New Brazilian variant of the SARS-CoV-2 (P1/Gamma) of COVID-19 in Alagoas state

RNA-binding domain of SARS-CoV2 nucleocapsid: MD simulation study of the effect of the proline substitutions P67S and P80R on the structure of the protein

The nucleocapsid component of SARS-CoV2 is involved in the viral genome packaging. GammaP.1(Brazil) and the 20 C-US(USA) variants had a high frequency of the P80R and P67S mutations respectively in the RNA-binding domain of the nucleocapsid. Since RNA-binding domain participates in the electrostatic interactions with the viral genome, the study of the effects of proline substitutions on the flexibility of the protein will be meaningful. It evinced that the trajectory of the wildtype and mutants was stable during the simulation and exhibited distinct changes in the flexibility of the protein. Moreover, the beta-hairpin loop region of the protein structures exhibited high amplitude fluctuations and dominant motions. Additionally, modulations were detected in the drug binding site. Besides, the extent of correlation and anti-correlation motions involving the protruding region, helix, and the other RNA binding sites differed between the wildtype and mutants. The secondary structure analysis disclosed the variation in the occurrence pattern of the secondary structure elements between the proteins. Protein-ssRNA interaction analysis was also done to detect the amino acid contacts with ssRNA. R44, R59, and Y61 residues of the wildtype and P80R mutant exhibited different duration contacts with the ssRNA. It was also noticed that R44, R59, and Y61 of the wildtype and P80R formed hydrogen bonds with the ssRNA. However in P67S, residues T43, R44, R45, R40, R59, and R41 displayed contacts and formed hydrogen bonds with ssRNA. Binding free energy was also calculated and was lowest for P67S than wildtype andP80R. Thus, proline substitutions influence the structure of the RNA-binding domain and may modulate viral genome packaging besides the host-immune response.Communicated by Ramaswamy H. Sarma.

Using Association Rules to Obtain Sets of Prevalent Symptoms throughout the COVID-19 Pandemic: An Analysis of Similarities between Cases of COVID-19 and Unspecified SARS in São Paulo-Brazil

The efficient recognition of symptoms in viral infections holds promise for swift and precise diagnosis, thus mitigating health implications and the potential recurrence of infections. COVID-19 presents unique challenges due to various factors influencing diagnosis, especially regarding disease symptoms that closely resemble those of other viral diseases, including other strains of SARS, thus impacting the identification of useful and meaningful symptom patterns as they emerge in infections. Therefore, this study proposes an association rule mining approach, utilising the Apriori algorithm to analyse the similarities between individuals with confirmed SARS-CoV-2 diagnosis and those with unspecified SARS diagnosis. The objective is to investigate, through symptom rules, the presence of COVID-19 patterns among individuals initially not diagnosed with the disease. Experiments were conducted using cases from Brazilian SARS datasets for São Paulo State. Initially, reporting percentage similarities of symptoms in both groups were analysed. Subsequently, the top ten rules from each group were compared. Finally, a search for the top five most frequently occurring positive rules among the unspecified ones, and vice versa, was conducted to identify identical rules, with a particular focus on the presence of positive rules among the rules of individuals initially diagnosed with unspecified SARS.

Potential zoonotic spillover at the human-animal interface: A mini-review

Wildlife markets and wet wildlife markets, a type of human-animal interface, are commonly trading centers for wild-caught and captive-exotic animals as well as their products. These markets provide an ideal environment for spillovers of zoonotic and emerging infectious diseases (EIDs). These conditions may raise serious concerns, particularly in relation to wildlife species that frequently interact with humans and domestic animals. EIDs pose a significant risk to humans, ecosystems, and public health, as demonstrated by the current COVID-19 pandemic, and other previous outbreaks, including the highly pathogenic avian influenza H5N1. Even though it seems appears impossible to eliminate EIDs, we may still be able to minimalize the risks and take several measures to prevent new EIDs originated from animals. The aim of this study was to review several types of human-animal interfaces with a high risk of zoonotic spillover, infectious agents, and animal hosts or reservoirs. Identifying those factors will support the development of interventions and effective disease control in human-animal interface settings.

