Options of Treatment in SARS-CoV-2 Infections

Starting December 2019, in China, the SARS-CoV-2 infection became a local health issue, but its high infectiveness quickly turned it into a pandemic, to be declared an epidemiological emergency of international concern less than two months from identifying the first cases. Despite tremendous efforts aimed at discovering new drug classes that would slow down or stop the evolution of the COVID-19 disease and curb the number of severe cases and related complications, there is no specific antiviral treatment to date. The only efficient measure proved to be the prophylaxis, through vaccination. © 2021 Alexandra Maria Cristea et al., published by Sciendo.

Clinical particularities regarding rehabilitation treatment of a young patient with right pontine ischemic stroke, resulting in dysarthria and left side brachial and crural hemiparesis, and quite recent COVID-19 history

Introduction. Cerebrovascular attacks are one of the leading causes of mortality and morbidity in the world. The most severe are considered to be pontine strokes, because of the high risk of vital functions impairment. The clinical presentation of a pontine stroke can vary from the classical crossed syndrome (ipsilateral cranial nerve palsy and contralateral motor and/or sensory impairment) to pure motor hemiplegia or pure sensory stroke, which are less common.(1) Materials and methods. This presentation describes the case of a 39-year-old patient with a recent history of untreated SARS-COV 2 infection that was followed in the next month by a sudden onset of facial paralysis, dysarthria and a progressive left hemiplegia. The patient's functional status was assessed in our Neuromuscular Clinical Division and he underwent a rehabilitation program consisting of physical, occupational and speech therapies. Multidisciplinary efforts were made in order to find the underlying cause of the pontine ischemic stroke. Results. The patient had a personal history of cardiovascular disease risk factors (essential hypertension and hyperlipidaemia), without other pathological brain imagining outcomes and normal bleeding tests. The suspicion of an interatrial communication was raised. The patient managed to rapidly maintain the standing posture and to practice walking with unilateral support. At discharge, the facial paralysis and dysarthria were almost completely remitted. The left brachial extremity also showed signs of improvement, as the patient was able to perform flexion and extension movements of the fingers, wrist and forearm. Conclusion. To conclude, the patient's evolution was favourable, although the certain cause of the underlying stroke has not been clarified. The neurological complications of COVID-19 include ischemic strokes, and cases were reported in young adults too(2,3). If this were the case, prevention of further cerebrovascular attacks and their complications is necessary(1). Caution in terms of medium/long-term prophylactic anticoagulant therapy and careful control of associated cardiovascular disease risk factors has been proposed and discussed in the multidisciplinary team: rehabilitation physician, neurologist, cardiologist.

Detecting Fine-Grained Emotions on Social Media during Major Disease Outbreaks: Health and Well-being before and during the COVID-19 Pandemic

The COVID-19 pandemic has affected the whole world in various ways. One type of impact is that communication, work, interaction, a great part of our lives has moved online on various platforms, with some of the most popular being the social media ones. Another, arguably less visible impact, is the emotional impact. Detecting and understanding emotions is important, to better discern the emotional health and well-being of the global population. Thus, in this work, we use a social media platform (Twitter) to analyse emotions in detail. Our contribution is twofold: (1) we propose EmoBERT, a new emotion-based variant of the BERT transformer model, able to learn emotion representations and outperform the state-of-the-art;(2) we provide a fine-grained analysis of the pandemic's effect in a major location, London, comparing specific emotions (annoyed, anxious, empathetic, sad) before and during the epidemic.

Encouraging Teacher-Sourcing of Social Recommendations Through Participatory Gamification Design

Teachers and learners who search for learning materials in open educational resources (OER) repositories greatly benefit from feedback and reviews left by peers who have activated these resources in their class. Such feedback can also fuel social-based ranking algorithms and recommendation systems. However, while educational users appreciate the recommendations made by other teachers, they are not highly motivated to provide such feedback by themselves. This situation is common in many consumer applications that rely on users’ opinions for personalisation. A possible solution that was successfully applied in several other domains to incentivise active participation is gamification. This paper describes for the first time the application of a comprehensive cutting-edge gamification taxonomy, in a user-centred participatory-design process of an OER system for Physics, PeTeL, used throughout Israel. Physics teachers were first involved in designing gamification features based on their preferences, helping shape the gamification mechanisms likely to enhance their motivation to provide reviews. The results informed directly the implementation of two gamification elements that were implemented in the learning environment, with a second experiment evaluating their actual effect on teachers’ behaviour. After a long-term, real-life pilot of two months, teachers’ response rate was measured and compared to the prior state. The results showed a statistically significant effect, with a 4X increase in the total amount of recommendations per month, even when taking into account the ‘Covid-pandemic effect’. © 2021, Springer Nature Switzerland AG.

Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. These platforms also bring incredible diversity of learners in terms of their traits. A research area called Author Profiling (AP in general;here, Learner Profiling (LP)), is to identify such traits about learners, which is vital in MOOCs for, e.g., preventing plagiarism, or eligibility for course certification. Identifying a learner’s trait in a MOOC is notoriously hard to do from textual content alone. We argue that to predict a learner’s academic level, we need to also be using other features stemming from MOOC platforms, such as derived from learners’ actions on the platform. In this study, we specifically examine time stamps, quizzes, and discussions. Our novel approach for the task achieves a high accuracy (90% in average) even with a simple shallow classifier, irrespective of data size, outperforming the state of the art. © 2021, Springer Nature Switzerland AG.

Management of Atypical TB during the COVID-19 Pandemic

Mycobacterium kansasii is a nontuberculous mycobacterium which leads to a chronic pulmonary infection that resembles pulmonary tuberculosis. Symptoms of pulmonary infection with M kansasii may include cough, sputum production, weight loss, breathlessness, chest pain, hemoptysis and fever or sweats.The paper presents the case of a 30-year-old woman, confirmed with moderate SARS-COV2 infection at a county hospital and transferred to "Marius Nasta" Institute for Pneumology for specialty treatment. At admission, the CT exam reveals multiple "tree in bud" lesions and cavity lesions located at the RUL level. Bacteriological examinations for the genus mycobacterium were initially negative, but from the bronchial aspirate, the bacteriological examination was BAAR positive. Genetic testing confirmed mycobacterium infection. Resistance to H, R was not detected. According to the national protocol, antibiotic treatment is initiated with Isoniazid, Rifampicin, Ethambutol and Pyrazinamide. The patient tolerated the treatment well throughout the hospitalization period, as there were no interactions between the treatment for SARS-COV2 and the antibiotic one. Due to the favorable evolution, the patient is discharged one month after starting treatment. As recommendations, the patient must present herself at the territorial TB clinic, in order to continue the antibiotic treatment for up to 6 months.At two months, the culture shows infection with mycobacterium kansassi, thus the treatment is modified by administering Rifampicin, Etambutol and Azithromycin. Following the administration of the new treatment, the lung lesions show regression. In the case of mycobacterium kansassi infections, the treatment will be continued for up to 12 months. The evolution of M. kansassi infection may be influenced by various factors, but in this case the moderate SARS-COV2 infection, cured in 3 weeks, did not influence the favourable evolution. Also, the absence of shortness of breath, as well as the compliance and absence of treatment resistance were keen factors in the evolution under treatment.In conclusion, the patient was initially admitted to the hospital for a moderate COVID-19 infection. The routine CT exam was also suggestive for a possible other pathology, and follow-up tests and examinations led to the discovery of an infection with M. kansassi. The SARS-COV-2 infection had favourable evolution under treatment and was cured in 3 weeks, but the treatment for M. kansassi is long-term and may have effects on the patients' psychology, as well as by the presence of adverse reactions, not following the treatment can be fatal with death occurring in 50% of cases.

COVID-19’s Impact on the Telecommunications Companies

Now the world is witnessing most significant challenges due the Covid-19 crisis. Beyond health effects, it has social and economic effects. With the enormous amount of data available and the widespread use of social web globally, research can and should use it to provide solutions. Customer satisfaction is known to affect customer churn (customers leaving companies), which is a problem affecting many companies in competitive and volatile markets – like the current one. One easily available open source of customer opinions are tweets – more relevant now in the online world. Whilst Natural Language Processing (NLP) on tweets is not new, few studies target customer satisfaction, and NLP body of research on Arabic tweets is modest;we are not aware of any other study on this during a global pandemic. Our research thus aims to propose a new model based on Twitter mining to measure customer satisfaction during Covid-19, as well as compare customer satisfaction before and during the crisis. This is a use case for the largest Telecom companies in Saudi Arabia, and we involve the popular method of Sentiment Analysis (SA) for the task. We additionally propose a new Saudi lexicon and apply it to monitor real-time customer satisfaction on Twitter using three different transfer network models on Arabic sentiment analysis. Also, this research evaluates using these models on Arabic Sentiment Analysis as the first study comparing between three different transfer network models on Arabic text. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.