Mercapto-pyrimidines are reversible covalent inhibitors of the papain-like protease (PLpro) and inhibit SARS-CoV-2 (SCoV-2) replication.

The papain-like protease (PLpro) plays a critical role in SARS-CoV-2 (SCoV-2) pathogenesis and is essential for viral replication and for allowing the virus to evade the host immune response. Inhibitors of PLpro have great therapeutic potential, however, developing them has been challenging due to PLpro's restricted substrate binding pocket. In this report, we screened a 115 000-compound library for PLpro inhibitors and identified a new pharmacophore, based on a mercapto-pyrimidine fragment that is a reversible covalent inhibitor (RCI) of PLpro and inhibits viral replication in cells. Compound 5 had an IC50 of 5.1 µM for PLpro inhibition and hit optimization yielded a derivative with increased potency (IC50 0.85 µM, 6-fold higher). Activity based profiling of compound 5 demonstrated that it reacts with PLpro cysteines. We show here that compound 5 represents a new class of RCIs, which undergo an addition elimination reaction with cysteines in their target proteins. We further show that their reversibility is catalyzed by exogenous thiols and is dependent on the size of the incoming thiol. In contrast, traditional RCIs are all based upon the Michael addition reaction mechanism and their reversibility is base-catalyzed. We identify a new class of RCIs that introduces a more reactive warhead with a pronounced selectivity profile based on thiol ligand size. This could allow the expansion of RCI modality use towards a larger group of proteins important for human disease.

Covid-19 pandemic, sleep quality, and emotional tone of dreams: A model to look into how psychological stress affects sleep quality, dream and emotionality

The outbreak of coronavirus disease (Covid-19) has impacted the health and welfare of people globally. Given the fundamental role of sleep in health and wellbeing, it is important to study the impact of Covid-19 on sleep quality, dream content and emotionality. This has not been studied among Indian population. The present study was carried out to understand the state of sleep quality, dream contents and the relation between sleep quality and emotionality in people during the Covid-19. Based on previous studies, we expected to find differences among individuals based on how much they were affected by Covid-19, which may be because of pandemic-related stressors (like altered family dynamics, economic stability, etc.). We used Mannheim Dream questionnaire (MADRE), Pittsburgh Sleep Quality Index (PSQI), Positive Affect Negative Affect Schedule (PANAS). We used robust statistics and resampling-based estimates to quantify differences and associations for hypothesis testing. The findings indicate that during pandemic, sleep quality deteriorated with increase in negative affect and improved with increase in positive affect. Sleep quality also deteriorates with an increase in the intensity of emotionally charged dreams. Furthermore, correlation analysis revealed a negative relationship between age and negative affect (NA) suggesting that the younger the age, higher the negative affective state. Subjects who reported to be Covid-19 affected had an increased frequency of nightmares and were more distressed by nightmares, compared to those reporting not affected. This supports the notion that sleep, dreams, and affective states were disrupted during the pandemic. Subjects infected with Covid-19 had dreams of relatives or friends suffering from coronavirus and this shows the strong effect of the pandemic on the dream contents. Our study highlights the impact of psychological stress on human sleep, and emotionality. According to the study findings, we suggest that monitoring sleep, dreams, and emotions may help in developing effective interventions to restore sleep quality, prevent sleep disorders, and manage affective behaviour in pandemic like situations. © 2023, International Journal of Dream Research. All Rights Reserved.

New onset diabetes mellitus in post-COVID-19 patients.

