Molecular Crowding Modulates SARS-CoV-2 Aptamer Affinity

SARS-CoV-2 aptamer is a favorable candidate for the recognition and detection of SARS-CoV-2, owing to its small size and easy synthesis. However, the issue of compromised binding affinities in real samples and targeting mutant SARS-CoV-2 hinder wide applications of the aptamer. In this study, it is discovered that molecular crowding could increase binding affinity of CoV2-6C3 aptamer against RBD (Receptor Binding Domain) of SARS-CoV-2 via increasing the absolute value of the enthalpy change. The values of the equilibrium dissociation constant in molecular crowding decrease by 70% and 150%, respectively, against wild-type and mutant RBD compared with those in buffer without crowding. Moreover, the detection limit of SARS-CoV-2 pseudovirus is up to 5 times lower under molecular crowding compared to dilute conditions. The discovery deepens the understanding of aptamer-target interaction mechanisms in crowding conditions and provides an effective way to apply SARS-CoV-2 aptamer for virus recognition and detection.

The repelling factors of the management accounting methods under the COVID-19-case studies based on the scenario of private enterprises

The new crown epidemic has brought various impacts to companies such as shrinking front-end consumption, and restricted personnel mobility. It also brings severe challenges to company's normal production and operation. Since the outbreak of the epidemic, all private enterprises have overcome numerous difficulties and made every effort to promote the resumption of work and production in an orderly manner while organising member units to prevent and fight the epidemic. This article first briefly introduces the measures and effects of private enterprises in response to the epidemic. After the resumption of work and production, in order to reduce the probability of infection on the way, more families choose to travel in private cars, and passenger traffic is expected to rebound sharply.

Clinical Considerations in Osteoporosis Management During the COVID-19 Pandemic

Coronavirus disease of 2019(COVID-19) is a highly contagious viral disease, causing reparatory symptoms, ranging from flu-like symptoms to acute respiratory distress. Since the end of 2019, COVID-19 has posed a tremendous threat to the healthcare systems nationwide. Multiple public health interventions, including mandating social distancing, closing outpatient visits, or postponing elective procedures have been implemented to mitigate the impact on disease transmission and prevent consumption of medical resources. Since the beginning of the pandemic, resources have been shifted away from chronic disease management and prevention. Osteoporosis, a chronic condition, which requires continuous and concerted medical attention to alleviate the long-term consequences such as osteoporotic fractures, morbidities, or mortalities. In this review article, we will discuss the strategies to cope with osteoporosis, especially focusing on pharmaceutical management considerations during the era of COVID-19 pandemic. We will also discuss different drug distribution models when outpatient clinics are not readily available or mandatory social distancing policy is employed. After all, we will propose alternative therapeutic options when the continuity of particular medications cannot be maintained. © 2021 Society of Internal Medicine of Taiwan. All rights reserved.

DEEP LEARNING BASED REAL-TIME FACIAL MASK DETECTION AND CROWD MONITORING

During the Covid pandemic, the importance of wearing mask has been noted globally. Additionally, crowded human clusters facilitated the transmission of the virus, which brings up the need for new systems for monitoring such situations. To address such issues, this research proposes an object recognition visual system based on deep learning to monitor the mask-wearing in a certain space and the control of the number of people indoors as an important tool during an epidemic. This research mainly investigates two types of identification. The first is to monitor whether people entering the site wear a mask at the entrance and exit of the field, and the second is to count the number of people entering a specific area. Experimental results show that by utilising the visual sensor, it is possible to detect and identify the people who frequently enter and exit in real-time. An advanced transfer learning approach has been employed to achieve the best discrimination performance. The actual training results prove that the migration learning Mask R-CNN algorithm produced by this method and the original Mask R-CNN algorithm have increased the mAP by 3 %, reaching a mAP of 96 %. In addition, the accuracy of the random sampling and identification in actual scenes has reached 92.1 %. The developed deep learning vision system has an enhanced identification ability for the verification and analysis of actual scenes and has a great application potential.

Anomaly detection with vision-based deep learning for epidemic prevention and control

During the COVID-19 pandemic, people were advised to keep a social distance from others. People's behaviors will also be noticed, such as lying down because of illness, regarded as abnormal conditions. This paper proposes a visual anomaly analysis system based on deep learning to identify individuals with various anomaly types. In the study, two types of anomaly detections are concerned. The first is monitoring the anomaly in the case of falling in an open public area. The second is measuring the social distance of people in the area to warn the individuals under a short distance. By implementing a deep model named You Only Look Once, the related anomaly can be identified accurately in a wide range of open spaces. Experimental results show that the detection accuracy of the proposed method is 91%. In the social distance, the actual social distance is calculated by calculating the plane distance to ensure that everyone can meet the specification. Integrating the two functions and implementing the environmental monitoring system will make it easier to monitor and manage the disease-related abnormalities on the site. © 2022 The Author(s) 2022.

