of accuracy and sensitivity were evaluated in the second, values of accuracy, sensitivity, specificity, and area under some tests. In addition, most mammograms are performed on benign tumors. With this, it is clear that these exams can use other tools to assist in decision-making, and machine learning can offer great utility and good cost/benefit in the diagnostic process of breast cancer. Many research papers for breast cancer biomarkers have been reported over the years. The present work will analyze the potential quantitative variables: age, receiver operating characteristic curve. Furthermore, the p value, Pearson correlation coefficient, and, depending on the input variable, the test only with variables with a significance threshold of 5% are computed from the normal distribution assessment (calculated from Kolmogorov–Smirnov test (KS test)) which were as follows: glucose, insulin, resistin, and homeostasis assessment model. As the best final classifier, the random forest was used in the training/test method and with nine variables, with 83.3% accuracy, 100% sensitivity, 64% specificity, and 0.881 of area under the curve.

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