Background: Diabetes, is known to have a bilateral relationship with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Precise mechanism of diabetes onset in COVID-19 patients remains unclear. Aim: To analyse the incidence of new onset diabetes (NODM) among COVID-19 patients, as well as the effect of body mass index (BMI), family history, and steroid use on the incidence of the disease. Methods: Adult, not known diabetic patients, tested positive with Rapid Antigen Test or RT-PCR admitted to a tertiary care hospital and research institute were included in the present prospective observational study. The patients who developed NODM and NOPD (New Onset Pre-diabetes) during the three months follow-up and the risk factors associated were assessed. Patients with HbA1c >6.4% were diagnosed with NODM. An HbA1c of 5.7% to 6.4% was used to characterize NOPD. Results: Out of 273 previously not known diabetic COVID-19 infected individuals, a total of 100 were studied for three months after consent. Mean age of the patients 48.31 ± 19.07 years with male predominance (67%). Among these, 58% were non-diabetics and 42% were pre-diabetics. 6 (10.3%) of the 58 non-diabetics developed NOPD, and 8 (13.8%) developed NODM. 6 (14.2%) of the 42 pre-diabetics became non-diabetic, and 16.6% (7) developed NODM. Family history of DM (P < 0.001), severity at admission (P < 0.006), diabetic ketoacidosis (P < 0.0275), and persistent symptoms were associated significantly with NODM. Those with NODM had significantly greater BMI, O2 duration, steroid duration, FBS, and PPBS (P < 0.001 for all). Nearly 67% of the patients who developed NOPD had shortness of breath as the common symptom at time of admission (P = 0.0165). Conclusion: The incidence of NODM was strongly influenced by positive family history of DM, higher BMI, steroid dosage, and its duration. Hence, patients with COVID-19 need to be under surveillance for blood glucose screening.

Development and Validation of RP-HPLC Method for the simultaneous determination of Nirmatrelvir and Ritonavir in bulk and pharmaceutical formulation

In this study, a rapid and sensitive stability indicating reversed phase HPLC method was developed for quantitation of Nirmatrelvir and Ritonavir simultaneously in bulk and tablet formulation. Nirmatrelvir and ritonavir were separated on a Thermo C18 column with mobile phase containing 0.01M potassium dihydrogen phosphate buffer and acetonitrile (45:55, v/v). The flow rate was 1 mL/min and detection wavelength was 272 nm. Method linearity was established over a range of 75-225 μg/mL for nirmatrelvir and 50-150 μg/mL for ritonavir. Limit of quantification was 0.694μg/mL for nirmatrelvir and 0.820μg/mL for ritonavir. The recovery (%) was 99.96 to 100.45 (Nirmatrelvir) and 100.25 to 101.35 (Ritonavir). The method precisions were 0.11% (Nirmatrelvir) and 0.33% (Ritonavir). Method was suitable to assay nirmatrelvir and ritonavir in tablet formulation (Paxlovid). Stress degradation studies have shown that this method can be implemented to assay nirmatrelvir and ritonavir in the presence of its degradants. © 2023 World Research Association. All rights reserved.

Rapid assembly of SARS-CoV-2 genomes reveals attenuation of the Omicron BA.1 variant through NSP6.

Although the SARS-CoV-2 Omicron variant (BA.1) spread rapidly across the world and effectively evaded immune responses, its viral fitness in cell and animal models was reduced. The precise nature of this attenuation remains unknown as generating replication-competent viral genomes is challenging because of the length of the viral genome (~30 kb). Here, we present a plasmid-based viral genome assembly and rescue strategy (pGLUE) that constructs complete infectious viruses or noninfectious subgenomic replicons in a single ligation reaction with >80% efficiency. Fully sequenced replicons and infectious viral stocks can be generated in 1 and 3 weeks, respectively. By testing a series of naturally occurring viruses as well as Delta-Omicron chimeric replicons, we show that Omicron nonstructural protein 6 harbors critical attenuating mutations, which dampen viral RNA replication and reduce lipid droplet consumption. Thus, pGLUE overcomes remaining barriers to broadly study SARS-CoV-2 replication and reveals deficits in nonstructural protein function underlying Omicron attenuation.

A covalent inhibitor targeting the papain-like protease from SARS-CoV-2 inhibits viral replication.

Covalent inhibitors of the papain-like protease (PLpro) from SARS-CoV-2 have great potential as antivirals, but their non-specific reactivity with thiols has limited their development. In this report, we performed an 8000 molecule electrophile screen against PLpro and identified an α-chloro amide fragment, termed compound 1, which inhibited SARS-CoV-2 replication in cells, and also had low non-specific reactivity with thiols. Compound 1 covalently reacts with the active site cysteine of PLpro, and had an IC50 of 18 µM for PLpro inhibition. Compound 1 also had low non-specific reactivity with thiols and reacted with glutathione 1-2 orders of magnitude slower than other commonly used electrophilic warheads. Finally, compound 1 had low toxicity in cells and mice and has a molecular weight of only 247 daltons and consequently has great potential for further optimization. Collectively, these results demonstrate that compound 1 is a promising lead fragment for future PLpro drug discovery campaigns.

