Search

Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated. [ABSTRACT FROM AUTHOR]

Simple Summary: As COVID-19 shifts from pandemic to endemic, emerging variants may be more or less virulent. Predicting whether an emerging COVID-19 variant has of high risk of causing severe disease is needed to plan for potential burdens on hospital capacity and protecting vulnerable populations. However, it takes time to do laboratory and animal experiments to determine whether a new genetic variant might be more severe, and the results may not be representative of when the virus infects humans. By the time there is epidemiological data on the severity of disease associated with a new variant, it can be too late for designing an optimal public health response. There is a critical need for computer models that can predict severe disease risk from genetic sequence data, which can be obtained from just the first few infections in a potential incoming wave. Two key challenges make computer modeling difficult: (1) sequence changes are complex, and (2) using historical data to predict future disease requires accounting for the confounding effects of changing patient demographics, improving therapeutics, and increased vaccination. In this paper, we introduce a novel interpretable deep learning architecture to solve this problem, demonstrating that it can make robust predictions for emerging variants. Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data. [ABSTRACT FROM AUTHOR]

During the last three years, numerous research papers have been reported which use social media data to explore several issues related to the COVID-19 pandemic. Bibliometric methods in this work are used to analyze 1427 peer-reviewed documents from the last three years extracted from the Web of Science database. The results of this study show that there was high growth in publications in open access journals with an annual rate reaching 19.3% and they also identify the top cited journals and research papers. The thematic analysis of papers shows that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, mental health, misinformation, and fake news are important and well-developed, whereas topics related to distance-learning education with social media are emerging. The results also show that the USA, China, and the UK have published many papers and received a high number of citations because of their strong international collaboration. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Kōfuku no Kagaku is a most visible new religious actor in contemporary Japan thanks to the vast publishing activities of its founder and its widespread professional use of new media. This paper examines how Kōfuku no Kagaku engages with the COVID-19 pandemic in practical and doctrinal terms. Notably, early in the pandemic, Kōfuku no Kagaku's international promotion of spiritual cures and vaccines even prompted The New York Times to feature the group in a widely circulated article in April 2020. This paper outlines these "spiritual technologies" and examines their doctrinal rationale as well as the wider doctrinal appropriation of the COVID-19 pandemic. It will be shown how the latter is instrumentalised to echo Kōfuku no Kagaku's millenarian agenda. [ABSTRACT FROM AUTHOR]

The sequencing of SARS-CoV-2 provides essential information on viral evolution, transmission, and epidemiology. In this paper, we performed the whole-genome sequencing of SARS-CoV-2 using nanopore and Illumina sequencing to describe the circulation of the virus lineages in Armenia. The analysis of 145 full genomes identified six clades (19A, 20A, 20B, 20I, 21J, and 21K) and considerable intra-clade PANGO lineage diversity. Phylodynamic and transmission analysis allowed to attribute specific clades as well as infer their importation routes. Thus, the first two waves of positive case increase were caused by the 20B clade, the third peak caused by the 20I (Alpha), while the last two peaks were caused by the 21J (Delta) and 21K (Omicron) variants. The functional analyses of mutations in sequences largely affected epitopes associated with protective HLA loci and did not cause the loss of the signal in PCR tests targeting ORF1ab and N genes as confirmed by RT-PCR. We also compared the performance of nanopore and Illumina short-read sequencing and showed the utility of nanopore sequencing as an efficient and affordable alternative for large-scale molecular epidemiology research. Thus, our paper describes new data on the genomic diversity of SARS-CoV-2 variants in Armenia in the global context of the virus molecular genomic surveillance. [ABSTRACT FROM AUTHOR]

Background: COVID-19-associated mucormycosis (CAM) is associated with high morbidity and mortality. MUNCO is an international database used to collect clinical data on cases of CAM in real time. Preliminary data from the Mycotic Infections in COVID-19 (MUNCO) online registry yielded 728 cases from May to September 2021 in four South Asian countries and the United States. A majority of the cases (694; 97.6%) consisted of a mucormycosis infection. The dataset allowed for the analysis of the risk factors for adverse outcomes from CAM and this analysis is presented in this paper. Methods: The submission of cases was aided by a direct solicitation and social media online. The primary endpoints were full recovery or death measured on day 42 of the diagnosis. All patients had histopathologically confirmed CAM. The groups were compared to determine the contribution of each patient characteristic to the outcome. Multivariable logistic regression models were used to model the probability of death after a CAM diagnosis. Results: The registry captured 694 cases of CAM. Within this, 341 could be analyzed as the study excluded patients with an unknown CAM recovery status due to either an interruption or a lack of follow up. The 341 viable cases consisted of 258 patients who survived after the completion of treatment and 83 patients who died during the period of observation. In a multivariable logistic regression model, the factors associated with an increased risk of mortality include old age (OR = 1.04, 95% CI 1.02–1.07, p = 0.001), history of diabetes mellitus (OR 3.5, 95% CI 1.01–11.9, p = 0.02) and a lower BMI (OR 0.9, 95% CI 0.82–0.98, p = 0.03). Mucor localized to sinus disease was associated with 77% reduced odds of death (OR = 0.23, 95% CI 0.09–0.57, p = 0.001), while cerebral mucor was associated with an increased odds of death (OR = 10.96, 95% CI 4.93–24.36, p = ≤0.0001). Conclusion: In patients with CAM, older age, a history of diabetes and a lower body mass index is associated with increased mortality. Disease limited to the sinuses without a cerebral extension is associated with a lower risk of mortality. Interestingly, the use of zinc and azithromycin were not associated with increased mortality in our study. [ABSTRACT FROM AUTHOR]

