Omicron variant (B.1.1.529) of SARS-CoV-2: Mutation, infectivity, transmission, and vaccine resistance

Inhibition potential of natural flavonoids against selected omicron (B.1.19) mutations in the spike receptor binding domain of SARS-CoV-2: a molecular modeling approach

The omicron (B.1.19) variant of contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is considered a variant of concern (VOC) due to its increased transmissibility and highly infectious nature. The spike receptor-binding domain (RBD) is a hotspot of mutations and is regarded as a prominent target for screening drug candidates owing to its crucial role in viral entry and immune evasion. To date, no effective therapy or antivirals have been reported; therefore, there is an urgent need for rapid screening of antivirals. An extensive molecular modelling study has been performed with the primary goal to assess the inhibition potential of natural flavonoids as inhibitors against RBD from a manually curated library. Out of 40 natural flavonoids, five natural flavonoids, namely tomentin A (-8.7 kcal/mol), tomentin C (-8.6 kcal/mol), hyperoside (-8.4 kcal/mol), catechin gallate (-8.3 kcal/mol), and corylifol A (-8.2 kcal/mol), have been considered as the top-ranked compounds based on their binding affinity and molecular interaction profiling. The state-of-the-art molecular dynamics (MD) simulations of these top-ranked compounds in complex with RBD exhibited stable dynamics and structural compactness patterns on 200 nanoseconds. Additionally, complexes of these molecules demonstrated favorable free binding energies and affirmed the docking and simulation results. Moreover, the post-simulation validation of these interacted flavonoids using principal component analysis (PCA) revealed stable interaction patterns with RBD. The integrated results suggest that tomentin A, tomentin C, hyperoside, catechin gallate, and corylifol A might be effective against the emerging variants of SARS-CoV-2 and should be further evaluated using in-vitro and in-vivo experiments.Communicated by Ramaswamy H. Sarma.

Prospective clinical performance of CoVarScan in identifying SARS-CoV-2 Omicron subvariants

The purpose of this work was to evaluate the performance of CoVarScan, a multiplex fragment analysis approach, in identifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of the Omicron lineage rapidly and accurately. The ability to identify variants with high fidelity and low turnaround time is important both epidemiologically and clinically for pandemic monitoring and therapeutic monoclonal antibody (mAb) selection. Currently, the gold-standard test for this task is whole-genome sequencing (WGS), which is prohibitively expensive and/or inaccessible due to equipment requirements for many laboratories. Omicron variants have been closely related, so the ability of genotyping tests to differentiate them is an important, outstanding question. CoVarScan uses PCR targeting eight SARS-CoV-2 mutational hot spots. In total, 4,918 SARS-CoV-2-positive cases between 17 December 2021 and 31 January 2024 were included in the analysis. CoVarScan achieved 96.5% concordance with WGS and could detect unique mutational signatures for BA.1, BA.2, BA.2.12.1, BA.4/BA.5, BA.2.75, XBB, and BA.2.86. These are the major variants of concern (VOCs) that have dominated since Omicron originally appeared in December 2021. Lastly, based on panel design, we predict a unique mutational pattern for the newly emergent, highly mutated variant BA.2.87. CoVarScan can rapidly, accurately, and cost-effectively identify all Omicron variants in a scalable manner. Furthermore, CoVarScan does not require design alterations to detect new VOCs. CoVarScan performs as accurately as WGS with higher sensitivity, allowing its use as a tool to quickly identify variants for epidemiological surveillance and clinical decision-making in the selection of effective therapeutic mAbs.IMPORTANCEAlmost 5 years since the start of the pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern continue to emerge, with mutations conferring new properties like increased transmissibility and resistance to therapeutic monoclonal antibodies and vaccines. Conventionally, whole-genome sequencing (WGS) has characterized new SARS-CoV-2 variants, but results come too late for clinical actionability. WGS suffers from high failure rates for samples with low viral RNA and is inaccessible for lower-resource laboratories. As new variants like Omicron appear, it is necessary to develop rapid and accurate testing to distinguish between variants. Fast and accurate identification of sensitive viral lineages would allow tailored use of monoclonal antibodies that may otherwise have been pulled from the market due to rising overall resistance. Rapid results also allow public health officials to make policy decisions in time to reduce morbidity and mortality for sensitive populations such as patients who are immunocompromised or have significant medical comorbidities.

Clinical characteristics and prediction model of re-positive nucleic acid tests among Omicron infections by machine learning: a real-world study of 35,488 cases

BACKGROUND

During the Omicron BA.2 variant outbreak in Shanghai, China, from April to May 2022, PCR nucleic acid test re-positivity (TR) occurred frequently, yet the risk factors and predictive models for TR remain unclear. This study aims to identify the factors influencing Omicron TR and to develop machine learning models to predict TR risk. Accurately predicting re-positive patients is crucial for identifying high-risk individuals, optimizing resource allocation, and developing personalized treatment and management plans, thereby effectively controlling the spread of the epidemic, reducing community burden, and ensuring public health.

