Perspectives toward e-learning system during Covid-19 pandemic among medical Colleagues, Egypt: A Cross-sectional survey (preprint)

Background: WHO declared the outbreak of COVID-19, which affected the educational system stopping it in Egypt. To maintain the educational process, E-learning was a suggested solution. This study aimed to assess students' satisfaction regarding e-learning experience and effectiveness of this system on medical education in Egyptian universities. Methods: A cross-sectional survey was conducted among medical schools in Egypt during 2020–2021. It was conducted through an online questionnaire composed of four sections: Demographic characteristics, quality of internet connection, the academic characteristics of the participants, and evaluation of the effectiveness of e-learning. We compared the effectiveness of e learning among the clinical and the academic students, and private and governmental universities. Results: Among 90592 medical students in Egypt, 6393 students from 28 universities filled out the questionnaire with a 96.6% response rate. The age of participants ranged from 16 to 29 years old. Also, about 59.3% of them were females. The blended e-learning was the most applied by universities (43.1%), followed by partial type (33.4%), and full type (23.6%). About 73.7% of students had "always/often" constant internet connection. Forty percent of students were "dissatisfied/not satisfied at all", 38.3% showed "neutral" satisfaction, while 21.7% were "satisfied or highly satisfied". There was a significant difference regarding internet connection and availability favoring urban areas compared to rural areas (p<0.001). There was a significant difference favoring the academic education over the clinical education regarding the effectiveness of online learning (p<0.01) and private education over governmental regarding the effectiveness of online learning (p<0.001). Conclusion: E-learning was better for academic education than for clinical education. It was better for private universities than governmental ones. Also, students in rural areas had worse availability and quality internet connection compared with those in urban areas.

Menstrual changes after COVID-19 vaccination and/or SARS-CoV-2 infection and their demographic, mood, and lifestyle determinants in Arab women of childbearing age, 2021.

Background: By September 2, 2021, over 30,000 COVID-19-vaccinated females had reported menstrual changes to the MHRA's Yellow Card surveillance system. As a result, the National Institutes of Health (NIH) is urging researchers to investigate the COVID-19 vaccine's effects on menstruation. Therefore, this study was conducted to explore the menstrual changes after COVID-19 vaccination and/or SARS-CoV-2 infection and their interrelations with demographic, mood, and lifestyle factors in Arab women of childbearing age (CBA). Methodology: A cross-sectional study was conducted during October 2021 using an Arabic validated and self-administrated questionnaire. In total, 1,254 Women of CBA in the Arabic Population (15-50 y) with regular menstrual cycles were randomly selected from five countries (Saudi Arabia, Egypt, Syria, Libya, and Sudan). Results: The mean (SD) age of the 1,254 studied females was 29.6 (8.5) years old. In total, 634 (50%) were married, 1,104 (88.0%) had a University education or above, 1,064 (84.4%) lived in urban areas, and 573 (45.7%) had normal body weight. Moreover, 524 (41.8%) were COVID-19 cases and 98 women (18.7%) reported menstrual changes (MCs). The 1,044 (83.5%) vaccinated females reported 418 (38.5%) MCs after being vaccinated, and these MCs resolved in 194 women (55.1%) after more than 9 months. Statistically significant relationships were observed between the reported MCs and the following variables: age, marital status, level of education, nationality, residence, and BMI. MCs were reported at 293(80.6) after the 2nd dose, and were mainly reported after 482 (46.1) Pfizer, 254 (24.3) Astrazenica, and 92 (8.8) Senopharm. Conclusion: MCs among women of CBA after COVID-19 infection and vaccination are prevalent and complex problems, and had many determinates.

A fractional-order mathematical model for analyzing the pandemic trend of COVID-19.

Many countries worldwide have been affected by the outbreak of the novel coronavirus (COVID-19) that was first reported in China. To understand and forecast the transmission dynamics of this disease, fractional-order derivative-based modeling can be beneficial. We propose in this paper a fractional-order mathematical model to examine the COVID-19 disease outbreak. This model outlines the multiple mechanisms of transmission within the dynamics of infection. The basic reproduction number and the equilibrium points are calculated from the model to assess the transmissibility of the COVID-19. Sensitivity analysis is discussed to explain the significance of the epidemic parameters. The existence and uniqueness of the solution to the proposed model have been proven using the fixed-point theorem and by helping the Arzela-Ascoli theorem. Using the predictor-corrector algorithm, we approximated the solution of the proposed model. The results obtained are represented by using figures that illustrate the behavior of the predicted model classes. Finally, the study of the stability of the numerical method is carried out using some results and primary lemmas.

CRISPR/Cas9-modified Human T Cell ( PD-1and ACE2 Knockout Engineered T Cells ) for Inducing Long-term Immunity in COVID-19 Patients

COVID-19 detection using federated machine learning.

The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient's private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model)using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID-19 patients. During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models' loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.

Synthesis of potentially new schiff bases of N-substituted-2-quinolonylacetohydrazides as anti-COVID-19 agents.

We report herein a new series of synthesized N-substituted-2-quinolonylacetohydrazides aiming to evaluate their activity towards SARS-CoV-2. The structures of the obtained products were fully confirmed by NMR, mass, IR spectra and elemental analysis as well. Molecular docking calculations showed that most of the tested compounds possessed good binding affinity to the SARS-CoV-2 main protease (Mpro) comparable toRemdesivir.