Since the onset of the COVID-19 pandemic, numerous publications have appeared describing autoimmune pathologies developing after a coronavirus infection, with several papers reporting autoantibody production during the acute period of the disease. Several viral diseases are known to trigger autoimmune processes, and the appearance of catalytic antibodies with DNase activity is one of the earliest markers of several autoimmune pathologies. Therefore, we analyzed whether IgG antibodies from blood plasma of SARS-CoV-2 patients after recovery could bind and hydrolyze DNA. We analyzed how vaccination of patients with adenovirus Sputnik V vaccine influences the production of abzymes with DNase activity. Four groups were selected for the analysis, each containing 25 patients according to their relative titers of antibodies to S-protein: with high and median titers, vaccinated with Sputnik V with high titers, and a control group of donors with negative titers. The relative titers of antibodies against DNA and the relative DNase activity of IgGs depended very much on the individual patient and the donor, and no significant correlation was found between the relative values of antibodies titers and their DNase activity. Our results indicate that COVID-19 disease and vaccination with adenoviral Sputnik V vaccine do not result in the development or enhancement of strong autoimmune reactions as in the typical autoimmune diseases associated with the production of anti-DNA and DNA hydrolyzing antibodies. [ABSTRACT FROM AUTHOR]

A timely diagnosis of coronavirus is critical in order to control the spread of the virus. To aid in this, we propose in this paper a deep learning-based approach for detecting coronavirus patients using ultrasound imagery. We propose to exploit the transfer learning of a EfficientNet model pre-trained on the ImageNet dataset for the classification of ultrasound images of suspected patients. In particular, we contrast the results of EfficentNet-B2 with the results of ViT and gMLP. Then, we show the results of the three models by learning from scratch, i.e., without transfer learning. We view the detection problem from a multiclass classification perspective by classifying images as COVID-19, pneumonia, and normal. In the experiments, we evaluated the models on a publically available ultrasound dataset. This dataset consists of 261 recordings (202 videos + 59 images) belonging to 216 distinct patients. The best results were obtained using EfficientNet-B2 with transfer learning. In particular, we obtained precision, recall, and F1 scores of 95.84%, 99.88%, and 24 97.41%, respectively, for detecting the COVID-19 class. EfficientNet-B2 with transfer learning presented an overall accuracy of 96.79%, outperforming gMLP and ViT, which achieved accuracies of 93.03% and 92.82%, respectively. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 lockdowns imposed new challenges to couples who were planning to conceive. In this research paper, we aimed to study the perceptions of women in Jordan during the pandemic regarding fertility behavior, the desire to use assisted reproductive technology (ART) and the awareness and beliefs of potential risks related to conception. Methods: A validated online-based questionnaire was distributed to women from April–May 2020, Statistical analysis was performed using the statistical software SPSS version 22 and R software (2020); p values ≤ 0.05 were considered statistically significant. Results: The total number of participants was 814 women, with 78.2% of the participants (58.7% fertile and 76.6% infertile) believing that pregnancy during the COVID-19 pandemic could be risky. Among them, 16% and 40%, respectively, were trying to conceive during the pandemic, and 97.4% and 89.9%, respectively, were not willing to use ART if needed during the pandemic. Young, nulliparous women who were married for less than one year were significantly associated with the desire to conceive during the COVID-19 pandemic. Conclusion: This study concluded that the fertility behavior of women in Jordan changed during the pandemic, and their desire for natural conception and for using ART declined, as they believed that there were potential risks related to conceiving during the pandemic. However, the effect was greater among the general fertile population than the infertile. [ABSTRACT FROM AUTHOR]

Simple Summary: The family of coronaviruses comprises a diverse set of strains and variants which cause diseases from the common cold to COVID-19. Moreover, they infect a wide array of hosts from bats, camels, birds, to humans. Studying coronaviruses through the lens of host specificity provides a unique perspective to understanding the evolution, diversity and dynamics of this family. In particular, this can reveal groups of different hosts infected by similar strains, giving clues on strains which were more likely to have evolved to jump from one host to another. In this work, we frame host specificity as a classification task, in designing a very compact numerical representation of the spike sequences of different coronaviruses. Based on this numerical representation, classification methods are able to detect the target host with high accuracy. Such an approach can used to efficiently scale to large volumes of sequences, in order to unveil trends in the host specificity of different coronavirus strains. The study of host specificity has important connections to the question about the origin of SARS-CoV-2 in humans which led to the COVID-19 pandemic—an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona)viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating, and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is important in determining host specificity, since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among birds, bats, camels, swine, humans, and weasels, to name a few. We propose a feature embedding based on the well-known position weight matrix (PWM), which we call PWM2Vec, and we use it to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications, such as determining protein function and identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs from viral sequences to generate fixed-length feature vector representations, and use them in the context of host classification. The results on real world data show that when using PWM2Vec, machine learning classifiers are able to perform comparably to the baseline models in terms of predictive performance and runtime—in some cases, the performance is better. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the embeddings of the baseline models. [ABSTRACT FROM AUTHOR]

Patients with haemoglobin disorders, particularly β-thalassaemia or sickle cell disease (SCD) or combined forms, on account of their underlying disease pathology and associated (iron load mainly in the case of thalassaemia) co-morbidities are defined as high-risk individuals prone to develop more severe complications from coronavirus disease-2019 (COVID-19). Despite the fact that epidemiological evidence concerning severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection in these patients is currently limited across the world, it is expected that COVID-19 pandemic will have a very serious, negative impact on national economies, healthcare and social systems and consequently significant respective repercussions on the patients particularly chronic ones, and their families. Although this may be a temporary challenge in some countries of high HDI and robust health, public health and social infrastructures, this can be a long term challenge with serious to tragic consequences in countries particularly devoid of universally covered heath care systems. Thalassaemia International Federation (TIF) in this present paper summarises the key challenges as expressed by the patients, their families and involved health care professionals themselves prior and consequent to COVID-19 pandemic, describes its response during the pandemic and expresses its position in support of its global patient community. [ABSTRACT FROM AUTHOR]