Prediction models of COVID-19 fatality in nine Peruvian provinces: A secondary analysis of the national epidemiological surveillance system

There are initiatives to promote the creation of predictive COVID-19 fatality models to assist decision-makers. The study aimed to develop prediction models for COVID-19 fatality using population data recorded in the national epidemiological surveillance system of Peru. A retrospective cohort study was conducted (March to September of 2020). The study population consisted of confirmed COVID-19 cases reported in the surveillance system of nine provinces of Lima, Peru. A random sample of 80% of the study population was selected, and four prediction models were constructed using four different strategies to select variables: 1) previously analyzed variables in machine learning models; 2) based on the LASSO method; 3) based on significance; and 4) based on a post-hoc approach with variables consistently included in the three previous strategies. The internal validation was performed with the remaining 20% of the population. Four prediction models were successfully created and validate using data from 22,098 cases. All models performed adequately and similarly; however, we selected models derived from strategy 1 (AUC 0.89, CI95% 0.87-0.91) and strategy 4 (AUC 0.88, CI95% 0.86-0.90). The performance of both models was robust in validation and sensitivity analyses. This study offers insights into estimating COVID-19 fatality within the Peruvian population. Our findings contribute to the advancement of prediction models for COVID-19 fatality and may aid in identifying individuals at increased risk, enabling targeted interventions to mitigate the disease. Future studies should confirm the performance and validate the usefulness of the models described here under real-world conditions and settings.

From Alpha to Omicron: How Different Variants of Concern of the SARS-Coronavirus-2 Impacted the World

SARS-CoV-2, the virus that causes COVID-19, is prone to mutations and the generation of genetic variants. Since its first outbreak in 2019, SARS-CoV-2 has continually evolved, resulting in the emergence of several lineages and variants of concern (VOC) that have gained more efficient transmission, severity, and immune evasion properties. The World Health Organization has given these variants names according to the letters of the Greek Alphabet, starting with the Alpha (B.1.1.7) variant, which emerged in 2020, followed by the Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529) variants. This review explores the genetic variation among different VOCs of SARS-CoV-2 and how the emergence of variants made a global impact on the pandemic.

An update on vaccine status and the role of nanomedicine against SARS-CoV-2: A narrative review

Background and Aims

Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 novel coronavirus, is a highly communicable disease that gave rise to the ongoing pandemic. Despite prompt action across many laboratories in many countries, effective management of this disease is still out of reach. The focus of this review is to describe various vaccination approaches and nanomedicine-based delivery systems against COVID-19.

Methods

The articles included in this study were searched and added from different electronic databases, including PubMed, Scopus, Cochrane, Embase, and preprint databases.

Results

Mass immunization with vaccines is currently at the forefront of COVID-19 infection control. Such vaccines are live attenuated vaccines, inactivated vaccines, nucleic acid-based vaccines, protein subunit vaccines, viral-vector vaccines, and virus-like particle platforms. However, many promising avenues are currently being explored in laboratory and clinical settings, including treatment options, prevention, diagnosis, and management of the disease. Soft nanoparticles like lipid nanoparticles (solid lipid nanoparticles (SLNPs), liposomes, nanostructured lipid carriers, nanoemulsions, and protein nanoparticles play an essential role in nanomedicine. Because of their unique and excellent properties, nanomedicines have potential applications in treating COVID-19 disease.

Conclusions

This review work provides an overview of the therapeutic aspects of COVID-19, including vaccination and the role of nanomedicines in the diagnosis, treatment, and prevention of COVID-19.

Tracing COVID-19 Trails in Wastewater: A Systematic Review of SARS-CoV-2 Surveillance with Viral Variants

The emergence of new variants of SARS-CoV-2 associated with varying infectivity, pathogenicity, diagnosis, and effectiveness against treatments challenged the overall management of the COVID-19 pandemic. Wastewater surveillance (WWS), i.e., monitoring COVID-19 infections in communities through detecting viruses in wastewater, was applied to track the emergence and spread of SARS-CoV-2 variants globally. However, there is a lack of comprehensive understanding of the use and effectiveness of WWS for new SARS-CoV-2 variants. Here we systematically reviewed published articles reporting monitoring of different SARS-CoV-2 variants in wastewater by following the PRISMA guidelines and provided the current state of the art of this study area. A total of 80 WWS studies were found that reported different monitoring variants of SARS-CoV-2 until November 2022. Most of these studies (66 out of the total 80, 82.5%) were conducted in Europe and North America, i.e., resource-rich countries. There was a high variation in WWS sampling strategy around the world, with composite sampling (50/66 total studies, 76%) as the primary method in resource-rich countries. In contrast, grab sampling was more common (8/14 total studies, 57%) in resource-limited countries. Among detection methods, the reverse transcriptase polymerase chain reaction (RT-PCR)-based sequencing method and quantitative RT-PCR method were commonly used for monitoring SARS-CoV-2 variants in wastewater. Among different variants, the B1.1.7 (Alpha) variant that appeared earlier in the pandemic was the most reported (48/80 total studies), followed by B.1.617.2 (Delta), B.1.351 (Beta), P.1 (Gamma), and others in wastewater. All variants reported in WWS studies followed the same pattern as the clinical reporting within the same timeline, demonstrating that WWS tracked all variants in a timely way when the variants emerged. Thus, wastewater monitoring may be utilized to identify the presence or absence of SARS-CoV-2 and follow the development and transmission of existing and emerging variants. Routine wastewater monitoring is a powerful infectious disease surveillance tool when implemented globally.