Using artificial intelligence to predict class loyalty and plagiarism in students in an online blended programming course during the covid-19 pandemic

During the COVID-19 epidemic, most programming courses were revised to distance learning. However, many problems occurred, such as students pretending to be actively learning while actually being absent and students engaging in plagiarism. In most existing systems, obtaining status updates on the progress of a student’s learning is hard. In this paper, we first define the term “class loyalty”, which means that a student studies hard and is willing to learn without using any tricks. Then, we propose a novel method combined with the parsing trees of program codes and the fuzzy membership function to detect plagiarism. Additionally, the fuzzy membership functions combined with a convolution neural network (CNN) are used to predict which students obtain high scores and high class loyalty. Two hundred and twenty-six students were involved in the experiments. The dataset was randomly separated into the training datasets and the test datasets for twenty runs. The average accuracies of the experiment in predicting which students obtain high scores using the fuzzy membership function combined with a CNN and using the duration and number of actions are 93.34% and 92.62%. The average accuracies of the experiment in predicting which students have high class loyalty are 95.00% and 92.74%. Both experiments show that our proposed method not only can detect plagiarism but also can be used to detect which students are diligent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Online Teaching Self-Efficacy, Social-Emotional Learning (SEL) Competencies, and Compassion Fatigue Among Educators During the COVID-19 Pandemic

Guided by the social-cognitive theory and job demands-resources model, this study examined how educators perceived online teaching self-efficacy and social and emotional learning (SEL) competencies concurrently and interactively influenced educators' compassion fatigue during distance learning in the COVID-19 pandemic among 321 educators in California. Survey results suggested that educators with longer years of working in education and White educators reported higher levels of compassion fatigue than their counterparts. Controlling for educators' demographic factors, online teaching self-efficacy had a negative and significant association with compassion fatigue. Moreover, the negative association between online teaching self-efficacy and compassion fatigue was intensified among educators with a higher level of SEL competencies. The findings highlight the importance of promoting educators' online teaching self-efficacy in preventing educator compassion fatigue. It also indicates that educators with higher SEL competencies were more attuned to the negative association between online teaching self-efficacy and compassion fatigue than educators with lower SEL competencies. IMPACT STATEMENT This is one of the first empirical studies examining educators' experiences with online teaching self-efficacy, compassion fatigue, and SEL competencies during the COVID-19 pandemic and distance education. It highlights the importance of promoting educators' online teaching self-efficacy and monitoring their SEL competencies in preventing compassion fatigue among educators. It also indicates that educators with a higher level of SEL competencies were more attuned to the negative influence of online teaching self-efficacy on compassion fatigue than educators with a lower SEL competency level.

Ethical considerations and patient safety concerns for cancelling non-urgent surgeries during the COVID-19 pandemic: a review

At the time of writing of this article, there have been over 110 million cases and 2.4 million deaths worldwide since the start of the Coronavirus Disease 2019 (COVID-19) pandemic, postponing millions of non-urgent surgeries. Existing literature explores the complexities of rationing medical care. However, implications of non-urgent surgery postponement during the COVID-19 pandemic have not yet been analyzed within the context of the four pillars of medical ethics. The objective of this review is to discuss the ethics of elective surgery cancellation during the COVID-19 pandemic in relation to beneficence, non-maleficence, justice, and autonomy. This review hypothesizes that a more equitable decision-making algorithm can be formulated by analyzing the ethical dilemmas of elective surgical care during the pandemic through the lens of these four pillars. This paper's analysis shows that non-urgent surgeries treat conditions that can become urgent if left untreated. Postponement of these surgeries can cause cumulative harm downstream. An improved algorithm can address these issues of beneficence by weighing local pandemic stressors within predictive algorithms to appropriately increase surgeries. Additionally, the potential harms of performing non-urgent surgeries extend beyond the patient. Non-maleficence is maintained through using enhanced screening protocols and modifying surgical techniques to reduce risks to patients and clinicians. This model proposes a system to transfer patients from areas of high to low burden, addressing the challenge of justice by considering facility burden rather than value judgments concerning the nature of a particular surgery, such as cosmetic surgeries. Autonomy can be respected by giving patients the option to cancel or postpone non-urgent surgeries. However, in the context of limited resources in a global pandemic, autonomy is not absolute. Non-urgent surgeries can ethically be postponed in opposition to the patient's preference. The proposed algorithm attempts to uphold the four principles of medical ethics in rationing non-urgent surgical care by building upon existing decision models, using additional measures of resource burden and surgical safety to increase health care access and decrease long-term harm as much as possible. The next global health crisis will undoubtedly present its own unique challenges. This model may serve as a comprehensive starting point in determining future guidelines for non-urgent surgical care.