Training the next generation of industrial engineers and managers is a constant challenge for academia, given the fast changes of industrial technology. The current and predicted development trends in applied technologies affecting industry worldwide as formulated in the Industry 4.0 initiative have clearly emphasized the needs for constantly adapting curricula. The sensible socioeconomic changes generated by the COVID-19 pandemic have induced significant challenges to society in general and industry. Higher education, specifically when dealing with Industry 4.0, must take these new challenges rapidly into account. Modernization of the industrial engineering curriculum combined with its migration to a blended teaching landscape must be updated in real-time with real-world cases. The COVID-19 crisis provides, paradoxically, an opportunity for dealing with the challenges of training industrial engineers to confront a virtual dematerialized work model which has accelerated during and will remain for the foreseeable future after the pandemic. The paper describes the methodology used for adapting, enhancing, and evaluating the learning and teaching experience under the urgent and unexpected challenges to move from face-to-face university courses distant and online teaching. The methodology we describe is built on a process that started before the onset of the pandemic, hence in the paper we start by describing the pre-COVID-19 status in comparison to published initiatives followed by the real time modifications we introduced in the faculty to adapt to the post-COVID-19 teaching/learning era. The focus presented is on Industry 4.0. subjects at the leading edge of the technology changes affecting the industrial engineering and technology management field. The manuscript addresses the flow from system design subjects to implementation areas of the curriculum, including practical examples and the rapid decisions and changes made to encompass the effects of the COVID-19 pandemic on content and teaching methods including feedback received from participants. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic represents not just a global health crisis, but may signal the beginning of a new era of economic activity, the potential consequences of which we currently do not fully understand. In this context, the mid-to-long-range impacts of the pandemic on the energy sector have been studied extensively in the last few months. Despite these efforts, the pandemic still raises many open questions concerning the long-term operation and planning of power systems. For instance, how will the pandemic affect the integration of renewable energy sources? Should current power system expansion plans change in light of the COVID-19 pandemic? What new tools should be provided to support system operators during global health crises? It is the purpose of this paper to better understand the many aspects of these open questions by reviewing the relevant recent literature and by analyzing measured data. We point out the main challenges that the pandemic introduced by presenting patterns of electricity generation and demand, frequency deviations, and load forecasting. Moreover, we suggest directions for future research that may assist in coping with the mentioned challenges. We hope that this paper will trigger fruitful discussions and encourage further research on these important emerging topics. [ABSTRACT FROM AUTHOR]

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models. [ABSTRACT FROM AUTHOR]

COVID-19 has spread around the world and caused serious public health and social problems. Although several vaccines have been authorized for emergency use, new effective antiviral drugs are still needed. Some repurposed drugs including Chloroquine, Hydroxychloroquine and Remdesivir were immediately used to treat COVID-19 after the pandemic. However, the therapeutic effects of these drugs have not been fully demonstrated in clinical studies. In this paper, we found an antimalarial drug, Naphthoquine, showed good broad-spectrum anti-coronavirus activity. Naphthoquineinhibited HCoV-229E, HCoV-OC43 and SARS-CoV-2 replication in vitro, with IC50 = 2.05 ± 1.44 μM, 5.83 ± 0.74 μM, and 2.01 ± 0.38 µM, respectively. Time-of-addition assay was also performed to explore at which stage Naphthoquine functions during SARS-CoV-2 replication. The results suggested that Naphthoquine may influence virus entry and post-entry replication. Considering the safety of Naphthoquine was even better than that of Chloroquine, we think Naphthoquine has the potential to be used as a broad-spectrum drug for coronavirus infection. [ABSTRACT FROM AUTHOR]

Dental clinics were suspected to be a hotspot for nosocomial transmission of coronavirus disease 19 (COVID-19), yet there has been no clear recommendation about emergency dental care and appropriate personal protective equipment during pandemics. In this paper, we aim to summarize recommendations for (i) patient risk assessment, (ii) patient triage, and (iii) measures to prevent infection of health professionals and nosocomial transmission in dental clinics. The available evidence was collected by performing searches on PubMed, Embase, and Cochrane databases. We reviewed papers on COVID-19, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), influenza, and related respiratory viral diseases. Legal and ethical frameworks, as well as international (e.g., World Health Organization (WHO)) and national (e.g., public health institutes, dental associations) guidelines were screened to summarize recommendations related to dental emergency care. To assess the patient risk, a questionnaire was developed to classify patients at unknown, high, and very high risk. Patient triage recommendations were summarized in a flow chart that graded the emergency level of treatments (i.e., urgent, as soon as possible, and postpone). Measures to prevent disease transmission based on current evidence were grouped for dental health professionals, dental clinics, and patients. The present recommendations may support health professionals implement preventative measures during the pandemic. [ABSTRACT FROM AUTHOR]

During disease outbreaks, women endure additional burdens associated with paid and unpaid work, often without consideration or the alleviation of other life responsibilities. This paper draws on the concept of the triple burden in theorizing the gender divisions in productive and reproductive work and community activities in the context of disaster. Events that include famine, war, natural disaster or disease outbreak are all well documented as increasing women's vulnerability to a worsening of gendered burdens. In the case of the Covid-19 coronavirus pandemic, this is no different. Focussing on Sri Lanka, Malaysia, Vietnam and Australia, the four vignettes in this paper serve to highlight the intersections between Covid-19 and gendered burdens, particularly in frontline work, unpaid care work and community activities. While pre-disaster gender burdens are well established as strong, our analysis during the early months of the pandemic indicates that women's burdens are escalating. We estimate that women will endure a worsening of their burdens until the pandemic is well under control, and for a long time after. Public policy and health efforts have not sufficiently acknowledged the issues concerned with the associations between gender and disease outbreaks. [ABSTRACT FROM AUTHOR]