Wastewater-based epidemiological investigation of SARS-CoV-2 in Porto Alegre, Southern Brazil

Wastewater-based epidemiology (WBE) may be successfully used to comprehensively monitor and determine the scale and dynamics of some infections in the community. We monitored severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in raw wastewater samples from Porto Alegre, Southern Brazil. The samples were collected and analyzed every week between May 2020 to May 2021. Meanwhile, different social restrictions were applied according to the number of hospitalized patients in the region. Weekly samples were obtained from two wastewater treatment plants (WWTP), named Navegantes and Serraria. To determine the SARS-CoV-2 RNA titers in wastewater, we performed RT-qPCR analysis targeting the N gene (N1). The highest titer of SARS-CoV-2 RNA was observed between epidemiological weeks (EWs) 33-37 (August), 42-43 (October), 45-46 (November), 49-51 (December) in 2020, and 1-3 (January), 7-13 (February to March) in 2021, with viral loads ranging from 1 × 106-3 × 106 genomic copies/Liter. An increase in positive confirmed cases followed such high viral loads. Depending on the sampling method used, positive cases increased in 6-7 days and 15 days after the rise of viral RNA titers in wastewater, with composite sampling methods showing a lower time lag and a higher resolution on the analyses. The results showed a direct relation between strict social restrictions and the loads of detected RNA reduction in wastewater, corroborating the number of confirmed cases. Differences in viral loads between different sampling points and methods were observed, as composite samples showed more stable results during the analyzed period. Besides, viral loads obtained from samples collected at Serraria WWTP were consistently higher than the ones obtained at Navegantes WWTP, indicating differences in local dynamics of SARS-CoV-2 spread in different regions of Porto Alegre. In conclusion, wastewater sampling to monitor SARS-CoV-2 is a robust tool to evaluate the viral loads contributing to hospitalized patients' data and confirmed cases. In addition, SARS-CoV-2 detection in sewage may inform and alert the government when there are asymptomatic or non-tested patients.

Characteristics of replication and pathogenicity of SARS-CoV-2 Alpha and Delta isolates

The continuously arising of SARS-CoV-2 variants has been posting a great threat to public health safety globally, from B.1.17 (Alpha), B.1.351 (Beta), P.1 (Gamma), B.1.617.2 (Delta) to B.1.1.529 (Omicron). The emerging or re-emerging of the SARS-CoV-2 variants of concern is calling for the constant monitoring of their epidemics, pathogenicity and immune escape. In this study, we aimed to characterize replication and pathogenicity of the Alpha and Delta variant strains isolated from patients infected in Laos. The amino acid mutations within the spike fragment of the isolates were determined via sequencing. The more efficient replication of the Alpha and Delta isolates was documented than the prototyped SARS-CoV-2 in Calu-3 and Caco-2 ​cells, while such features were not observed in Huh-7, Vero E6 and HPA-3 ​cells. We utilized both animal models of human ACE2 (hACE2) transgenic mice and hamsters to evaluate the pathogenesis of the isolates. The Alpha and Delta can replicate well in multiple organs and cause moderate to severe lung pathology in these animals. In conclusion, the spike protein of the isolated Alpha and Delta variant strains was characterized, and the replication and pathogenicity of the strains in the cells and animal models were also evaluated.

Plasma and memory antibody responses to Gamma SARS-CoV-2 provide limited cross-protection to other variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to be a global problem in part because of the emergence of variants of concern that evade neutralization by antibodies elicited by prior infection or vaccination. Here we report on human neutralizing antibody and memory responses to the Gamma variant in a cohort of hospitalized individuals. Plasma from infected individuals potently neutralized viruses pseudotyped with Gamma SARS-CoV-2 spike protein, but neutralizing activity against Wuhan-Hu-1-1, Beta, Delta, or Omicron was significantly lower. Monoclonal antibodies from memory B cells also neutralized Gamma and Beta pseudoviruses more effectively than Wuhan-Hu-1. 69% and 34% of Gamma-neutralizing antibodies failed to neutralize Delta or Wuhan-Hu-1. Although Class 1 and 2 antibodies dominate the response to Wuhan-Hu-1 or Beta, 54% of antibodies elicited by Gamma infection recognized Class 3 epitopes. The results have implications for variant-specific vaccines and infections, suggesting that exposure to variants generally provides more limited protection to other variants.