Self-Protection of Medical Workers in Traditional Hospitals and Clinics Against SARS-CoV-2 Infection

Coronavirus Disease 2019 (COVID-19) is the most prevalent infectious human disease spreading in several parts of the world caused by SARS Coronavirus 2 (SARS-CoV-2). COVID-19 transmission is mainly spreading via the respiratory tract, personal contact, digestive tract and hospital-acquired infections. Health care workers particularly working in clinics practicing traditional medicine need to be in close contact with patients, so they have a higher risk of SARS-CoV-2 infection. In this paper, therefore, the personal-protective measures need to be followed by healthcare workers in traditional medicine clinics during COVID-19 pandemic are emphasized, to enlighten them about self-protection and to improve the safety of such a special group of traditional healers.Copyright © 2021 Bentham Science Publishers.

The emerging role of exosomes in innate immunity, diagnosis and therapy.

Exosomes, which are nano-sized transport bio-vehicles, play a pivotal role in maintaining homeostasis by exchanging genetic or metabolic information between different cells. Exosomes can also play a vital role in transferring virulent factors between the host and parasite, thereby regulating host gene expression and the immune interphase. The association of inflammation with disease development and the potential of exosomes to enhance or mitigate inflammatory pathways support the notion that exosomes have the potential to alter the course of a disease. Clinical trials exploring the role of exosomes in cancer, osteoporosis, and renal, neurological, and pulmonary disorders are currently underway. Notably, the information available on the signatory efficacy of exosomes in immune-related disorders remains elusive and sporadic. In this review, we discuss immune cell-derived exosomes and their application in immunotherapy, including those against autoimmune connective tissue diseases. Further, we have elucidated our views on the major issues in immune-related pathophysiological processes. Therefore, the information presented in this review highlights the role of exosomes as promising strategies and clinical tools for immune regulation.

Customer expectations in the hotel industry during the COVID-19 pandemic: a global perspective using sentiment analysis

Hotel industry is the one which has confronted the unprecedented effect of the coronavirus disease 2019 (COVID-19) pandemic to significant social and economic risks. The COVID-19 pandemic has challenged the tourism across the globe and impacted hospitality in hotel industry severely. This study aims to assess customer satisfaction by carrying sentiment analysis and topic modelling over customer reviews on the hospitality provided by hotels in different continents during January to September 2020, i.e. the COVID-19 pandemic. We formulate an improved new scale of metrics to categorize customer satisfaction assessed by sentiment analysis in an elaborate way. Topic modelling was deployed to understand various topics most often discussed by customers. We find that North America and Europe could perform up to customer expectation. In Asia, Sri Lanka did well, Indonesia could maintain its customer satisfaction, while India consistently improved the satisfaction level. We identified 12 most discussed topics, and main reasons of dissatisfaction appear in staff, service, room, cleanliness, slow booking, and pandemic response by hotel. Findings of this study will help senior managers of hotels of developed as well as developing countries in providing new and effective services that can satisfy customers and restore their confidence. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images.

Recently, the Covid-19 pandemic has affected several lives of people globally, and there is a need for a massive number of screening tests to diagnose the existence of coronavirus. For the medical specialist, detecting COVID-19 cases is a difficult task. There is a need for fast, cheap and accurate diagnostic tools. The chest X-ray and the computerized tomography (CT) play a significant role in the COVID-19 diagnosis. The advancement of deep learning (DL) approaches helps to introduce a COVID diagnosis system to achieve maximum detection rate with minimum time complexity. This research proposed a discrete wavelet optimized network model for COVID-19 diagnosis and feature extraction to overcome these problems. It consists of three stages pre-processing, feature extraction and classification. The raw images are filtered in the pre-processing phase to eliminate unnecessary noises and improve the image quality using the MMG hybrid filtering technique. The next phase is feature extraction, in this stage, the features are extracted, and the dimensionality of the features is diminished with the aid of a modified discrete wavelet based Mobile Net model. The third stage is the classification here, the convolutional Aquila COVID detection network model is developed to classify normal and COVID-19 positive cases from the collected images of the COVID-CT and chest X-ray dataset. Finally, the performance of the proposed model is compared with some of the existing models in terms of accuracy, specificity, sensitivity, precision, f-score, negative predictive value (NPV) and positive predictive value (PPV), respectively. The proposed model achieves the performance of 99%, 100%, 98.5%, and 99.5% for the CT dataset, and the accomplished accuracy, specificity, sensitivity, and precision values of the proposed model for the X-ray dataset are 98%, 99%, 98% and 97% respectively. In addition, the statistical and cross validation analysis is conducted to validate the effectiveness of the proposed model.