This paper presents a rapid diagnostic device for the detection of the pandemic coronavirus (COVID-19) using a micro-immunosensor cavity resonator. Coronavirus has been declared an international public health crisis, so it is important to design quick diagnostic methods for the detection of infected cases, especially in rural areas, to limit the spread of the virus. Herein, a proof-of-concept is presented for a portable laboratory device for the detection of the SARS-CoV-2 virus using electromagnetic biosensors. This device is a microwave cavity resonator (MCR) composed of a sensor operating at industrial, scientific and medical (ISM) 2.45 GHz inserted in 3D housing. The changes of electrical properties of measured serum samples after passing the sensor surface are presented. The three change parameters of the sensor are resonating frequency value, amplitude and phase of the reflection coefficient |S11|. This immune-sensor offers a portable, rapid and accurate diagnostic method for the SARS-CoV-2 virus, which can enable on-site diagnosis of infection. Medical validation for the device is performed through biostatistical analysis using the ROC (Receiver Operating Characteristic) method. The predictive accuracy of the device is 63.3% and 60.6% for reflection and phase, respectively. The device has advantages of low cost, low size and weight and rapid response. It does need a trained technician to operate it since a software program operates automatically. The device can be used at ports' quarantine units, hospitals, etc. [ABSTRACT FROM AUTHOR]

In view of the current Coronavirus Disease 2019 (COVID-19) pandemic outbreak, the research community is focusing on development of diagnostics, treatment, and vaccines to halt or reverse this scenario. Although there are already various vaccines available, adaptive mutations in the SARS-CoV-2 genome can alter its pathogenic potential and, at the same time, increase the difficulty of developing drugs or immunization by vaccines. Nanotechnology carries a potential to act in all stages in fighting this viral disease, with several possibilities of strategies such as applying nanoparticles directly as antivirals in delivery systems against these viruses or incorporating them in materials, with power of achievement in therapeutics, vaccines and prevention. In this paper, we review and bring insights of recent studies using metal nanocomposites as antivirals against coronavirus and structurally similar viruses. [ABSTRACT FROM AUTHOR]

Human civilization is experiencing a critical situation that presents itself for a new coronavirus disease 2019 (COVID-19). This virus emerged in late December 2019 in Wuhan city, Hubei, China. The grim fact of COVID-19 is, it is highly contagious in nature, therefore, spreads rapidly all over the world and causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Responding to the severity of COVID-19 research community directs the attention to the analysis of COVID-19, to diminish its antagonistic impact towards society. Numerous studies claim that the subcontinent, i.e., Bangladesh, India, and Pakistan, could remain in the worst affected region by the COVID-19. In order to prevent the spread of COVID-19, it is important to predict the trend of COVID-19 beforehand the planning of effective control strategies. Fundamentally, the idea is to dependably estimate the reproduction number to judge the spread rate of COVID-19 in a particular region. Consequently, this paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers. More specifically, we use various models (for example, susceptible infection recovery (SIR), exponential growth (EG), sequential Bayesian (SB), maximum likelihood (ML) and time dependent (TD)) to estimate the reproduction numbers and observe the model fitness in the corresponding data set. Experimental results show that the reproduction numbers produced by these models are greater than 1.2 (approximately) indicates that COVID-19 is gradually spreading in the subcontinent. [ABSTRACT FROM AUTHOR]

The novel coronavirus SARS-CoV-2 is the seventh identified human coronavirus. Understanding the extent of pre-existing immunity induced by seropositivity to endemic seasonal coronaviruses and the impact of cross-reactivity on COVID-19 disease progression remains a key research question in immunity to SARS-CoV-2 and the immunopathology of COVID-2019 disease. This paper describes a panel of lentiviral pseudotypes bearing the spike (S) proteins for each of the seven human coronaviruses (HCoVs), generated under similar conditions optimized for high titre production allowing a high-throughput investigation of antibody neutralization breadth. Optimal production conditions and most readily available permissive target cell lines were determined for spike-mediated entry by each HCoV pseudotype: SARS-CoV-1, SARS-CoV-2 and HCoV-NL63 best transduced HEK293T/17 cells transfected with ACE2 and TMPRSS2, HCoV-229E and MERS-CoV preferentially entered HUH7 cells, and CHO cells were most permissive for the seasonal betacoronavirus HCoV-HKU1. Entry of ACE2 using pseudotypes was enhanced by ACE2 and TMPRSS2 expression in target cells, whilst TMPRSS2 transfection rendered HEK293T/17 cells permissive for HCoV-HKU1 and HCoV-OC43 entry. Additionally, pseudotype viruses were produced bearing additional coronavirus surface proteins, including the SARS-CoV-2 Envelope (E) and Membrane (M) proteins and HCoV-OC43/HCoV-HKU1 Haemagglutinin-Esterase (HE) proteins. This panel of lentiviral pseudotypes provides a safe, rapidly quantifiable and high-throughput tool for serological comparison of pan-coronavirus neutralizing responses; this can be used to elucidate antibody dynamics against individual coronaviruses and the effects of antibody cross-reactivity on clinical outcome following natural infection or vaccination. [ABSTRACT FROM AUTHOR]

Transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurs through respiratory droplets passed directly from person to person or indirectly through fomites, such as common use surfaces or objects. The aim of this study was to determine the virucidal efficacy of blue LED (405 nm) and far-UVC (222 nm) light in comparison to standard UVC (254 nm) irradiation for the inactivation of feline infectious peritonitis virus (FIPV) on different matrices as a model for SARS-CoV-2. Wet or dried FIPV on stainless steel, plastic, or paper discs, in the presence or absence of artificial saliva, were exposed to various wavelengths of light for different time periods (1–90 min). Dual activity of blue LED and far-UVC lights were virucidal for most wet and dried FIPV within 4 to 16 min on all matrices. Individual action of blue LED and far-UVC lights were virucidal for wet FIPV but required longer irradiation times (8–90 min) to reach a 4-log reduction. In comparison, LED (265 nm) and germicidal UVC (254 nm) were virucidal on almost all matrices for both wet and dried FIPV within 1 min exposure. UVC was more effective for the disinfection of surfaces as compared to blue LED and far-UVC individually or together. However, dual action of blue LED and far-UVC was virucidal. This combination of lights could be used as a safer alternative to traditional UVC. [ABSTRACT FROM AUTHOR]

Simple Summary: In view of the current global situation due to the COVID-19 pandemic and considering the evidence that SARS-CoV-2 has the ability to infect multiple species, this work was aimed at identifying the clinical signs of SARS-CoV-2 infection in domestic and wild felids. Our results evince that the signs developed in felids show similar progression to those occurring in humans, suggesting a relationship between the viral cycle and target tissues of the virus in different species. Recently, it has been proved that SARS-CoV-2 has the ability to infect multiple species. This work was aimed at identifying the clinical signs of SARS-CoV-2 infection in domestic and wild felids. A PRISMA-based systematic review was performed on case reports on domestic and wild cats, reports on experimental infections, case reports in databases, preprints and published press releases. Descriptive statistical analysis of the data was performed. A total of 256 articles, 63 detailed official reports and 2 press articles on SARS-CoV-2 infection in domestic and wild cats were analyzed, of which 19 articles and 65 reports were finally included. In domestic cats, most cats' infections are likely to be asymptomatic, and 46% of the reported infected animals were symptomatic and predominantly presented respiratory signs such as sneezing and coughing. In wild felines, respiratory clinical signs were most frequent, and up to 96.5% of the reported affected animals presented coughing. It is noteworthy that, to date, symptomatic animals with SARS-CoV-2 infection have been reported to belong to two different subfamilies (Phanterinae and Felinae), with up to five different felid species affected within the Felidae family. Reported results evince that the signs developed in felids show similar progression to those occurring in humans, suggesting a relationship between the viral cycle and target tissues of the virus in different species. While viral transmission to humans in contact with animal populations has not been reported, spill-back could result in the emergence of immune-escape mutants that might pose a risk to public health. Despite the clear results in the identification of the typical clinical picture of SARS-CoV-2 infection in felines, the number of detailed academic reports and papers on the subject is scarce. Therefore, further description of these cases will allow for more accurate and statistically robust clinical approaches in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: Intranasal vaccination is one of the methods used to stimulate mucosal immunity. It has been widely practised to control many human and animal respiratory diseases. Coronavirus disease 2019 (COVID-19) is a highly contagious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which resulted in a global pandemic. COVID-19 has reminded some veterinarians of various contagious veterinary diseases, including coronavirus infections in animals. This article discusses the control of highly contagious diseases of veterinary importance with emphasis on an intranasal vaccination approach, and the potential of implementing similar strategies in human medicine to control the ongoing COVID-19 pandemic. The world is currently facing an ongoing coronavirus disease 2019 (COVID-19) pandemic. The disease is a highly contagious respiratory disease which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Current control measures used by many countries include social distancing, wearing face masks, frequent hand washing, self-isolation, and vaccination. The current commercially available vaccines are injectable vaccines, although a few intranasal vaccines are in trial stages. The reported side effects of COVID-19 vaccines, perceptions towards the safety of the vaccines, and frequent mutation of the virus may lead to poor herd immunity. In veterinary medicine, attaining herd immunity is one of the main considerations in disease control, and herd immunity depends on the use of efficacious vaccines and the vaccination coverage in a population. Hence, many aerosol or intranasal vaccines have been developed to control veterinary respiratory diseases such as Newcastle disease, rinderpest, infectious bronchitis, and haemorrhagic septicaemia. Different vaccine technologies could be employed to improve vaccination coverage, including the usage of an intranasal live recombinant vaccine or live mutant vaccine. This paper discusses the potential use of intranasal vaccination strategies against human COVID-19, based on a veterinary intranasal vaccine strategy. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused by SARS-CoV-2 coronavirus deeply affected the world community. It gave a strong impetus to the development of not only approaches to diagnostics and therapy, but also fundamental research of the molecular biology of this virus. Fluorescence microscopy is a powerful technology enabling detailed investigation of virus–cell interactions in fixed and live samples with high specificity. While spatial resolution of conventional fluorescence microscopy is not sufficient to resolve all virus-related structures, super-resolution fluorescence microscopy can solve this problem. In this paper, we review the use of fluorescence microscopy to study SARS-CoV-2 and related viruses. The prospects for the application of the recently developed advanced methods of fluorescence labeling and microscopy—which in our opinion can provide important information about the molecular biology of SARS-CoV-2—are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a method of implementation and the results of aerosol dispersion tests in underground mine workings. Numerous tests were carried out to determine the potential risk of SARS-CoV-2 coronavirus infection in the underground environment of the mines. The influence of selected parameters of mine air on the possibility and method of aerosol transmission through ventilation routes was experimentally determined in real conditions. The concentration of additional aerosols in the class of ultrafine and fine aerosols increased with the distance from the generator, while the concentration of coarse particles decreased. Assuming the consumption of the solution with which aerosols were generated, even at a small level of 1 cm3/min., the number of additional aerosols was several hundred particles in one cubic centimeter of air at a distance of 50–70 m from the generator. The concentration of ultrafine particles in the range of 40–20,000 nm increased from 122 particles/cm3 to 209 particles/cm3 at air temperature of 12 °C and relative humidity of 95–96%, and from 90 particles/cm3 to 243 particles/cm3 at air temperature of 17 °C and relative humidity of 76–82%, with the increasing distance from the generator (10 m to 50 m). [ABSTRACT FROM AUTHOR]

