Genomic characteristics and clinical effect of the emergent SARS-CoV-2 B.1.1.7 lineage in London, UK: a whole-genome sequencing and hospital-based cohort study

Using Machine Learning to Analyze Molecular Dynamics Simulations of Biomolecules

Machine learning (ML) techniques have become powerful tools in both industrial and academic settings. Their ability to facilitate analysis of complex data and generation of predictive insights is transforming how scientific problems are approached across a wide range of disciplines. In this tutorial, we present a cursory introduction to three widely used ML techniques─logistic regression, random forest, and multilayer perceptron─applied toward analyzing molecular dynamics (MD) trajectory data. We employ our chosen ML models to the study of the SARS-CoV-2 spike protein receptor binding domain interacting with the receptor ACE2. We develop a pipeline for processing MD simulation trajectory data and identifying residues that significantly impact the stability of the complex.

Virus-specific host responses and gene signatures following infection with major SARS-CoV-2 variants of concern: role of ZBP1 in viral clearance and lung inflammation

SARS-CoV-2 can cause severe lung damage due to uncontrolled viral replication or/and excessive inflammation. New variants of concern (VOCs) have raised additional concerns due to disparate pathogenicity and possible enhanced virulence. Herein, using RNA sequencing, we performed a comparative transcriptomic analysis following infection with major VOCs. We evaluated the transcriptional changes induced in the lungs of K18-hACE2 mice following infection with the ancestral B.1 lineage (Wuhan), B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), B.1.1.529 (Omicron) variants or mouse-adapted SARS-CoV-2 (MA10). Our work reveals the molecular basis of pathological hallmarks in the lungs associated with SARS-CoV-2 infection. We report that infection with B.1, pre-Omicron VOCs, and MA10 induce similar molecular fingerprints of excessive lung inflammation and immune activation in K18-hACE2 mice. Analysis of differentially expressed genes revealed both shared and variant-specific responses, with key immune markers such as Cxcl10, Zbp1, Ifit3, Isg15, Rsad2, and Irf7 consistently upregulated across variants. Clustering of highly variable genes across samples revealed two variant groups distinguished by upregulation of antigen presentation and immune-related genes (e.g. Retnla, Saa3, Plac8, Ly6c2, H2-D1, and H2-K1). Delta, Beta, Alpha, and MA10 showed elevated expression, whereas Wuhan and Omicron exhibited attenuated responses. In addition, we show that Z-DNA-binding protein 1 (ZBP1) plays a role in viral clearance in the lungs after SARS-CoV-2 infection. ZBP1 deficiency resulted in reduced expression of cell death-associated markers and virus-induced cell death in the lungs following MA10 infection. Furthermore, the knockout of ZBP1 resulted in an attenuated inflammatory response with reduced production of proinflammatory cytokines and chemokines and decreased macrophage infiltration in the lungs. These results suggest that ZBP1 plays a role in viral clearance and in enhancing the inflammatory response and virus-induced cell death during SARS-CoV-2 infection. Altogether, our study provides insights into the pathogenesis of SARS-CoV-2 infection in mice, facilitating the identification of biomarkers and the development of potential therapeutic targets.

Insights into the implementation of a whole genome sequencing report form (SRF) to reduce nosocomial SARS-CoV-2 in UK hospitals within an unfolding pandemic: A qualitative process evaluation using normalisation process theory

BACKGROUND

Here we report on a process evaluation conducted as part of a large multisite non-randomised trial of the effectiveness of a novel whole genome sequence report form (SRF) to reduce nosocomial SARS-CoV-2 through changing infection prevention and control (IPC) behaviours during the COVID -19 pandemic. We detail how the SRF was implemented across a heterogeneous purposive sub-sample of hospital trial sites (n=5/14).

METHODS

We conducted in-depth interviews from diverse professional staff (N=39). Deductive and inductive thematic analysis initially explored participants' accounts of implementing the SRF. The resulting themes, concerning the way the SRF was used within sites, were then coded in relation to the key tenets of normalisation process theory (NPT).

RESULTS

Factors that enabled the implementation of the SRF included: elements of the context such as health care professional passion; the existence of whole genome sequencing (WGS) infrastructure; effective communication channels, the creation of new connections across professionals and teams; the integration of SRF-led discussions within pre-existing meetings and the ability of a site to achieve a rapid turnaround time. In contrast, we found factors that constrained the use of the SRF included elements of the context such as the impact of the Alpha-variant overwhelming hospitals. In turn, dealing with COVID-19 breached the limited capacity of infection prevention and control (IPC) to respond to the SRF and ensure its routinisation.

CONCLUSION

We show preliminary support for this SRF being an acceptable, useable and potentially scalable way of enhancing existing IPC activities for viral respiratory infections. However, the context of both the trial and the alpha wave of COVID-19 limit confidence in these insights.

CLINICAL TRIAL NUMBER

https://www.isrctn.com/ISRCTN50212645, Registration date 20/05/2020.

Impact of tiered restrictions in December 2020 on COVID-19 hospitalisations in England: a synthetic control study

OBJECTIVES

To evaluate the effectiveness of localised Tier 3 restrictions, implemented in England in December 2020, on reducing COVID-19 hospitalisations compared with less stringent Tier 2 measures and the variations by neighbourhood deprivation and the prevalence of Alpha (B.1.1.7) variant, the primary variant of concern then, to measure hospital services' burden and inequalities across different communities.

DESIGN

Observational study using a synthetic control method, comparing weekly hospitalisation rates in Tier 3 areas to a synthetic control from Tier 2 areas.

SETTING

England between 4 October 2020 and 21 February 2021.

PARTICIPANTS

23 million people under Tier 3 restrictions, compared with a synthetic control group derived from 29 million people under Tier 2 restrictions.

INTERVENTIONS

Tier 3 restrictions in designated areas were implemented from 7 December 2020, imposing stricter limits on gatherings and hospitality than Tier 2, followed by a national lockdown on 6 January 2021.

PRIMARY AND SECONDARY OUTCOME MEASURES

Weekly COVID-19-related hospitalisations for neighbourhoods in England over 11 weeks following the interventions.

RESULTS

Implementing Tier 3 restrictions were associated with a 17% average reduction in hospitalisations compared with Tier 2 areas (95% CI 13% to 21%; 8158 (6286 to 9981) in total). The effects were similar across different levels of neighbourhood deprivation and prevalence of the Alpha variant.

CONCLUSIONS

Regionally targeted Tier 3 restrictions in England had a moderate but significant effect on reducing hospitalisations. The impact did not exacerbate socioeconomic inequalities during the pandemic. Our findings suggest that regionally targeted restrictions can be effective in managing infectious diseases.

Vaccination status and disease severity of COVID-19 in different phases of the pandemic

This study aimed to explore the clinical profile and the impact of vaccination status on various health outcomes among COVID-19 patients diagnosed in different phases of the pandemic, during which several variants of concern (VOCs) circulated in South Carolina (SC). The current study included 861,526 adult COVID-19 patients diagnosed between January 2021 and April 2022. We extracted their information about demographic characteristics, vaccination, and clinical outcomes from a statewide electronic health record database. Multiple logistic regression models were used to compare clinical outcomes by vaccination status in different pandemic phases, accounting for key covariates (e.g. historical comorbidities). A reduction in mortality was observed among COVID-19 patients during the whole study period, although there were fluctuations during the Delta and Omicron dominant periods. Compared to non-vaccinated patients, full-vaccinated COVID-19 patients had lower mortality in all dominant variants, including Pre-alpha (adjusted odds ratio [aOR]: 0.33; 95%CI: 0.15-0.72), Alpha (aOR: 0.58; 95%CI: 0.42-0.82), Delta (aOR: 0.28; 95%CI: 0.25-0.31), and Omicron (aOR: 0.29; 95%CI: 0.26-0.33) phases. Regarding hospitalization, full-vaccinated parties showed lower risk of hospitalization than non-vaccinated patients in Delta (aOR: 0.44; 95%CI: 0.41-0.47) and Omicron (aOR: 0.53; 95%CI: 0.50-0.57) dominant periods. The findings demonstrated the protection effect of the COVID-19 vaccines against all VOCs, although some of the full-vaccinated population still have symptoms to varying degrees from COVID-19 disease at different phases of the pandemic.