Spontaneous Post-COVID-19 Pneumothorax in a Patient with No Prior Respiratory Tract Pathology: A Case Report

Spontaneous pneumothorax in the setting of coronavirus disease 19 (COVID-19) has been first described as an unlikely complication, mainly occurring in critically ill patients or as a consequence of mechanical ventilation. We report a case with COVID-19 pneumonia followed by a spontaneous pneumothorax in a young non-smoker without any predisposing pathology.

SARS-CoV-2 Variant Determination Through SNP Assays in Samples From Industry Workers From Rio de Janeiro, Brazil

Since the first reported case in December 2019, SARS-CoV-2 infections have become a major public health worldwide. Even with the increasing vaccination in several countries and relaxing of social distancing measures, the pandemic remains a threat especially due to the emergence of new SARS-CoV-2 variants. Despite the presence of an enzyme capable of proofreading its genome, high rates of replication provide a source of accumulation of mutations within the viral genome. In this retrospective study, samples from a cohort of industry workers tested by the SESI's COVID-19 mass testing program from September 2020 to May 2021 were analyzed using a mutation panel in order to describe the circulation of currently identified SARS-CoV-2 variants within the samples obtained in Rio de Janeiro State. Our results demonstrated that the variant of interest (VOI) Zeta has been in circulation since October 2020 and reached 87% of prevalence in February 2021 followed by a decrease due to the emergence of Gamma variant of concern (VOC). Gamma was detected in January 2021 in our studied population, and its prevalence increased during the following months, reaching absolute prevalence within positive samples in May. The Alpha variant was detected only in 4-7% of samples during March and April while Beta VOC was not detected in our study. Our data agree with sequencing genomic surveillance databases and highlight the importance of continuous mass testing programs and variant detection in order to control viral spread and guide public health measures.

Meharry Medical College Mobile Vaccination Program: Implications for Increasing COVID-19 Vaccine Uptake among Minority Communities in Middle Tennessee

To end or curtail the COVID-19 pandemic, it is essential to incorporate mobile vaccination programs into the national vaccination strategy. Mobile COVID-19 vaccination programs play an important role in providing comprehensive vaccination from federally qualified institutions to underserved communities facing a higher risk for COVID-19 acquisition. The Meharry Medical College COVID-19 mobile vaccine program (MMC-MVP) has provided lifesaving COVID-19 vaccines, free of charge, to communities throughout Middle Tennessee. Mobile deployment is vital for those forced to travel long distances to get vaccinated and who have limited access to medical providers or vaccine clinics, lack access to public transportation, or may be homebound. The MMC-MVP, established on 13 April 2021, via funding from the Bloomberg Foundation, is sourced with infectious disease experts, nurse practitioners, and community engagement personnel to provide COVID-19 vaccinations and information in a culturally competent manner to diverse communities in Middle Tennessee. To provide broader access to COVID-19 vaccinations and vaccine-related information, the MMC-MVP partnered with the Tennessee Community Engagement Alliance, Vanderbilt University School of Nursing COVID-19 vaccine strike teams, non-academic, community-based organizations, and faith-based organizations. During the September 2021 COVID-19 surge in Tennessee, the MMC-MVP provided nearly 5000 free COVID-19 vaccinations to targeted, underserved communities. The MMC-MVP has provided vaccine equity in communities with the highest risk for acquiring COVID-19 and with greatest need in this pandemic.