Since the initial identification of the novel coronavirus SARS-CoV-2 in December 2019, the COVID-19 pandemic has become a leading cause of morbidity and mortality worldwide. As effective vaccines and treatments begin to emerge, it will become increasingly important to identify and proactively manage the long-term respiratory complications of severe disease. The patterns of imaging abnormalities coupled with data from prior coronavirus outbreaks suggest that patients with severe COVID-19 pneumonia are likely at an increased risk of progression to interstitial lung disease (ILD) and chronic pulmonary vascular disease. In this paper, we briefly review the definition, classification, and underlying pathophysiology of interstitial lung disease (ILD). We then review the current literature on the proposed mechanisms of lung injury in severe COVID-19 infection, and outline potential viral- and immune-mediated processes implicated in the development of post-COVID-19 pulmonary fibrosis (PCPF). Finally, we address patient-specific and iatrogenic risk factors that could lead to PCPF and discuss strategies for reducing risk of pulmonary complications/sequelae. [ABSTRACT FROM AUTHOR]

Coronaviruses are a group of RNA (ribonucleic acid) viruses with the capacity for rapid mutation and recombination. Coronaviruses are known to cause respiratory or intestinal infections in humans and animals. In this paper, a biologically compatible set of nonlinear fractional differential equations governing the outbreak of the novel coronavirus is suggested based on a model previously proposed in the literature. Then, this set is numerically solved utilizing two new methods employing sine–cosine and Bernoulli wavelets and their operational matrices. Moreover, the convergence of the solution is experimentally studied. Furthermore, the accuracy of the solution is proved via comparing the results with those obtained in previous research for the primary model. Furthermore, the computational costs are compared by measuring the CPU running time. Finally, the effects of the fractional orders on the outbreak of the COVID-19 are investigated. [ABSTRACT FROM AUTHOR]

In this paper, a highly sensitive graphene-based multiple-layer (BK7/Au/PtSe2/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θSPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique. [ABSTRACT FROM AUTHOR]

COVID-19 has driven several global offline communities to go online. Restrictions to the free movement of people in response to the coronavirus pandemic triggered a profound rethinking of jobs, products and services, and among them, the activities of religious communities, which are well consolidated in the offline sphere. In Spain, since the lockdown established by the government in March 2020, the Catholic Church has reinvented its activity, as all the churches and other places of worship have been closed. This constituted a considerable challenge, considering the history and dynamics of the institution. This paper aims to analyze how Catholicism, as one of the most consolidated offline communities, reworked its communication, going online in a matter of days. With this objective, researchers surveyed each and every one of the 70 Spanish dioceses, taking them as representatives of the global Catholic community in the country. Their responses are complemented with an in-depth interview with the Director of Communications at the Spanish Conference of Bishops. The results highlight the huge and unprecedented step towards the digitalization of the community through consistent, creative and efficient action. New methods, platforms and languages have been implemented, even broadening community membership. Despite an offline essence that is still detected in some decisions, this pandemic has brought a new communicative paradigm to the Spanish Catholic community. Digitalization has been consolidated whilst preserving the best aspects of direct contact and action. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges. [ABSTRACT FROM AUTHOR]

The establishment of selective colonies of potential vertebrate hosts for viruses would provide experimental models for the understanding of pathogen-host interactions. This paper briefly surveys the reasons to conduct such studies and how the results might provide information that could be applied to disease prevention activities. [ABSTRACT FROM AUTHOR]

Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection. [ABSTRACT FROM AUTHOR]

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4 % , 99.6 % , 99.8 % , 99.6 % , and 99.4 % on the SARS-CoV-2 dataset, and 92.9 % , 91.3 % , 93.7 % , 92.2 % , and 92.5 % on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models' predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists. [ABSTRACT FROM AUTHOR]

Emetine is a potent antiviral that acts on many viruses in the low-nM range, with several studies in animals and humans demonstrating antiviral activity. Historically, emetine was used to treat patients with Spanish influenza, in the last stages of the pandemic in the early 1900s. Some of these patients were "black" with cyanosis. Emetine rapidly reversed the cyanosis and other symptoms of this disease in 12–24 h. However, emetine also has been shown to have anti-inflammatory properties and it appears it is these anti-inflammatory properties that were responsible for the effects seen in patients with Spanish influenza. Emetine, in the past, has also been used in 10s to 100s of millions of people at a dose of ~60 mg daily to treat amoebiasis. Based on viral inhibition data we can calculate a likely SARS-CoV2 antiviral dose of ~1/10th the amoebiasis dose, which should dramatically reduce the risk of any side effects. While there are no anti-inflammatory dose response data available, based on the potential mode of action, the anti-inflammatory actions may also occur at low doses. This paper also examines the toxicity of emetine seen in clinical practice and that seen in the laboratory, and discusses the methods of administration aimed at reducing side effects if higher doses were found to be necessary. While emetine is a "pure drug" as it is extracted from ipecac, some of the differences between emetine and ipecac are also discussed. [ABSTRACT FROM AUTHOR]