Strategies Used by SARS-CoV-2 to Evade the Innate Immune System in an Evolutionary Perspective

By the end of 2019, the COVID-19 pandemic, resulting from the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), had diffused widely across the globe, with 770 million infected individuals and over 7 million deaths reported. In addition to its high infectivity and pathogenicity and its rapid mutation rate, the unique capacity of SARS-CoV-2 to circumvent the immune system has also contributed to the widespread nature of this pandemic. SARS-CoV-2 elicits the onset of innate immune system activation and initiates antiviral responses once it has infected the host. While battling the host's immune responses, SARS-CoV-2 has established many countermeasures to evade attack and clearance. As the exploration of SARS-CoV-2 continues, substantial evidence has revealed that the 29 proteins synthesized by the SARS-CoV-2 genome are integral to the viral infection process. They not only facilitate viral replication and transmission, but also assist SARS-CoV-2 in escaping the host's immune defenses, positioning them as promising therapeutic targets that have attracted considerable attention in recent studies. This review summarizes the manner in which SARS-CoV-2 interfaces with the innate immune system, with a particular focus on the continuous evolution of SARS-CoV-2 and the implications of mutations.

Current Updates on Variants of SARS-CoV- 2: Systematic Review

Background

Coronavirus disease 2019 is caused by the severe acute respiratory syndrome coronavirus 2, which has become a pandemic. Severe acute respiratory syndrome coronavirus 2 is an enveloped, unsegmented, positive-sense, single-stranded RNA virus that belongs to the family Coronaviridae.

Aim

The objective of this review is to conduct a qualitative analysis of the current updates on epidemiology, evolution, and vaccine variants for SARS-CoV-2.

Method

The search strategy was done from the database based on the PRISMA criteria for qualitative analysis of this review. Literature on variants of severe acute respiratory syndrome coronavirus 2, published in English in the last 5 years (2019-2023), were included. From 179 a total of 105 articles were reviewed, searched, and retrieved from the electronic databases PubMed. The search was done using keywords like COVID-19, SARS-CoV-2, variants, mutations, and vaccines, and articles were managed using EndNote X8 software. The scope of view for this review was the course of the pandemic by emerging variants and how man is struggling to overcome this sudden pandemic through vaccines. The narrative skeleton was constructed based on the article's scope of view.

Result

From the parent severe acute respiratory syndrome coronavirus 2, many variants emerged during the course of this pandemic. They are mainly categorized into two variants: variants of interest and variants of concern based on the impact on public health. The World Health Organization leveled five variants: Alpha (strain B.1.1.7), Beta (strain B.1.351), Gamma (strain P.1), Delta (strain B.1.617.2), and Omicron (B.1.1.529).

Conclusions

It is crucial to stay informed about the latest developments in the understanding of SARS-CoV-2 variants, as new variants can emerge and impact the course of the pandemic. Health authorities and researchers continuously have to monitor and study these variants to assess their characteristics, transmissibility, severity, and the effectiveness of vaccines against them. One has to always refer to the latest information from reputable health journals or organizations for the most up-to-date and accurate details on COVID-19 variants.

Shift in Demographic Involvement and Clinical Characteristics of COVID-19 From Wild-Type SARS-CoV-2 to the Delta Variant in the Indian Population: In Silico Analysis

BACKGROUND

The Delta variant (B.1.617.2) was considered the most dangerous SARS-CoV-2 strain; however, in-depth studies on its impact based on demographic and clinical characteristics of COVID-19 are scarce.

OBJECTIVE

We aimed to investigate the shift in demographic and clinical characteristics of the COVID-19 pandemic with the emergence of the SARS-CoV-2 Delta variant compared with the wild-type (WT) strain (B.1).

METHODS

A cross-sectional study of COVID-19 cases in the Indian population caused by the WT strain (B.1) and Delta variant of SARS-CoV-2 was performed. The viral genomic sequence metadata containing demographic, vaccination, and patient status details (N=9500, NDelta=6238, NWT=3262) were statistically analyzed.

RESULTS

With the Delta variant, in comparison with the WT strain, a higher proportion of young individuals (<20 years) were infected (0-9 years: Delta: 281/6238, 4.5% vs B.1: 75/3262, 2.3%; 10-19 years: Delta: 562/6238, 9% vs B.1: 229/3262, 7%; P<.001). The proportion of women contracting infection increased (Delta: 2557/6238, 41% vs B.1: 1174/3262, 36%; P<.001). However, it decreased for men (Delta: 3681/6238, 59% vs B.1: 2088/3262, 64%; P<.001). An increased proportion of the young population developed symptomatic illness and were hospitalized (Delta: 27/262, 10.3% vs B.1: 5/130, 3.8%; P=.02). Moreover, an increased proportion of the women (albeit not men) from the young (Delta: 37/262, 14.1% vs B.1: 4/130, 3.1%; P<.001) and adult (Delta: 197/262, 75.2% vs B.1: 72/130, 55.4%; P<.001) groups developed symptomatic illness and were hospitalized. The mean age of men and women who contracted infection (Delta: men=37.9, SD 17.2 years; women=36.6, SD 17.6 years; P<.001; B.1: men=39.6, SD 16.9 years; women=40.1, SD 17.4 years; P<.001) as well as developing symptoms or being hospitalized (Delta: men=39.6, SD 17.4 years; women=35.6, SD 16.9 years, P<.001; B.1: men=47, SD 18 years; women=49.5, SD 20.9 years, P<.001) were considerably lower with the Delta variant than the B.1 strain. The total mortality was about 1.8 times higher with the Delta variant than with the WT strain. With the Delta variant, compared with B.1, mortality decreased for men (Delta: 58/85, 68% vs B.1: 15/20, 75%; P<.001); in contrast, it increased for women (Delta: 27/85, 32% vs B.1: 5/20, 25%; P<.001). The odds of death increased with age, irrespective of sex (odds ratio 3.034, 95% CI 1.7-5.2, P<.001). Frequent postvaccination infections (24/6238) occurred with the Delta variant following complete doses.

CONCLUSIONS

The increased involvement of young people and women, the lower mean age for illness, higher mortality, and frequent postvaccination infections were significant epidemiological concerns with the Delta variant.

SARS-CoV-2 viral load is linked to remdesivir efficacy in severe Covid-19 admitted to intensive care

Remdesivir therapy has been declared as efficient in the early stages of Covid-19. Of the 339 patients (males 55.8%, age 71(59;77) years) with a detectable viral load, 140 were treated with remdesivir (of those 103 in the ICU and 57 immunosuppressed) and retrospectively compared with 199 patients (of those 82 in the ICU and 28 immunosuppressed) who were denied therapy due to advanced Covid-19. The viral load was estimated by detecting nucleocapsid antigen in serum (n = 155, median 217(28;1524)pg/ml), antigen in sputum (n = 18, COI 18(4.6;32)), nasopharyngeal antigen (n = 44, COI 17(8;35)) and the real-time PCR (n = 122, Ct 21(18;27)). After adjustment for confounders, patients on remdesivir had better 12-month survival (HR 0.66 (0.44;0.98), p = 0.039), particularly when admitted to the ICU (HR 0.49 (0.29;0.81), p = 0.006). For the immunocompromised patients, the difference did not reach statistical significance (HR 0.55 (0.18;1.69), p = 0.3). The other most significant confounders were age, ICU admission, mechanical ventilation, leukocyte/lymphocyte ratio, admission creatinine and immunosuppression. The impact of monoclonal antibodies or previous vaccinations was not significant. Despite frequent immune suppression including haemato-oncology diseases, lymphopenia, and higher inflammatory markers in the remdesivir group, the results support remdesivir administration with respect to widely available estimates of viral load in patients with high illness severity.

Inference of epidemic dynamics in the COVID-19 era and beyond

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

A micro-disc-based multiplex method for monitoring emerging SARS-CoV-2 variants using the molecular diagnostic tool Intelli-OVI

BACKGROUND

Highly transmissible viruses including SARS-CoV-2 frequently accumulate novel mutations that are detected via high-throughput sequencing. However, there is a need to develop an alternative rapid and non-expensive approach. Here we developed a novel multiplex DNA detection method Intelli-OVI for analysing existing and novel mutations of SARS-CoV-2.