Forecasting the Severity of COVID-19 Pandemic Amidst the Emerging SARS-CoV-2 Variants: Adoption of ARIMA Model

Currently, the global report of COVID-19 cases is around 110 million, and more than 2.43 million related death cases as of February 18, 2021. Viruses continuously change through mutation; hence, different virus of SARS-CoV-2 has been reported globally. The United Kingdom (UK), South Africa, Brazil, and Nigeria are the countries from which these emerged variants have been notified and now spreading globally. Therefore, these countries have been selected as a research sample for the present study. The datasets analyzed in this study spanned from March 1, 2020, to January 31, 2021, and were obtained from the World Health Organization website. The study used the Autoregressive Integrated Moving Average (ARIMA) model to forecast coronavirus incidence in the UK, South Africa, Brazil, and Nigeria. ARIMA models with minimum Akaike Information Criterion Correction (AICc) and statistically significant parameters were chosen as the best models in this research. Accordingly, for the new confirmed cases, ARIMA (3,1,14), ARIMA (0,1,11), ARIMA (1,0,10), and ARIMA (1,1,14) models were chosen for the UK, South Africa, Brazil, and Nigeria, respectively. Also, the model specification for the confirmed death cases was ARIMA (3,0,4), ARIMA (0,1,4), ARIMA (1,0,7), and ARIMA (Brown); models were selected for the UK, South Africa, Brazil, and Nigeria, respectively. The results of the ARIMA model forecasting showed that if the required measures are not taken by the respective governments and health practitioners in the days to come, the magnitude of the coronavirus pandemic is expected to increase in the study's selected countries.

An outlook on potential protein targets of COVID-19 as a druggable site

BACKGROUND

SARS-CoV-2 which causes COVID-19 disease has started a pandemic episode all over the world infecting millions of people and has created medical and economic crisis. From December 2019, cases originated from Wuhan city and started spreading at an alarming rate and has claimed millions of lives till now. Scientific studies suggested that this virus showed genomic similarity of about 90% with SARS-CoV and is found to be more contagious as compared to SARS-CoV and MERS-CoV. Since the pandemic, virus has undergone constant mutation and few strains have raised public concern like Delta and Omicron variants of SARS-CoV-2.

OBJECTIVE

This review focuses on the structural features of SARS-CoV-2 proteins and host proteins as well as their mechanism of action. We have also elucidated the repurposed drugs that have shown potency to inhibit these protein targets in combating COVID-19. Moreover, the article discusses the vaccines approved so far and those under clinical trials for their efficacy against COVID-19.

CONCLUSION

Using cryo-electron microscopy or X-ray diffraction, hundreds of crystallographic data of SARS-CoV-2 proteins have been published including structural and non-structural proteins. These proteins have a significant role at different aspects in the viral machinery and presented themselves as potential target for drug designing and therapeutic interventions. Also, there are few host cell proteins which helps in SARS-CoV-2 entry and proteolytic cleavage required for viral infection.

Residual-Shuffle Network with Spatial Pyramid Pooling Module for COVID-19 Screening

Since the start of the COVID-19 pandemic at the end of 2019, more than 170 million patients have been infected with the virus that has resulted in more than 3.8 million deaths all over the world. This disease is easily spreadable from one person to another even with minimal contact, even more for the latest mutations that are more deadly than its predecessor. Hence, COVID-19 needs to be diagnosed as early as possible to minimize the risk of spreading among the community. However, the laboratory results on the approved diagnosis method by the World Health Organization, the reverse transcription-polymerase chain reaction test, takes around a day to be processed, where a longer period is observed in the developing countries. Therefore, a fast screening method that is based on existing facilities should be developed to complement this diagnosis test, so that a suspected patient can be isolated in a quarantine center. In line with this motivation, deep learning techniques were explored to provide an automated COVID-19 screening system based on X-ray imaging. This imaging modality is chosen because of its low-cost procedures that are widely available even in many small clinics. A new convolutional neural network (CNN) model is proposed instead of utilizing pre-trained networks of the existing models. The proposed network, Residual-Shuffle-Net, comprises four stacks of the residual-shuffle unit followed by a spatial pyramid pooling (SPP) unit. The architecture of the residual-shuffle unit follows an hourglass design with reduced convolution filter size in the middle layer, where a shuffle operation is performed right after the split branches have been concatenated back. Shuffle operation forces the network to learn multiple sets of features relationship across various channels instead of a set of global features. The SPP unit, which is placed at the end of the network, allows the model to learn multi-scale features that are crucial to distinguish between the COVID-19 and other types of pneumonia cases. The proposed network is benchmarked with 12 other state-of-the-art CNN models that have been designed and tuned specially for COVID-19 detection. The experimental results show that the Residual-Shuffle-Net produced the best performance in terms of accuracy and specificity metrics with 0.97390 and 0.98695, respectively. The model is also considered as a lightweight model with slightly more than 2 million parameters, which makes it suitable for mobile-based applications. For future work, an attention mechanism can be integrated to target certain regions of interest in the X-ray images that are deemed to be more informative for COVID-19 diagnosis.