There is a current pandemic of a new type of coronavirus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The number of confirmed infected cases has been rapidly increasing. This paper analyzes the characteristics of SARS-CoV-2 in comparison with Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and influenza. COVID-19 is similar to the diseases caused by SARS-CoV and MERS-CoV virologically and etiologically, but closer to influenza in epidemiology and virulence. The comparison provides a new perspective for the future of the disease control, and offers some ideas in the prevention and control management strategy. The large number of infectious people from the origin, and the highly infectious and occult nature have been two major problems, making the virus difficult to eradicate. We thus need to contemplate the possibility of long-term co-existence with COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has tremendously changed private and professional interactions and behaviors worldwide. The effects of this pandemic and the actions taken have changed our healthcare systems, which consequently has affected medical education and surgical training. In the face of constant disruptions of surgical education and training during this pandemic outbreak, structured and innovative concepts and adapted educational curricula are important to ensure a high quality of medical treatment. While efforts were undertaken to prevent viral spreading, it is important to analyze and assess the effects of this crisis on medical education, surgical training and teaching at large and certainly in the field of surgical oncology. Against this background, in this paper we introduce practical and creative recommendations for the continuity of students' and residents' medical and surgical training and teaching. This includes virtual educational curricula, skills development classes, video-based feedback and simulation in the specialty field of surgical oncology. In conclusion, the effects of COVID 19 on Surgical Training and Teaching, certainly in the field of Surgical Oncology, are challenging. [ABSTRACT FROM AUTHOR]

Simple Summary: The introduction of immune checkpoint inhibitors (ICI) in 2011 revolutionized the management of many solid cancers and hematological malignancies. However, there are concerns regarding the use of ICI in the era of COVID-19. We present currently available information on the pros and cons of using ICI in cancer patients with respect to the risk of acquiring an infection by SARS-CoV2 and mortality from COVID-19. By means of the present paper, clinicians and researchers may update their knowledge on a highly topical clinical question—is the use of ICI in cancer patients with SARS-CoV2 infection harmful with respect to COVID-19 outcome? Whether cancer patients receiving immune checkpoint inhibitors (ICI) are at an increased risk of severe infection and mortality during the corona pandemic is a hotly debated topic that will continue to evolve. Here, we summarize and discuss current studies regarding COVID-19 and anti-cancer treatment with an emphasis on ICI. Importantly, several lines of evidence suggest that patients currently treated with ICI do not display an increased vulnerability to infection with SARS-CoV-2. Data regarding morbidity and mortality associated with COVID-19 in cancer patients receiving ICI are less clear and often conflicting. Although mostly based on experimental data, it is possible that ICI can promote the exacerbated immune response associated with adverse outcome in COVID-19 patients. On the other hand, mounting evidence suggests that ICI might even be useful in the treatment of viral infections by preventing or ameliorating T cell exhaustion. In this context, the right timing of treatment might be essential. Nevertheless, some cancer patients treated with ICI experience autoimmune-related side effects that require the use of immunosuppressive therapies, which in turn may promote a severe course of infection with SARS-CoV-2. Although there is clear evidence that withholding ICI will have more serious consequences, further studies are urgently needed in to better evaluate the effects of ICI in patients with COVID-19 and the use of ICI during the corona pandemic in general. [ABSTRACT FROM AUTHOR]

Background: To prioritize the development of antiviral compounds, it is necessary to compare their relative preclinical activity and clinical efficacy. Methods: We reviewed in vitro, animal model, and clinical studies of candidate anti-coronavirus compounds and placed extracted data in an online relational database. Results: As of August 2020, the Coronavirus Antiviral Research Database (CoV-RDB; covdb.stanford.edu) contained over 2800 cell culture, entry assay, and biochemical experiments, 259 animal model studies, and 73 clinical studies from over 400 published papers. SARS-CoV-2, SARS-CoV, and MERS-CoV account for 85% of the data. Approximately 75% of experiments involved compounds with known or likely mechanisms of action, including monoclonal antibodies and receptor binding inhibitors (21%), viral protease inhibitors (17%), miscellaneous host-acting inhibitors (10%), polymerase inhibitors (9%), interferons (7%), fusion inhibitors (5%), and host protease inhibitors (5%). Of 975 compounds with known or likely mechanism, 135 (14%) are licensed in the U.S. for other indications, 197 (20%) are licensed outside the U.S. or are in human trials, and 595 (61%) are pre-clinical investigational compounds. Conclusion: CoV-RDB facilitates comparisons between different candidate antiviral compounds, thereby helping scientists, clinical investigators, public health officials, and funding agencies prioritize the most promising compounds and repurposed drugs for further development. [ABSTRACT FROM AUTHOR]

Increasing evidence suggests that Severe Acute Respiratory Syndrome-coronavirus-2 (SARS-CoV-2) can also invade the central nervous system (CNS). However, findings available on its neurological manifestations and their pathogenic mechanisms have not yet been systematically addressed. A literature search on neurological complications reported in patients with COVID-19 until June 2020 produced a total of 23 studies. Overall, these papers report that patients may exhibit a wide range of neurological manifestations, including encephalopathy, encephalitis, seizures, cerebrovascular events, acute polyneuropathy, headache, hypogeusia, and hyposmia, as well as some non-specific symptoms. Whether these features can be an indirect and unspecific consequence of the pulmonary disease or a generalized inflammatory state on the CNS remains to be determined; also, they may rather reflect direct SARS-CoV-2-related neuronal damage. Hematogenous versus transsynaptic propagation, the role of the angiotensin II converting enzyme receptor-2, the spread across the blood-brain barrier, the impact of the hyperimmune response (the so-called "cytokine storm"), and the possibility of virus persistence within some CNS resident cells are still debated. The different levels and severity of neurotropism and neurovirulence in patients with COVID-19 might be explained by a combination of viral and host factors and by their interaction. [ABSTRACT FROM AUTHOR]