METHODS

We have developed Intelli-OVI that includes the micro-disc-based method IntelliPlex and computational algorithms of objective variant identification (OVI). More than 250 SARS-CoV-2 positive samples including wastewater ones were analysed to verify the efficiency of the method.

RESULTS

IntelliPlex uses micro-discs printed with a unique pictorial pattern as a labelling conjugate for DNA probes, and OVI allows simultaneous identification of several variants using multidimensional data obtained by the IntelliPlex method. Importantly, de novo mutations can be identified by decreased signals, which indicates that there is an emergence of de novo variant virus as well as prompts the need to design additional primers and probes. We have upgraded probe panel according to the emergence of new variants and demonstrated that Intelli-OVI efficiently identified more than 20 different SARS-CoV-2 variants by using 35 different probes simultaneously.

CONCLUSIONS

Intelli-OVI can be upgraded to keep up with rapidly evolving viruses as we showed in this study using SARS-CoV-2 as an example and may be suitable for other viruses but would need to be validated.

Use of a risk assessment tool to determine the origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

The origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is contentious. Most studies have focused on a zoonotic origin, but definitive evidence such as an intermediary animal host is lacking. We used an established risk analysis tool for differentiating natural and unnatural epidemics, the modified Grunow-Finke assessment tool (mGFT) to study the origin of SARS-COV-2. The mGFT scores 11 criteria to provide a likelihood of natural or unnatural origin. Using published literature and publicly available sources of information, we applied the mGFT to the origin of SARS-CoV-2. The mGFT scored 41/60 points (68%), with high inter-rater reliability (100%), indicating a greater likelihood of an unnatural than natural origin of SARS-CoV-2. This risk assessment cannot prove the origin of SARS-CoV-2 but shows that the possibility of a laboratory origin cannot be easily dismissed.

A randomised controlled trial of plasma exchange compared to standard of care in the treatment of severe COVID-19 infection (COVIPLEX)

COVID-19 disease is associated with a hyperinflammatory, pro-thrombotic state and a high mortality. Our primary objective was to assess the change in inflammatory and thrombotic markers associated with PEX, and secondary objectives were to assess the effects of PEX on progression of respiratory failure and incidence of acute thrombotic events. We conducted a prospective, phase II, non-blinded randomised control trial of plasma exchange compared to standard of care in critically ill adults with severe COVID-19 associated respiratory failure, requiring supplemental oxygen or ventilatory support and elevated thrombo-inflammatory markers (LDH, CRP, ferritin, and D-Dimer). Patients randomised to receive PEX were treated with a daily single volume plasma exchange for a minimum of five days. Twenty-two patients were randomised of who 11 received PEX. Demographic and clinical characteristics were similar between groups at presentation. PEX was associated with a significant reduction in pro-thrombotic markers FVIII, VWF and VWF Ag: ADAMTS 13 ratio (p < 0.001). There were no differences in the reduction of inflammatory markers, severity of respiratory failure (p = 0.7), thrombotic events (p = 0.67), or mortality (p > 0.99) at 28 days. PEX successfully reduced pro-thrombotic markers, although was not associated with reduction in inflammatory markers, respiratory failure, or thrombotic events.Trial registration: (NCT04623255); first posted on 10/11/2020.

Uncovering strain- and age-dependent innate immune responses to SARS-CoV-2 infection in air-liquid-interface cultured nasal epithelia

Continuous assessment of the impact of SARS-CoV-2 on the host at the cell-type level is crucial for understanding key mechanisms involved in host defense responses to viral infection. We investigated host response to ancestral-strain and Alpha-variant SARS-CoV-2 infections within air-liquid-interface human nasal epithelial cells from younger adults (26-32 Y) and older children (12-14 Y) using single-cell RNA-sequencing. Ciliated and secretory-ciliated cells formed the majority of highly infected cell-types, with the latter derived from ciliated lineages. Strong innate immune responses were observed across lowly infected and uninfected bystander cells and heightened in Alpha-infection. Alpha highly infected cells showed increased expression of protein-refolding genes compared with ancestral-strain-infected cells in children. Furthermore, oxidative phosphorylation-related genes were down-regulated in bystander cells versus infected and mock-control cells, underscoring the importance of these biological functions for viral replication. Overall, this study highlights the complexity of cell-type-, age- and viral strain-dependent host epithelial responses to SARS-CoV-2.

Differential responses of SARS-CoV-2 variants to environmental drivers during their selective sweeps

Previous work has shown that environmental variables affect SARS-CoV-2 transmission, but it is unclear whether different strains show similar environmental responses. Here we leverage genetic data on the transmission of three (Alpha, Delta and Omicron BA.1) variants of SARS-CoV-2 throughout England, to unpick the roles that climate and public-health interventions play in the circulation of this virus. We find evidence for enhanced transmission of the virus in colder conditions in the first variant selective sweep (of Alpha, in winter), but limited evidence of an impact of climate in either the second (of Delta, in the summer, when vaccines were prevalent) or third sweep (of Omicron, in the winter, during a successful booster-vaccination campaign). We argue that the results for Alpha are to be expected if the impact of climate is non-linear: we find evidence of an asymptotic impact of temperature on the alpha variant transmission rate. That is, at lower temperatures, the influence of temperature on transmission is much higher than at warmer temperatures. As with the initial spread of SARS-CoV-2, however, the overwhelming majority of variation in disease transmission is explained by the intrinsic biology of the virus and public-health mitigation measures. Specifically, when vaccination rates are high, a major driver of the spread of a new variant is it's ability to evade immunity, and any climate effects are secondary (as evidenced for Delta and Omicron). Climate alone cannot describe the transmission dynamics of emerging SARS-CoV-2 variants.

Genomic surveillance of SARS-CoV-2 evolution by a centralised pipeline and weekly focused sequencing, Austria, January 2021 to March 2023

BackgroundThe COVID-19 pandemic was largely driven by genetic mutations of SARS-CoV-2, leading in some instances to enhanced infectiousness of the virus or its capacity to evade the host immune system. To closely monitor SARS-CoV-2 evolution and resulting variants at genomic-level, an innovative pipeline termed SARSeq was developed in Austria.AimWe discuss technical aspects of the SARSeq pipeline, describe its performance and present noteworthy results it enabled during the pandemic in Austria.MethodsThe SARSeq pipeline was set up as a collaboration between private and public clinical diagnostic laboratories, a public health agency, and an academic institution. Representative SARS-CoV-2 positive specimens from each of the nine Austrian provinces were obtained from SARS-CoV-2 testing laboratories and processed centrally in an academic setting for S-gene sequencing and analysis.ResultsSARS-CoV-2 sequences from up to 2,880 cases weekly resulted in 222,784 characterised case samples in January 2021-March 2023. Consequently, Austria delivered the fourth densest genomic surveillance worldwide in a very resource-efficient manner. While most SARS-CoV-2 variants during the study showed comparable kinetic behaviour in all of Austria, some, like Beta, had a more focused spread. This highlighted multifaceted aspects of local population-level acquired immunity. The nationwide surveillance system enabled reliable nowcasting. Measured early growth kinetics of variants were predictive of later incidence peaks.ConclusionWith low automation, labour, and cost requirements, SARSeq is adaptable to monitor other pathogens and advantageous even for resource-limited countries. This multiplexed genomic surveillance system has potential as a rapid response tool for future emerging threats.