A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images

Recently, the Covid-19 pandemic has affected several lives of people globally, and there is a need for a massive number of screening tests to diagnose the existence of coronavirus. For the medical specialist, detecting COVID-19 cases is a difficult task. There is a need for fast, cheap and accurate diagnostic tools. The chest X-ray and the computerized tomography (CT) play a significant role in the COVID-19 diagnosis. The advancement of deep learning (DL) approaches helps to introduce a COVID diagnosis system to achieve maximum detection rate with minimum time complexity. This research proposed a discrete wavelet optimized network model for COVID-19 diagnosis and feature extraction to overcome these problems. It consists of three stages pre-processing, feature extraction and classification. The raw images are filtered in the pre-processing phase to eliminate unnecessary noises and improve the image quality using the MMG hybrid filtering technique. The next phase is feature extraction, in this stage, the features are extracted, and the dimensionality of the features is diminished with the aid of a modified discrete wavelet based Mobile Net model. The third stage is the classification here, the convolutional Aquila COVID detection network model is developed to classify normal and COVID-19 positive cases from the collected images of the COVID-CT and chest X-ray dataset. Finally, the performance of the proposed model is compared with some of the existing models in terms of accuracy, specificity, sensitivity, precision, f-score, negative predictive value (NPV) and positive predictive value (PPV), respectively. The proposed model achieves the performance of 99%, 100%, 98.5%, and 99.5% for the CT dataset, and the accomplished accuracy, specificity, sensitivity, and precision values of the proposed model for the X-ray dataset are 98%, 99%, 98% and 97% respectively. In addition, the statistical and cross validation analysis is conducted to validate the effectiveness of the proposed model.

An Automated Facemask Detection For Covid-19 Using Viola-Jones Algorithm

Corona Virus Disease (Covid-19) is a label species of the Corona virus family. It can cause a variety of illnesses, from the ordinary cold to advanced respiratory syndromes like Middle-East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). This virus is highly contagious and spreads due to the droplets produced by coughing and sneezing. Though there are several ways to prevent the transmission of Covid-19, one of the most important and effective way is using a face mask or a face shield. In this paper, we constructed face mask detection framework using Viola-Jones algorithm in order to recognize whether an individual is wearing a mask or not. This algorithm includes the selection of Haar features of a face, integral image creation, adaptive boost training and cascading. An extensive study is carried out in order to analyze the performance of the proposed approach;we use a large facial image dataset from the publicly available MAFA dataset. The results indicate the proposed method can accurately identify face mask wearing images with a classifier accuracy of 98.26%, suggesting it might be useful in Covid-19 prevention. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Covid and Kidney-Oea Friend or Foe