The rampant spread of COVID-19 and the worldwide prevalence of infected cases demand a rapid, simple, and cost-effective Point of Care Test (PoCT) for the accurate diagnosis of this pandemic. The most common molecular tests approved by regulatory bodies across the world for COVID-19 diagnosis are based on Polymerase Chain Reaction (PCR). While PCR-based tests are highly sensitive, specific, and remarkably reliable, they have many limitations ranging from the requirement of sophisticated laboratories, need of skilled personnel, use of complex protocol, long wait times for results, and an overall high cost per test. These limitations have inspired researchers to search for alternative diagnostic methods that are fast, economical, and executable in low-resource laboratory settings. The discovery of Loop-mediated isothermal Amplification (LAMP) has provided a reliable substitute platform for the accurate detection of low copy number nucleic acids in the diagnosis of several viral diseases, including epidemics like Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). At present, a cocktail of LAMP assay reagents along with reverse transcriptase enzyme (Reverse Transcription LAMP, RT-LAMP) can be a robust solution for the rapid and cost-effective diagnosis for COVID-19, particularly in developing, and low-income countries. In summary, the development of RT-LAMP based diagnostic tools in a paper/strip format or the integration of this method into a microfluidic platform such as a Lab-on-a-chip may revolutionize the concept of PoCT for COVID-19 diagnosis. This review discusses the principle, technology and past research underpinning the success for using this method for diagnosing MERS and SARS, in addition to ongoing research, and the prominent prospect of RT-LAMP in the context of COVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19), due to the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become an epidemiological threat and a worldwide concern. SARS-CoV-2 has spread to 210 countries worldwide and more than 6,500,000 confirmed cases and 384,643 deaths have been reported, while the number of both confirmed and fatal cases is continually increasing. COVID-19 is a viral disease that can affect every age group—from infants to the elderly—resulting in a wide spectrum of various clinical manifestations. COVID-19 might present different degrees of severity—from mild or even asymptomatic carriers, even to fatal cases. The most common complications include pneumonia and acute respiratory distress syndrome. Fever, dry cough, muscle weakness, and chest pain are the most prevalent and typical symptoms of COVID-19. However, patients might also present atypical symptoms that can occur alone, which might indicate the possible SARS-CoV-2 infection. The aim of this paper is to review and summarize all of the findings regarding clinical manifestations of COVID-19 patients, which include respiratory, neurological, olfactory and gustatory, gastrointestinal, ophthalmic, dermatological, cardiac, and rheumatologic manifestations, as well as specific symptoms in pediatric patients. [ABSTRACT FROM AUTHOR]

It is well-known that atmospheric pollution, first and foremost the particulate matter (PM), causes serious diseases in humans. China's metropolises and Italy's Po Valley have in fact achieved a concerning degree of notoriety thanks to runaway air pollution problems. The spread of viral respiratory diseases is facilitated in polluted environments, an example of which is the respiratory syncytial virus bronchiolitis. In this opinion paper, we consider the possible relationship between air pollution, primarily airborne PM10–2.5, and the spread of the novel coronavirus in Northern Italy. If it is true that the novel coronavirus remains active from some hours to several days on various surfaces, it is logical to postulate that the same can occur when it is adsorbed or absorbed by the atmospheric particulate matter, which may also help carry the virus into the human respiratory system. As the Earth presents us with a very high bill to pay, governments and other authorities need to take prompt action to counter excessive pollution levels, both in Italy and in other countries. [ABSTRACT FROM AUTHOR]

Since the declaration of the global pandemic of COVID-19 by the World Health Organization on 11 March 2020, we have continued to see a steady rise in the number of patients infected by SARS-CoV-2. However, there is still very limited data on the course and outcomes of this serious infection in a vulnerable population of pregnant patients and their fetuses. International perinatal societies and institutions including SMFM, ACOG, RCOG, ISUOG, CDC, CNGOF, ISS/SIEOG, and CatSalut have released guidelines for the care of these patients. We aim to summarize these current guidelines in a comprehensive review for patients, healthcare workers, and healthcare institutions. We included 15 papers from 10 societies through a literature search of direct review of society's websites and their journal publications up till 20 April 2020. Recommendations specific to antepartum, intrapartum, and postpartum were abstracted from the publications and summarized into Tables. The summary of guidelines for the management of COVID-19 in pregnancy across different perinatal societies is fairly consistent, with some variation in the strength of recommendations. It is important to recognize that these guidelines are frequently updated, as we continue to learn more about the course and impact of COVID-19 in pregnancy. [ABSTRACT FROM AUTHOR]

The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. In this paper, we set out to shed light on the SARS-CoV-2/host receptor recognition, a crucial factor for successful virus infection. Based on the current knowledge of the interactome between SARS-CoV-2 and host cell proteins, we performed Master Regulator Analysis to detect which parts of the human interactome are most affected by the infection. We detected, amongst others, affected apoptotic and mitochondrial mechanisms, and a downregulation of the ACE2 protein receptor, notions that can be used to develop specific therapies against this new virus. [ABSTRACT FROM AUTHOR]

The COVID-19 coronavirus is currently spreading around the globe with limited treatment options available. This article presents the rationale for potentially using old drugs (emetine, other ipecac alkaloids or analogues) that have been used to treat amoebiasis in the treatment of COVID-19. Emetine had amongst the lowest reported half-maximal effective concentration (EC50) from over 290 agents screened for the Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS) coronaviruses. While EC50 concentrations of emetine are achievable in the blood, studies show that concentrations of emetine can be almost 300 times higher in the lungs. Furthermore, based on the relative EC50s of emetine towards the coronaviruses compared with Entamoeba histolytica, emetine could be much more effective as an anti-coronavirus agent than it is against amoebiasis. This paper also discusses the known side effects of emetine and related compounds, how those side effects can be managed, and the optimal method of administration for the potential treatment of COVID-19. Given the serious and immediate threat that the COVID-19 coronavirus poses, our long history with emetine and the likely ability of emetine to reach therapeutic concentrations within the lungs, ipecac, emetine, and other analogues should be considered as potential treatment options, especially if in vitro studies confirm viral sensitivity. [ABSTRACT FROM AUTHOR]