Central Countries' and Brazil's Contributions to Nanotechnology

Abstract:

Nanotechnology is a cornerstone of the scientific advances witnessed over the past few years. Nanotechnology applications are extensively broad, and an overview of the main trends worldwide can give an insight into the most researched areas and gaps to be covered. This document presents an overview of the trend topics of the three leading countries studying in this area, as well as Brazil for comparison. The data mining was made from the Scopus database and analyzed using the VOSviewer and Voyant Tools software. More than 44.000 indexed articles published from 2010 to 2020 revealed that the countries responsible for the highest number of published articles are The United States, China, and India, while Brazil is in the fifteenth position. Thematic global networks revealed that the standing-out research topics are health science, energy, wastewater treatment, and electronics. In a temporal observation, the primary topics of research are: India (2020), which was devoted to facing SARS-COV 2; Brazil (2019), which is developing promising strategies to combat cancer; China (2018), whit research on nanomedicine and triboelectric nanogenerators; the United States (2017) and the Global tendencies (2018) are also related to the development of triboelectric nanogenerators. The collected data are available on GitHub. This study demonstrates the innovative use of data-mining technologies to gain a comprehensive understanding of nanotechnology's contributions and trends and highlights the diverse priorities of nations in this cutting-edge field.

Infection kinetics, syncytia formation, and inflammatory biomarkers as predictive indicators for the pathogenicity of SARS-CoV-2 Variants of Concern in Calu-3 cells

The ongoing COVID-19 pandemic has led to the emergence of new SARS-CoV-2 variants as a result of continued host-virus interaction and viral genome mutations. These variants have been associated with varying levels of transmissibility and disease severity. We investigated the phenotypic profiles of six SARS-CoV-2 variants (WT, D614G, Alpha, Beta, Delta, and Omicron) in Calu-3 cells, a human lung epithelial cell line. In our model demonstrated that all variants, except for Omicron, had higher efficiency in virus entry compared to the wild-type. The Delta variant had the greatest phenotypic advantage in terms of early infection kinetics and marked syncytia formation, which could facilitate cell-to-cell spreading, while the Omicron variant displayed slower replication and fewer syncytia formation. We also identified the Delta variant as the strongest inducer of inflammatory biomarkers, including pro-inflammatory cytokines/chemokines (IP-10/CXCL10, TNF-α, and IL-6), anti-inflammatory cytokine (IL-1RA), and growth factors (FGF-2 and VEGF-A), while these inflammatory mediators were not significantly elevated with Omicron infection. These findings are consistent with the observations that there was a generally more pronounced inflammatory response and angiogenesis activity within the lungs of COVID-19 patients as well as more severe symptoms and higher mortality rate during the Delta wave, as compared to less severe symptoms and lower mortality observed during the current Omicron wave in Thailand. Our findings suggest that early infectivity kinetics, enhanced syncytia formation, and specific inflammatory mediator production may serve as predictive indicators for the virulence potential of future SARS-CoV-2 variants.

COVID-19: From emerging variants to vaccination

The vigorous spread of SARS-CoV-2 resulted in the rapid infection of millions of people worldwide and devastation of not only public healthcare, but also social, educational, and economic infrastructures. The evolution of SARS-CoV-2 over time is due to the mutations that occurred in the genome during each replication. These mutated forms of SARS-CoV-2, otherwise known as variants, were categorized as variants of interest (VOI) or variants of concern (VOC) based on the increased risk of transmissibility, disease severity, immune escape, decreased effectiveness of current social measures, and available vaccines and therapeutics. The swift development of COVID-19 vaccines has been a great success for biomedical research, and billions of vaccine doses, including boosters, have been administered worldwide. BNT162b2 vaccine (Pfizer-BioNTech), mRNA-1273 (Moderna), ChAdOx1 nCoV-19 (AstraZeneca), and Janssen (Johnson & Johnson) are the four major COVID-19 vaccines that received early regulatory authorization based on their efficacy. However, some SARS-CoV-2 variants resulted in higher resistance to available vaccines or treatments. It has been four years since the first reported infection of SARS-CoV-2, yet the Omicron variant and its subvariants are still infecting people worldwide. Despite this, COVID-19 vaccines are still expected to be effective at preventing severe disease, hospitalization, and death from COVID. In this review, we provide a comprehensive overview of the COVID-19 pandemic focused on evolution of VOC and vaccination strategies against them.

Comparative effectiveness of dexamethasone in treatment of hospitalized COVID-19 patients in the United States during the first year of the pandemic: Findings from the National COVID Cohort Collaborative (N3C) data repository

BACKGROUND

Dexamethasone was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial, but evidence is still needed to support its real-world effectiveness in heterogeneous populations of patients with a wide range of comorbidities.

METHODS

COVID-19 inpatients represented within the National COVID Cohort Collaborative (N3C) Data Enclave, prior to vaccine availability, were studied. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome defined by use of ECMO or mechanical ventilation. Missing data were imputed with single imputation. Dexamethasone-treated patients were propensity score (PS) matched to non-dexamethasone-treated controls, stratified by remdesivir treatment and based on demographics, baseline laboratory values, comorbidities, and amount of missing data before imputation. Treatment benefit was quantified using logistic regression. Further sensitivity analyses were performed using clinical adjusters in matched groups and in strata defined by quartiles of PS.

RESULTS

Dexamethasone treatment was associated with reduced risk of in-hospital mortality for n = 1,263 treated, matched 1:3 to untreated, patients not receiving remdesivir (OR = 0.77, 95% CI: 0.62 to 0.95, p = 0.017), and for n = 804 treated, matched 1:1 to untreated, patients receiving remdesivir (OR = 0.74, 95% CI: 0.53 to 1.02, p = 0.054). Treatment showed secondary outcome benefit. In sensitivity analyses, treatment effect generally remained similar with some heterogeneity of benefit across quartiles of PS, possibly reflecting concentration of benefit among the more severely affected.

CONCLUSIONS

We add evidence that dexamethasone provides benefit with respect to mortality and severe outcomes in a diverse, national hospitalized sample, prior to vaccine availability.

Targeted amplification and genetic sequencing of the severe acute respiratory syndrome coronavirus 2 surface glycoprotein

IMPORTANCE

The COVID-19 pandemic was accompanied by an unprecedented surveillance effort. The resulting data were and will continue to be critical for surveillance and control of SARS-CoV-2. However, some genomic surveillance methods experienced challenges as the virus evolved, resulting in incomplete and poor quality data. Complete and quality coverage, especially of the S-gene, is important for supporting the selection of vaccine candidates. As such, we developed a robust method to target the S-gene for amplification and sequencing. By focusing on the S-gene and imposing strict coverage and quality metrics, we hope to increase the quality of surveillance data for this continually evolving gene. Our technique is currently being deployed globally to partner laboratories, and public health representatives from 79 countries have received hands-on training and support. Expanding access to quality surveillance methods will undoubtedly lead to earlier detection of novel variants and better inform vaccine strain selection.

Distinct lung cell signatures define the temporal evolution of diffuse alveolar damage in fatal COVID-19

BACKGROUND

Lung damage in severe COVID-19 is highly heterogeneous however studies with dedicated spatial distinction of discrete temporal phases of diffuse alveolar damage (DAD) and alternate lung injury patterns are lacking. Existing studies have also not accounted for progressive airspace obliteration in cellularity estimates. We used an imaging mass cytometry (IMC) analysis with an airspace correction step to more accurately identify the cellular immune response that underpins the heterogeneity of severe COVID-19 lung disease.

METHODS

Lung tissue was obtained at post-mortem from severe COVID-19 deaths. Pathologist-selected regions of interest (ROIs) were chosen by light microscopy representing the patho-evolutionary spectrum of DAD and alternate disease phenotypes were selected for comparison. Architecturally normal SARS-CoV-2-positive lung tissue and tissue from SARS-CoV-2-negative donors served as controls. ROIs were stained for 40 cellular protein markers and ablated using IMC before segmented cells were classified. Cell populations corrected by ROI airspace and their spatial relationships were compared across lung injury patterns.

FINDINGS

Forty patients (32M:8F, age: 22-98), 345 ROIs and >900k single cells were analysed. DAD progression was marked by airspace obliteration and significant increases in mononuclear phagocytes (MnPs), T and B lymphocytes and significant decreases in alveolar epithelial and endothelial cells. Neutrophil populations proved stable overall although several interferon-responding subsets demonstrated expansion. Spatial analysis revealed immune cell interactions occur prior to microscopically appreciable tissue injury.

INTERPRETATION

The immunopathogenesis of severe DAD in COVID-19 lung disease is characterised by sustained increases in MnPs and lymphocytes with key interactions occurring even prior to lung injury is established.