BACKGROUND: Covid-19 has been associated with worsened prognosis in patients with kidney involvement. The incidence of acute kidney injury (AKI) in Coronavirus-19 disease (COVID-19) patients ranges from 0.5% to 35%. AIM OF THE STUDY: We evaluated the prevalence severity risk factors and prognosis in patients with COVID-19 having AKI or CKD. METHOD(S): We conducted a retrospective analysis of 70 patients with Covid-19 presenting to nephrology department. Outcome of patients with CKD stage 1-2 was compared with that of patients with AKI kidney transplant and CKD stage G3a-G5D. RESULT(S): In this study, 15 (21.4%) patients had CKD stage G1-2, 18 (25.71%) had CKD stage G3a-5c and 11 (15.7%) had CKD stage G5d. Eight patients (11.4%) were with functioning renal allograft (CKD-T). Four (5.71%) developed AKI and 14 patients (20%) had acute on CKD. Overall;in-hospital mortality was 27.14% (n = 19). Of these, 3 patients (15.78%) had CKD stage G1-G2, 7 (36.84%) had CKD stage G3a-5c, 3 had CKD G5D, 2 (10.55%) had acute on CKD, one had AKI and 3 patients had a functioning kidney allograft. Baseline & nadir serum creatinine & eGFR of CKD stage 1-2, CKD Stage 3a-5c and stage CKD-t was 0.87 (eGFR 82), 7.34 (eGFR 11.61), 3.24 (eGFR-24.86);and 0.74 (eGFR 93.55), 5.37 (eGFR 16.55) and 1.85 (eGFR 42.28) respectively. CONCLUSION(S): A rather low prevalence of AKI in our Covid-19 patients, lower mortality in acute on CKD patients & improvement in eGFR in CKD & transplant patient in our study suggest that coronavirus has minimal, if any direct toxic effect on kidney. But presence of renal failure worsens the outcome of Covid-19 disease.

Gamification for Teaching Sustainability to Engineering Students

This innovative practice full paper describes sustainability teaching through game-based activities in engineering classrooms for potentially enhancing student interest in Science, Technology, Engineering and Mathematics (STEM) fields. Meta-analytic research indeed supports that gamification enhances student learning by increasing engagement and motivation, promoting goal setting behaviors and supporting the need for recognition. While there is a high scope for utilizing game-based tools to help students connect with global sustainability challenges, literature reflects a scarcity of such innovative pedagogical tools in engineering education. To tackle this challenge, we introduced interactive game-based modules to teach sustainability within two different education cultural contexts - one in the U.S. as a part of a Honours course at a large public university, and one in India, as a part of the first year engineering curriculum at a small private university.To broaden the outlook of engineering students towards sustainability, our game-based activity learning outcomes for students were to: 1) contextualize sustainability and its importance in contemporary global issues 2) recognize how Sustainable Development Goals (SDGs) could be interrelated, 3) explain how change in entropy and our actions can affect sustainability. To facilitate these broad learning outcomes, we developed two interactive gamified activities and implemented them online (due to COVID-based shift to instruction) in two different engineering institutions. This innovation article reports the design of two gamified activities used to teach sustainability, and studies its impact on student learning outcomes through SDT framework of motivation and thematic analysis of student reflections. We used surveys and minute paper to record student perceptions which were analyzed thematically. Results indicated that students enjoyed these games, saw value in peer learning, and simultaneously developed a deeper, more contextual understanding of sustainability by perceiving the interconnections between SDGs and ways in which entropy through their everyday actions influence sustainability. © 2022 IEEE.

Crowd Maps: A Crowd Density Control System to Address Contagious Pandemics

In an ordered community, a society's population is evident in public gathering spaces. Meeting people in such places is done deliberately on particular events, but in times when it is not needed or when the capacity of a place is met, it becomes difficult to mass plan a population's visit. There must be a mechanism to distribute the population over space and time. A collaborative effort to make sure that the crowd density is as low as it can be for the sake of certain problems such as traffic, parking space, pandemics, or natural disasters needs to be made by crowdsourcing information. To overcome these problems, showing the population a real-time map of the crowd density in an area over time is one way to curb crowds by voluntary action. In this paper, we present a system of two applications for this purpose. A desktop application which, with the help of CCTV cameras, counts the people in an area and projects that number onto a map, and a mobile application which, with the help of location sensors, will project each user's location alone on the same map. The crowding is evident on a map and hence tells the users the crowd density in an area. © 2022 American Institute of Physics Inc.. All rights reserved.

