ABSTRACT
Background. After COVID-19 emergence, medical education witnessed a shift from face-to-face education to digital education, which inevitably affected medical students. Globally, due to the closure of schools and universities, medical education was shifted to electronic learning (E-learning). This paper aimed to assess the effects of the COVID-19 pandemic on medical education and determine medical students' knowledge, attitude, and practices towards E-learning in the Kurdistan Region of Iraq.Materials and Methods. An online cross-sectional study was conducted among 500 undergraduate students of seven medical colleges in the Kurdistan Region, Iraq, in November 2021, to assess their state during the COVID-19 pandemic and how this affected their education.Results. There were 50.6% of males and 49.4% of females. The mean age was 20.6 ( +/- 1.5 SD) years. Approximately 17% of participants mentioned having financial issues, while 19.2% of students experienced health-related problems. As many as 67% of participants reported that the Internet quality was good or very good, whereas 46.8% of students disagreed that E-learning was a possible substitute for traditional learning. About two-thirds of participants agreed or were neutral that downloadable content was better than live content;however, only 19.2% of students agreed that E-learning could be used in the clinical aspect. A total of 52.2% of participants disagreed that E-testing could replace traditional learning methods. Surprisingly, 86.4% of students stated that they regularly used the Internet in their study.Conclusions. E-learning was the main adjustment made in the educational system, including medical education. The study concluded with insights into how different circumstances could have different conse-quences on the efficacy of medical education. E-learning showed effective results in continuing learning until the educational system switched to a blended system. Training programs for medical education personnel are vital in effective E-learning opportunities.
ABSTRACT
Genetic variations are critical for understanding clinical outcomes of infections including server acute respiratory syndrome coronavirus 2 (SARS CoV-2). The immunological reactions of human immune genes with SARS CoV-2 have been under investigation. Toll-like receptors (TLRs), a group of proteins, are important for microbial detections including bacteria and viruses. TLR4 can sense both bacterial lipopolysaccharides (LPS) and endogenous oxidized phospholipids triggered by Covid-19 infection. Two TLR4 single nucleotide polymorphisms (SNPs), Asp299Gly and Thr399Ile have been linked to infectious diseases. No studies have focused on these SNPs in association with Covid-19. This study aims to reveal the association between Covid-19 infection with these SNPs by comparing a group of patients and a general population. Restriction fragment length polymorphisms (RFLP) were used to identify the TLR4 SNPs in both the general population (n = 114) and Covid-19 patient groups (n = 125). The results found no association between the TLR4 polymorphisms and Covid-19 infections as the data showed no statistically significant difference between the compared groups. This suggested that these TLR4 SNPs may not be associated with Covid-19 infections.
ABSTRACT
New SARS-CoV-2 infections are difficult to be verified, whether they are reinfections or persistent infections. The most prominent factors used for differentiating reinfections from persistent infections are whole-genome sequencing and phylogenetic analyses that require time and funds, which may not be feasible in most developing countries. This study explores reinfections with COVID-19 that harbors D614G and N501Y mutations by rapid inexpensive methods. It exploits the previously developed rapid economic methods that identified both D614G and N501Y mutations in clinical samples using real-time reverse transcriptase polymerase chain reaction (rRT-PCR) probes and conventional PCR specific primers. In the present study, an immunocompetent patient has been found with a SARS-CoV-2 N501Y reinfection without comorbidities. According to the obtained results, this study suggests that the initial infection was due to a variant that contained only D614G mutation whereas the reinfection was potentially a result of alpha variant contained three mutations confirmed by DNA sequencing, including D614G, N501Y, and A570D mutations. These techniques will support rapid detection of SARS-CoV-2 reinfections through the identification of common spike mutations in the developing countries where sequencing tools are unavailable. Furthermore, seven cases of reinfections were also confirmed by these methods. These rapid methods can also be applied to large samples of reinfections that may increase our understanding epidemiology of the pandemic.
ABSTRACT
COVID-19 has altered the way businesses throughout the world perceive cyber security. It resulted in a series of unique cyber-crime-related conditions that impacted society and business. Distributed Denial of Service (DDoS) has dramatically increased in recent year. Automated detection of this type of attack is essential to protect business assets. In this research, we demonstrate the use of different deep learning algorithms to accurately detect DDoS attacks. We show the effectiveness of Long Short-Term Memory (LSTM) algorithms to detect DDoS attacks in computer networks with high accuracy. The LSTM algorithms have been trained and tested on the widely used NSL-KDD dataset. We empirically demonstrate our proposed model achieving high accuracy (~97.37%). We also show the effectiveness of our model in detecting 22 different types of attacks. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.