FUNDING

UK Research and Innovation/Medical Research Council through the UK Coronavirus Immunology Consortium, Barbour Foundation, General Sir John Monash Foundation, Newcastle University, JGW Patterson Foundation, Wellcome Trust.

Intranasal mask for protecting the respiratory tract against viral aerosols

The spread of many infectious diseases relies on aerosol transmission to the respiratory tract. Here we design an intranasal mask comprising a positively-charged thermosensitive hydrogel and cell-derived micro-sized vesicles with a specific viral receptor. We show that the positively charged hydrogel intercepts negatively charged viral aerosols, while the viral receptor on vesicles mediates the entrapment of viruses for inactivation. We demonstrate that when displaying matched viral receptors, the intranasal masks protect the nasal cavity and lung of mice from either severe acute respiratory syndrome coronavirus 2 or influenza A virus. With computerized tomography images of human nasal cavity, we further conduct computational fluid dynamics simulation and three-dimensional printing of an anatomically accurate human nasal cavity, which is connected to human lung organoids to generate a human respiratory tract model. Both simulative and experimental results support the suitability of intranasal masks in humans, as the likelihood of viral respiratory infections induced by different variant strains is dramatically reduced.

Epidemiological and clinical features of COVID-19 inpatients in Changsha, China: A retrospective study from 2020 to 2022

Objectives

The spread of SARS-Cov-2 remains a global concern along with the emergence of variants. This study aims to characterize the epidemiological and clinical features of hospitalized patients who were dragonized with five different variants of SARS-CoV-2 during the past 3 years.

Methods

This retrospective study recruited 432 COVID-19 patients who were hospitalized in the First Hospital of Changsha from January 2020 to August 2022. Clinical records on clinical symptoms, laboratory profiles, and chest CT images was collected. The epidemiological and clinical features were compared between COVID-19 patients infected with either the wild-type, Omicron variant or pre- Omicron variants (e.g., Alpha, Beta, Delta).

Results

A total of 432 laboratory-confirmed COVID-19 inpatients were dialogized during three waves, including 247 cases during the wild-type transmission period, 65 cases during the transmission period of pre-Omicron variants, and 119 cases during the transmission period of Omicron variants. The proportion of moderately or severely ill inpatients showed a gradual decline from the wild-type transmission period to the Omicron transmission period. The common symptoms of inpatients infected with SARS-CoV-2 wildtype strains included fever (67.61 %), cough (57.89 %), fatigue (33.60 %), and shortness of breath (12.15 %). In contrast, patients infected with other variants mostly showed upper respiratory symptoms. Based on chest CT images, a lower degree of acute pulmonary infection was observed among inpatients infected with the Omicron variants than those infected with the wild-type strain (31.09 % vs 93.12 %, p-value<0.01).

Conclusions

Compared with the wild-type strain, SARS-CoV-2 variants of concern, especially the Omicron variant, mostly caused a lower degree of acute pulmonary infection, indicating the reduced disease severity and mortality among hospitalized COVID-19 patients.

Evolutionary trajectory of SARS-CoV-2 genome shifts during widespread vaccination and emergence of Omicron variant

Understanding the adaptation of SARS-CoV-2 is critical for the development of effective treatments against this exceptionally successful human pathogen. To predict the emergence of new variants that may escape host immunity or increase virulence, it is important to characterize the biological forces driving its evolution. We conducted a comprehensive population genetic study of over thirteen million SARS-CoV-2 genome sequences, collected over a timeframe of ~3 years, to investigate these forces. Our analysis revealed that during the first year of the pandemic (2020 to 2021), the SARS-CoV-2 genome was subject to strong conservation, with only 3.6% of sites under diversifying pressure in the receptor binding domain (RBD) of the Spike protein. However, we observed a sharp increase in the diversification of the RBD during 2021 (8.1% of sites under diversifying pressure up to 2022), indicating selective pressures that promote the accumulation of mutations. This period coincided with broad viral infection and adoption of vaccination worldwide, and we observed the acquisition of mutations that later defined the Omicron lineages in independent SARS-CoV-2 strains, suggesting that diversifying selection at these sites could have led to their fixation in Omicron lineages by convergent evolution. Since the emergence of Omicron, we observed a further decrease in the conservation of structural genes, including M, N, and the spike proteins (13.1% of RBD sites under diversifying pressure up to 2023), and identified new sites defining future potential emerging strains. Our results exhibit that ongoing rapid antigenic evolution continues to produce new high-frequency functional variants. Sites under selection are critical for virus fitness, and currently known T cell epitope sequences are highly conserved. Altogether, our study provides a comprehensive dynamic map of sites under selection and conservation across the entirety of the SARS-CoV-2 genome.

Bioinformatic investigation of discordant sequence data for SARS-CoV-2: insights for robust genomic analysis during pandemic surveillance

The COVID-19 pandemic has necessitated the rapid development and implementation of whole-genome sequencing (WGS) and bioinformatic methods for managing the pandemic. However, variability in methods and capabilities between laboratories has posed challenges in ensuring data accuracy. A national working group comprising 18 laboratory scientists and bioinformaticians from Australia and New Zealand was formed to improve data concordance across public health laboratories (PHLs). One effort, presented in this study, sought to understand the impact of the methodology on consensus genome concordance and interpretation. SARS-CoV-2 WGS proficiency testing programme (PTP) data were retrospectively obtained from the 2021 Royal College of Pathologists of Australasia Quality Assurance Programmes (RCPAQAP), which included 11 participating Australian laboratories. The submitted consensus genomes and reads from eight contrived specimens were investigated, focusing on discordant sequence data and findings were presented to the working group to inform best practices. Despite using a variety of laboratory and bioinformatic methods for SARS-CoV-2 WGS, participants largely produced concordant genomes. Two participants returned five discordant sites in a high-Cτ replicate, which could be resolved with reasonable bioinformatic quality thresholds. We noted ten discrepancies in genome assessment that arose from nucleotide heterogeneity at three different sites in three cell-culture-derived control specimens. While these sites were ultimately accurate after considering the participants' bioinformatic parameters, it presented an interesting challenge for developing standards to account for intrahost single nucleotide variation (iSNV). Observed differences had little to no impact on key surveillance metrics, lineage assignment and phylogenetic clustering, while genome coverage <90 % affected both. We recommend PHLs bioinformatically generate two consensus genomes with and without ambiguity thresholds for quality control and downstream analysis, respectively, and adhere to a minimum 90 % genome coverage threshold for inclusion in surveillance interpretations. We also suggest additional PTP assessment criteria, including primer efficiency, detection of iSNVs and minimum genome coverage of 90 %. This study underscores the importance of multidisciplinary national working groups in informing guidelines in real time for bioinformatic quality acceptance criteria. It demonstrates the potential for enhancing public health responses through improved data concordance and quality control in SARS-CoV-2 genomic analysis during pandemic surveillance.

Long-term SARS-CoV-2 surveillance in wastewater and estimation of COVID-19 cases: An application of wastewater-based epidemiology

The role of wastewater-based epidemiology (WBE), a powerful tool to complement clinical surveillance, has increased as many grassroots-level facilities, such as municipalities and cities, are actively involved in wastewater monitoring, and the clinical testing of coronavirus disease 2019 (COVID-19) is downscaled widely. This study aimed to conduct long-term wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Yamanashi Prefecture, Japan, using one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assay and estimate COVID-19 cases using a cubic regression model that is simple to implement. Influent wastewater samples (n = 132) from a wastewater treatment plant were collected normally once weekly between September 2020 and January 2022 and twice weekly between February and August 2022. Viruses in wastewater samples (40 mL) were concentrated by the polyethylene glycol precipitation method, followed by RNA extraction and RT-qPCR. The K-6-fold cross-validation method was used to select the appropriate data type (SARS-CoV-2 RNA concentration and COVID-19 cases) suitable for the final model run. SARS-CoV-2 RNA was successfully detected in 67 % (88 of 132) of the samples tested during the whole surveillance period, 37 % (24 of 65) and 96 % (64 of 67) of the samples collected before and during 2022, respectively, with concentrations ranging from 3.5 to 6.3 log10 copies/L. This study applied a nonnormalized SARS-CoV-2 RNA concentration and nonstandardized data for running the final 14-day (1 to 14 days) offset models to estimate weekly average COVID-19 cases. Comparing the parameters used for a model evaluation, the best model showed that COVID-19 cases lagged 3 days behind the SARS-CoV-2 RNA concentration in wastewater samples during the Omicron variant phase (year 2022). Finally, 3- and 7-day offset models successfully predicted the trend of COVID-19 cases from September 2022 until February 2023, indicating the applicability of WBE as an early warning tool.