A secure communication using multifactor authentication and key agreement techniques in internet of medical things for COVID-19 patients

Currently, Internet of Medical Things (IoMT) gained popularity because of an ongoing pandemic. A few developed countries plan to deploy the IoMT for improving the security and safety of frontline workers to decrease the mortality rates of COVID-19 patients. However, IoMT devices share the information through an open network which leads to increased vulnerability to various attacks. Hence, electronic health management systems remain many security challenges, like recording sensitive patient data, secure communication, transferring patient information to other doctors, providing the data for future medical diagnosis, collecting data from WBAN, etc. In addition, the sensor devices attached to the human body are resource-limited and have minimal power capacity. Hence, to protect the medical privacy of patients, confidentiality and reliability of the system, the register sensor, doctor and server need to authenticate each other. Therefore, rather than two factors, in this work, a multifactor authentication protocol has been proposed to provide more secure communication. The presented scheme uses biometric and fuzzy extractors for more security purposes. Furthermore, the scheme is proved using informal and formal security verification BAN logic, ProVerif and AVISPA tools. The ProVerif simulation result of the suggested scheme shows that the proposed protocol achieves session key secrecy and mutual authentication

New onset diabetes mellitus in post-COVID-19 patients

Background: Diabetes, is known to have a bilateral relationship with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Precise mechanism of diabetes onset in COVID-19 patients remains unclear. Aim: To analyse the incidence of new onset diabetes (NODM) among COVID-19 patients, as well as the effect of body mass index (BMI), family history, and steroid use on the incidence of the disease. Methods: Adult, not known diabetic patients, tested positive with Rapid Antigen Test or RT-PCR admitted to a tertiary care hospital and research institute were included in the present prospective observational study. The patients who developed NODM and NOPD (New Onset Pre-diabetes) during the three months follow-up and the risk factors associated were assessed. Patients with HbA1c >6.4% were diagnosed with NODM. An HbA1c of 5.7% to 6.4% was used to characterize NOPD. Results: Out of 273 previously not known diabetic COVID-19 infected individuals, a total of 100 were studied for three months after consent. Mean age of the patients 48.31 ± 19.07 years with male predominance (67%). Among these, 58% were non-diabetics and 42% were pre-diabetics. 6 (10.3%) of the 58 non-diabetics developed NOPD, and 8 (13.8%) developed NODM. 6 (14.2%) of the 42 pre-diabetics became non-diabetic, and 16.6% (7) developed NODM. Family history of DM (P < 0.001), severity at admission (P < 0.006), diabetic ketoacidosis (P < 0.0275), and persistent symptoms were associated significantly with NODM. Those with NODM had significantly greater BMI, O2 duration, steroid duration, FBS, and PPBS (P < 0.001 for all). Nearly 67% of the patients who developed NOPD had shortness of breath as the common symptom at time of admission (P = 0.0165). Conclusion: The incidence of NODM was strongly influenced by positive family history of DM, higher BMI, steroid dosage, and its duration. Hence, patients with COVID-19 need to be under surveillance for blood glucose screening.

Work Stress and Quality of Work Life of The Doctors During Covid 19 Pandemic in Malappuram District, Kerala

Anxiety, depression and stress were the news norm of working environment for doctors fighting against Covid 19 virus spread. Doctors practicing in Government and Private Hospitals were found to be the major victims of this unavoidable situations. This article was constructed with twin objectives to identifying: the prime factors that caused stress for doctors during the Covid Pandemic period in Malappuram district in Kerala and to assess the doctors' satisfaction towards the factors that influenced their QWL during Covid Pandemic period in Malappuram district in Kerala. Two hundred doctors serving in Malappuram district in Kerala, in both category of hospitals group were considered as the sample. Researcher moved by the behaviour and attitude of the doctors toward their patients and treating them without caring their personal health or not bothering of getting infected the Covid effected patients. Both Government and Private hospitals ensured that doctors through Kerala have said that they had satisfactory access needed PPE kits (73.70 per cent), satisfied with the support extended to the doctor's family who were deputed in Covid wards (73 per cent), supports from the colleagues and technical staff (71 per cent) and they feel happy that the hospitals were well equipped to handle patients (70.70 per cent). The empirical data of the article declared that there exist association between nature of stress faced by the doctors during the Covid Pandemic period and their satisfaction towards their performance during this period and doctors' satisfaction towards their performance during this period with the factors that influenced their QWL during Covid Pandemic period. Copyright © 2022, Anka Publishers. All rights reserved.