Differential Transcriptomic Landscapes of SARS-CoV-2 Variants in Multiple Organs from Infected Rhesus Macaques

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the persistent coronavirus disease 2019 (COVID-19) pandemic, which has resulted in millions of deaths worldwide and brought an enormous public health and global economic burden. The recurring global wave of infections has been exacerbated by growing variants of SARS-CoV-2. In this study, the virological characteristics of the original SARS-CoV-2 strain and its variants of concern (VOCs; including Alpha, Beta, and Delta) in vitro, as well as differential transcriptomic landscapes in multiple organs (lung, right ventricle, blood, cerebral cortex, and cerebellum) from the infected rhesus macaques, were elucidated. The original strain of SARS-CoV-2 caused a stronger innate immune response in host cells, and its VOCs markedly increased the levels of subgenomic RNAs, such as N, Orf9b, Orf6, and Orf7ab, which are known as the innate immune antagonists and the inhibitors of antiviral factors. Intriguingly, the original SARS-CoV-2 strain and Alpha variant induced larger alteration of RNA abundance in tissues of rhesus monkeys than Beta and Delta variants did. Moreover, a hyperinflammatory state and active immune response were shown in the right ventricles of rhesus monkeys by the up-regulation of inflammation- and immune-related RNAs. Furthermore, peripheral blood may mediate signaling transmission among tissues to coordinate the molecular changes in the infected individuals. Collectively, these data provide insights into the pathogenesis of COVID-19 at the early stage of infection by the original SARS-CoV-2 strain and its VOCs.

Molecular insights into the SARS-CoV-2 Omicron variant from Bangladesh suggest diverse and continuous evolution

The study analyzed the molecular dynamics of the circulating SARS-CoV-2 Omicron variant from its identification in November 2021 to January 2023. The SARS-CoV-2 sequences from Bangladesh revealed three distinct waves of the Omicron variant. More than 50 sub-lineages of Omicron variant were introduced into the country, with the majority belonging to the major lineages of BA.1-like (24.91%), BA.2-like (43.35%), BA.5-like (5.76%), XBB (10.47%), and "Others and Unassigned" (18.64%). Furthermore, the relative frequencies over time revealed that Omicron lineages existed for a short period of time before being replaced by other sub-lineages. Many potential mutations were found in the receptor binding domain of the Spike protein including G339D/H, S371 L/F, K417 N, T478K, E484A, Q493R, Q498R, and N501Y. In conclusion, the SARS-CoV-2 Omicron variant from Bangladesh showed diverse genetic features and continuous evolution. Therefore, the choice of vaccine and monitoring of hospitalized patients is important alongside genetic characterization of the circulating SARS-CoV-2.

Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater

Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (<5% TPR), moderate (5%-8% TPR), substantial (8%-10% TPR), and high (>10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. IMPORTANCE We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.

Discriminative Identification of SARS-CoV-2 Variants Based on Mass-Spectrometry Analysis

The spread of SARS-CoV-2 variants of concern (VOCs) is of great importance since genetic changes may increase transmissibility, disease severity and reduce vaccine effectiveness. Moreover, these changes may lead to failure of diagnostic measures. Therefore, variant-specific diagnostic methods are essential. To date, genetic sequencing is the gold-standard method to discriminate between variants. However, it is time-consuming (taking several days) and expensive. Therefore, the development of rapid diagnostic methods for SARS-CoV-2 in accordance with its genetic modification is of great importance. In this study we introduce a Mass Spectrometry (MS)-based methodology for the diagnosis of SARS-CoV-2 in propagated in cell-culture. This methodology enables the universal identification of SARS-CoV-2, as well as variant-specific discrimination. The universal identification of SARS-CoV-2 is based on conserved markers shared by all variants, while the identification of specific variants relies on variant-specific markers. Determining a specific set of peptides for a given variant consists of a multistep procedure, starting with an in-silico search for variant-specific tryptic peptides, followed by a tryptic digest of a cell-cultured SARS-CoV-2 variant, and identification of these markers by HR-LC-MS/MS analysis. As a proof of concept, this approach was demonstrated for four representative VOCs compared to the wild-type Wuhan reference strain. For each variant, at least two unique markers, derived mainly from the spike (S) and nucleocapsid (N) viral proteins, were identified. This methodology is specific, rapid, easy to perform and inexpensive. Therefore, it can be applied as a diagnostic tool for pathogenic variants.

Delay-differential SEIR modeling for improved modelling of infection dynamics

SEIR (Susceptible-Exposed-Infected-Recovered) approach is a classic modeling method that is frequently used to study infectious diseases. However, in the vast majority of such models transitions from one population group to another are described using the mass-action law. That causes inability to reproduce observable dynamics of an infection such as the incubation period or progression of the disease's symptoms. In this paper, we propose a new approach to simulate the epidemic dynamics based on a system of differential equations with time delays and instant transitions to approximate durations of transition processes more correctly and make model parameters more clear. The suggested approach can be applied not only to Covid-19 but also to the study of other infectious diseases. We utilized it in the development of the delay-based model of the COVID-19 pandemic in Germany and France. The model takes into account testing of different population groups, symptoms progression from mild to critical, vaccination, duration of protective immunity and new virus strains. The stringency index was used as a generalized characteristic of the non-pharmaceutical government interventions in corresponding countries to contain the virus spread. The parameter identifiability analysis demonstrated that the presented modeling approach enables to significantly reduce the number of parameters and make them more identifiable. Both models are publicly available.

Directions of change in intrinsic case severity across successive SARS-CoV-2 variant waves have been inconsistent

OBJECTIVES

To determine how the intrinsic severity of successively dominant SARS-CoV-2 variants changed over the course of the pandemic.

METHODS

A retrospective cohort analysis in the NHS Greater Glasgow and Clyde (NHS GGC) Health Board. All sequenced non-nosocomial adult COVID-19 cases in NHS GGC with relevant SARS-CoV-2 lineages (B.1.177/Alpha, Alpha/Delta, AY.4.2 Delta/non-AY.4.2 Delta, non-AY.4.2 Delta/Omicron, and BA.1 Omicron/BA.2 Omicron) during analysis periods were included. Outcome measures were hospital admission, ICU admission, or death within 28 days of positive COVID-19 test. We report the cumulative odds ratio; the ratio of the odds that an individual experiences a severity event of a given level vs all lower severity levels for the resident and the replacement variant after adjustment.

RESULTS

After adjustment for covariates, the cumulative odds ratio was 1.51 (95% CI: 1.08-2.11) for Alpha versus B.1.177, 2.09 (95% CI: 1.42-3.08) for Delta versus Alpha, 0.99 (95% CI: 0.76-1.27) for AY.4.2 Delta versus non-AY.4.2 Delta, 0.49 (95% CI: 0.22-1.06) for Omicron versus non-AY.4.2 Delta, and 0.86 (95% CI: 0.68-1.09) for BA.2 Omicron versus BA.1 Omicron.

CONCLUSIONS

The direction of change in intrinsic severity between successively emerging SARS-CoV-2 variants was inconsistent, reminding us that the intrinsic severity of future SARS-CoV-2 variants remains uncertain.

Viral burden is associated with age, vaccination, and viral variant in a population-representative study of SARS-CoV-2 that accounts for time-since-infection-related sampling bias

In this study, we evaluated the impact of viral variant, in addition to other variables, on within-host viral burden, by analysing cycle threshold (Ct) values derived from nose and throat swabs, collected as part of the UK COVID-19 Infection Survey. Because viral burden distributions determined from community survey data can be biased due to the impact of variant epidemiology on the time-since-infection of samples, we developed a method to explicitly adjust observed Ct value distributions to account for the expected bias. By analysing the adjusted Ct values using partial least squares regression, we found that among unvaccinated individuals with no known prior exposure, viral burden was 44% lower among Alpha variant infections, compared to those with the predecessor strain, B.1.177. Vaccination reduced viral burden by 67%, and among vaccinated individuals, viral burden was 286% higher among Delta variant, compared to Alpha variant, infections. In addition, viral burden increased by 17% for every 10-year age increment of the infected individual. In summary, within-host viral burden increases with age, is reduced by vaccination, and is influenced by the interplay of vaccination status and viral variant.

Assessing the evolution of SARS-CoV-2 lineages and the dynamic associations between nucleotide variations

Despite seminal advances towards understanding the infection mechanism of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), it continues to cause significant morbidity and mortality worldwide. Though mass immunization programmes have been implemented in several countries, the viral transmission cycle has shown a continuous progression in the form of multiple waves. A constant change in the frequencies of dominant viral lineages, arising from the accumulation of nucleotide variations (NVs) through favourable selection, is understandably expected to be a major determinant of disease severity and possible vaccine escape. Indeed, worldwide efforts have been initiated to identify specific virus lineage(s) and/or NVs that may cause a severe clinical presentation or facilitate vaccination breakthrough. Since host genetics is expected to play a major role in shaping virus evolution, it is imperative to study the role of genome-wide SARS-CoV-2 NVs across various populations. In the current study, we analysed the whole genome sequence of 3543 SARS-CoV-2-infected samples obtained from the state of Telangana, India (including 210 from our previous study), collected over an extended period from April 2020 to October 2021. We present a unique perspective on the evolution of prevalent virus lineages and NVs during this period. We also highlight the presence of specific NVs likely to be associated favourably with samples classified as vaccination breakthroughs. Finally, we report genome-wide intra-host variations at novel genomic positions. The results presented here provide critical insights into virus evolution over an extended period and pave the way to rigorously investigate the role of specific NVs in vaccination breakthroughs.

Genomic Diversity and Evolution of SARS-CoV-2 Lineages in Pakistan

The emergence of SARS-CoV-2 variants has posed a challenge to disease control efforts worldwide. This study explored the genomic diversity and phylogenetic relationship of SARS-CoV-2 variants reported in Pakistan. Our objective was to understand the transmission dynamics of different lineages within the country. We retrieved and analyzed spike protein sequences from Pakistan and compared them with reference sequences reported worldwide. Our analysis revealed the clustering of Pakistan-origin isolates in nine different clades representing different regions worldwide, suggesting the transmission of multiple lineages within the country. We found 96 PANGO lineages of SARS-CoV-2 in Pakistan, and 64 of these corresponded to 4 WHO-designated variants: Alpha, Beta, Delta, and Omicron. The most dominant variants in Pakistan were Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617.2, AY.108), and Omicron (BA.2.75, BA.5.2), and the N-terminal domain and receptor binding regions were the most hypervariable regions of the spike gene. Compared to the reference strain, characteristic substitutions were found in dominant variants. Our findings emphasize the importance of continuously monitoring and assessing nucleotide and residue substitutions over time to understand virus evolutionary trends better and devise effective disease control interventions.

In Vitro Analysis of the Effect of SARS-CoV-2 Non-VOC and four Variants of Concern on MHC-Class-I Expression on Calu-3 and Caco-2 Cells

As the MHC-I-pathway is key to antigen presentation to cytotoxic T-cells and, therefore, recognition by the host adaptive immune system, we hypothesized that SARS-CoV-2 including its Variants of Concern (VOCs), influences MHC-I expression on epithelial cell surfaces as an immune evasion strategy. We conducted an in vitro time course experiment with the human airway epithelial cell line Calu-3 and the human colorectal adenocarcinoma cell line Caco-2. Cells were infected with SARS-CoV-2 strains non-VOC/B.1.1, Alpha/B.1.1.7, Beta/B.1.351, Gamma/P.1, and Delta/B.1.617.2. At 2, 24, 48 and 72 h post-infection we performed RT-qPCR to track viral replication. Simultaneously, we performed intracellular staining with a serum of a double-vaccinated healthy adult containing a high amount of spike protein antibody. In flow cytometry experiments, we differentiated between infected (spike protein positive) and bystander (spike protein negative) cells. To compare their HLA expression levels, cells were stained extracellularly with anti-HLA-A-IgG and anti-HLA-B,C-IgG. While HLA-A expression was stable on infected Calu-3 cells for all variants, it increased to different degrees on bystander cells in samples infected with VOCs Beta, Gamma, Delta, or non-VOC over the time course analyzed. In contrast, HLA-A levels were stable in bystander Calu-3 cells in samples infected with the Alpha variant. The upregulation of MHC-I on spike protein negative bystander cells in Calu-3 cell cultures infected with Beta, Gamma, Delta, and partly non-VOC might suggest that infected cells are still capable of secreting inflammatory cytokines like type-I interferons stimulating the MHC-I expression on bystander cells. In comparison, there was no distinct effect on HLA expression level on Caco-2 cells of any of the VOCs or non-VOC. Further investigations of the full range of immune evasion strategies of SARS-CoV-2 variants are warranted.

Generating simple classification rules to predict local surges in COVID-19 hospitalizations

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

Highly Selective Aptamer-Molecularly Imprinted Polymer Hybrids for Recognition of SARS-CoV-2 Spike Protein Variants

Virus recognition has been driven to the forefront of molecular recognition research due to the COVID-19 pandemic. Development of highly sensitive recognition elements, both natural and synthetic is critical to facing such a global issue. However, as viruses mutate, it is possible for their recognition to wane through changes in the target substrate, which can lead to detection avoidance and increased false negatives. Likewise, the ability to detect specific variants is of great interest for clinical analysis of all viruses. Here, a hybrid aptamer-molecularly imprinted polymer (aptaMIP), that maintains selective recognition for the spike protein template across various mutations, while improving performance over individual aptamer or MIP components (which themselves demonstrate excellent performance). The aptaMIP exhibits an equilibrium dissociation constant of 1.61 nM toward its template which matches or exceeds published examples of imprinting of the spike protein. The work here demonstrates that "fixing" the aptamer within a polymeric scaffold increases its capability to selectivity recognize its original target and points toward a methodology that will allow variant selective molecular recognition with exceptional affinity.

The Quantification of Spike Proteins in the Inactivated SARS-CoV-2 Vaccines of the Prototype, Delta, and Omicron Variants by LC-MS

Developing variant vaccines or multivalent vaccines is a feasible way to address the epidemic as the SARS-CoV-2 variants of concern (VOCs) posed an increased risk to global public health. The spike protein of the SARS-CoV-2 virus was usually used as the main antigen in many types of vaccines to produce neutralizing antibodies against the virus. However, the spike (S) proteins of different variants were only differentiated by a few amino acids, making it difficult to obtain specific antibodies that can distinguish different VOCs, thereby challenging the accurate distinction and quantification of the variants using immunological methods such as ELISA. Here, we established a method based on LC-MS to quantify the S proteins in inactivated monovalent vaccines or trivalent vaccines (prototype, Delta, and Omicron strains). By analyzing the S protein sequences of the prototype, Delta, and Omicron strains, we identified peptides that were different and specific among the three strains and synthesized them as references. The synthetic peptides were isotopically labeled as internal targets. Quantitative analysis was performed by calculating the ratio between the reference and internal target. The verification results have shown that the method we established had good specificity, accuracy, and precision. This method can not only accurately quantify the inactivated monovalent vaccine but also could be applied to each strain in inactivated trivalent SARS-CoV-2 vaccines. Hence, the LC-MS method established in this study can be applied to the quality control of monovalent and multivalent SARS-CoV-2 variation vaccines. By enabling more accurate quantification, it will help to improve the protection of the vaccine to some extent.

SARS-CoV-2 variants and mutational patterns: relationship with risk of ventilator-associated pneumonia in critically ill COVID-19 patients in the era of dexamethasone

We aimed to explore the relationships between specific viral mutations/mutational patterns and ventilator-associated pneumonia (VAP) occurrence in COVID-19 patients admitted in intensive care units between October 1, 2020, and May 30, 2021. Full-length SARS-CoV-2 genomes were sequenced by means of next-generation sequencing. In this prospective multicentre cohort study, 259 patients were included. 222 patients (47%) had been infected with pre-existing ancestral variants, 116 (45%) with variant α, and 21 (8%) with other variants. 153 patients (59%) developed at least one VAP. There was no significant relationship between VAP occurrence and a specific SARS CoV-2 lineage/sublineage or mutational pattern.

The SARS-CoV-2 Alpha variant was associated with increased clinical severity of COVID-19 in Scotland: A genomics-based retrospective cohort analysis

OBJECTIVES

The SARS-CoV-2 Alpha variant was associated with increased transmission relative to other variants present at the time of its emergence and several studies have shown an association between Alpha variant infection and increased hospitalisation and 28-day mortality. However, none have addressed the impact on maximum severity of illness in the general population classified by the level of respiratory support required, or death. We aimed to do this.

METHODS

In this retrospective multi-centre clinical cohort sub-study of the COG-UK consortium, 1475 samples from Scottish hospitalised and community cases collected between 1st November 2020 and 30th January 2021 were sequenced. We matched sequence data to clinical outcomes as the Alpha variant became dominant in Scotland and modelled the association between Alpha variant infection and severe disease using a 4-point scale of maximum severity by 28 days: 1. no respiratory support, 2. supplemental oxygen, 3. ventilation and 4. death.

RESULTS

Our cumulative generalised linear mixed model analyses found evidence (cumulative odds ratio: 1.40, 95% CI: 1.02, 1.93) of a positive association between increased clinical severity and lineage (Alpha variant versus pre-Alpha variants).

CONCLUSIONS

The Alpha variant was associated with more severe clinical disease in the Scottish population than co-circulating lineages.

Research progress in spike mutations of SARS-CoV-2 variants and vaccine development

The coronavirus disease 2019 (COVID-19) pandemic can hardly end with the emergence of different variants over time. In the past 2 years, several variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), such as the Delta and Omicron variants, have emerged with higher transmissibility, immune evasion and drug resistance, leading to higher morbidity and mortality in the population. The prevalent variants of concern (VOCs) share several mutations on the spike that can affect virus characteristics, including transmissibility, antigenicity, and immune evasion. Increasing evidence has demonstrated that the neutralization capacity of sera from COVID-19 convalescent or vaccinated individuals is decreased against SARS-CoV-2 variants. Moreover, the vaccine effectiveness of current COVID-19 vaccines against SARS-CoV-2 VOCs is not as high as that against wild-type SARS-CoV-2. Therefore, more attention might be paid to how the mutations impact vaccine effectiveness. In this review, we summarized the current studies on the mutations of the SARS-CoV-2 spike, particularly of the receptor binding domain, to elaborate on how the mutations impact the infectivity, transmissibility and immune evasion of the virus. The effects of mutations in the SARS-CoV-2 spike on the current therapeutics were highlighted, and potential strategies for future vaccine development were suggested.

Benchmarking machine learning robustness in Covid-19 genome sequence classification

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome-millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses, is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models. This paper makes the first effort (to our knowledge) to benchmark the robustness of ML models by simulating biological sequences with errors. In this paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show from experiments on a wide array of ML models that some simulation-based approaches with different perturbation budgets are more robust (and accurate) than others for specific embedding methods to certain noise simulations on the input sequences. Our benchmarking framework may assist researchers in properly assessing different ML models and help them understand the behavior of the SARS-CoV-2 virus or avoid possible future pandemics.

Clinical outcomes of COVID-19 caused by the Alpha variant compared with one by wild type in Kobe, Japan. A multi-center nested case-control study

OBJECTIVES

The emergence of the Alpha variant of novel coronavirus 2019 (SARS-CoV-2) is a concerning issue but their clinical implications have not been investigated fully.

METHODS

We conducted a nested case-control study to compare severity and mortality caused by the Alpha variant (B.1.1.7) with the one caused by the wild type as a control from December 2020 to March 2021, using whole-genome sequencing. 28-day mortality and other clinically important outcomes were evaluated.

RESULTS

Infections caused by the Alpha variant were associated with an increase in the use of oxygen (43.4% vs 26.3%. p = 0.017), high flow nasal cannula (21.2% vs 4.0%, p = 0.0007), mechanical ventilation (16.2% vs 6.1%, p = 0.049), ICU care (30.3% vs 14.1%, p = 0.01) and the length of hospital stay (17 vs 10 days, p = 0.031). More patients with the Alpha variant received medications such as dexamethasone. However, the duration of each modality did not differ between the 2 groups. Likewise, there was no difference in 28-day mortality between the 2 groups (12% vs 8%, p = 0.48), even after multiple sensitivity analyses, including propensity score analysis.

CONCLUSION

The Alpha variant was associated with a severe form of COVID-19, compared with the non-Alpha wild type, but might not be associated with higher mortality.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Clinical and Virological Features of SARS-CoV-2 Variants during the Four Waves of the Pandemic in the Mexican Southeast

We conducted a retrospective study using a population of patients who were hospitalized at Dr. Juan Graham Casasus Hospital in Villahermosa (Tabasco, Mexico) and had a positive RT-PCR test for SARS-CoV-2 between June 2020 and January 2022. We analyzed all medical records, including demographic data, SARS-CoV-2 exposure history, underlying comorbidities, symptoms, signs at admission, laboratory findings during the hospital stay, outcome, and whole-genome sequencing data. Finally, the data were analyzed in different sub-groups according to distribution during waves of the COVID-19 pandemic regarding Mexican reports from June 2020 to January 2022. Of the 200 patients who tested positive via PCR for SARS-CoV-2, only 197 had samples that could be sequenced. Of the samples, 58.9% (n = 116) were males and 41.1% (n = 81) females, with a median age of 61.7 ± 17.0 years. Comparisons between the waves of the pandemic revealed there were significant differences in the fourth wave: the age of patients was higher (p = 0.002); comorbidities such as obesity were lower (p = 0.000), while CKD was higher (p = 0.011); and hospital stays were shorter (p = 0.003). The SARS-CoV-2 sequences revealed the presence of 11 clades in the study population. Overall, we found that adult patients admitted to a third-level Mexican hospital had a wide range of clinical presentations. The current study provides evidence for the simultaneous circulation of SARS-CoV-2 variants during the four pandemic waves.

Accurate Detection of SARS-CoV-2 by Next-Generation Sequencing in Low Viral Load Specimens

As new SARS-CoV-2 variants emerge, there is an urgent need to increase the efficiency and availability of viral genome sequencing, notably to detect the lineage in samples with a low viral load. SARS-CoV-2 genome next-generation sequencing (NGS) was performed retrospectively in a single center on 175 positive samples from individuals. An automated workflow used the Ion AmpliSeq SARS-CoV-2 Insight Research Assay on the Genexus Sequencer. All samples were collected in the metropolitan area of the city of Nice (France) over a period of 32 weeks (from 19 July 2021 to 11 February 2022). In total, 76% of cases were identified with a low viral load (Ct ≥ 32, and ≤200 copies/µL). The NGS analysis was successful in 91% of cases, among which 57% of cases harbored the Delta variant, and 34% the Omicron BA.1.1 variant. Only 9% of cases had unreadable sequences. There was no significant difference in the viral load in patients infected with the Omicron variant compared to the Delta variant (Ct values, p = 0.0507; copy number, p = 0.252). We show that the NGS analysis of the SARS-CoV-2 genome provides reliable detection of the Delta and Omicron SARS-CoV-2 variants in low viral load samples.

Model training periods impact estimation of COVID-19 incidence from wastewater viral loads

Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective responses. As the wastewater (WW) becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision-making. This research aimed to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in WW. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. A testing period is classified as adequate when the rate of change in testing is greater than the rate of change in cases. We present a Bayesian deconvolution and linear regression model to estimate COVID-19 cases from WW data. The effective reproductive number is estimated from reconstructed cases using WW. The proposed modeling framework was applied to three Northern California communities served by distinct WW treatment plants. The results showed that training periods with adequate testing are essential to provide accurate projections of COVID-19 incidence.