METHODS

A retrospective study was conducted among individuals infected with Omicron BA.2 variant from April 12 to May 25, 2022, in the largest Shanghai Fangcang shelter hospital. Five machine learning models were compared, including k-nearest-neighbors (KNN), logistic regression (logistic), bootstrap aggregation (bagging), error back-propagation (BP) neural network, and support vector machines (SVM), to select the best prediction model for the TR risk factors.

RESULTS

A total of 35,488 cases were included in this real-world study. The TR and control groups comprised of 6,171 and 29,317 cases respectively, with a re-positive rate of 17.39%. Higher occurrence of TR was observed in young age, males, those with obvious symptoms, underlying diseases, and a low Ct value. The KNN model proved to be the best in predicting the prognosis in the overall evaluation (accuracy = 0.8198, recall = 0.8026, and AUC = 0.8110 in the test set).

INTERPRETATION

Higher TR risk was found in infected cases who were underage or with underlying diseases; vaccine brand and inoculation status were not significantly associated with TR. KNN was the most effective machine learning model to predict TR occurrence in isolation.

Comparative Effectiveness of Antivirals and Monoclonal Antibodies for Treating COVID-19 Patients Infected With Omicron Variant: A Systematic Review and Network Meta-Analysis

Antiviral drugs likely remain effective against the SARS-CoV-2 Omicron variant, while monoclonal antibody (mAb) therapies have experienced drops in neutralizing ability. This systematic review and network meta-analysis aims to estimate the comparative effectiveness of antivirals and mAb therapies for treating COVID-19 patients infected with Omicron, capturing primarily acute outcomes. We searched multiple databases from July 4 to July 19, 2022, with updates through November 4, 2022. Studies comparing the effectiveness of antivirals or mAb to either nonuser controls or other treatments were included. Risk of bias was assessed using the Cochrane RoB 2 and ROBINS-I tools. Data extraction and verification involved five independent researchers. Among 39 studies (727,893 individuals with COVID-19, including 38 nonrandomized trials), nirmatrelvir/ritonavir and sotrovimab were associated with lower risks of mortality (HR = 0.317, 95% credible intervals [CrI] = 0.144-0.678; HR = 0.176, 95%CrI = 0.052-0.527) and hospitalization (HR = 0.479, 95%CrI = 0.319-0.711; HR = 0.489, 95%CrI = 0.293-0.797) compared with nonuser controls. Remdesivir users were associated with a lower risk of hospitalization (HR = 0.367, 95%CrI = 0.147-0.868) but not mortality. Molnupiravir and bebtelovimab showed no significant benefits for these outcomes. In conclusion, among individuals infected with COVID-19 during the Omicron wave, mortality risk was lower with nirmatrelvir/ritonavir or sotrovimab use, whereas hospitalization was reduced with nirmatrelvir/ritonavir, remdesivir, or sotrovimab. Sotrovimab and nirmatrelvir/ritonavir were effective against Omicron B.1.1.529/BA.1 and BA.2/BA.4/BA.5 subvariants, respectively. A key limitation is that findings rely on data from the last search and may be impacted by potential changes in mortality risk due to immune evasion by emerging variants, highlighting the need for ongoing randomized trials across variants and populations. TRIAL REGISTRATION: The study was registered on PROSPERO, CRD42022351508.

Research progress of spike protein mutation of SARS-CoV-2 mutant strain and antibody development

The coronavirus disease 2019 (COVID-19) is a respiratory disease with a very high infectious rate caused by the Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2). Because SARS-CoV-2 is easy to mutate, the continuous emergence of SARS-CoV-2 variant strains not only enhances the infectivity of the SARS-CoV-2 but also brings great obstacles to the treatment of COVID-19. Neutralizing antibodies have achieved good results in the clinical application of the novel coronavirus pneumonia, which can be used for pre-infection protection and treatment of novel coronavirus patients. This review makes a detailed introduction to the mutation characteristics of SARS-CoV-2, focusing on the molecular mechanism of mutation affecting the infectivity of SARS-CoV-2, and the impact of mutation on monoclonal antibody therapy, providing scientific reference for the prevention of SARS-CoV-2 variant strains and the research and development of antibody drugs.

Engineered bispecific antibodies with enhanced breadth and potency against SARS-CoV-2 variants and SARS-related coronaviruses

The concern of COVID-19 persists due to the continuous emergence of variants and the potential spillover of animal coronaviruses. The broad-spectrum neutralizing antibodies play a pivotal role in the prevention and treatment of coronavirus (CoV) infections. Here, we constructed 18 bi-specific antibodies (bsAbs) using 9 antibodies isolated from COVID-19 convalescents and vaccinated individuals, designed as dual variable domain immunoglobulin (DVD-Ig). A bsAb 5-HI showed a high binding capability to the S1 subunit of spike and exhibited breadth and potency against pseudotyped SARS-CoV-2 variants of concerns (VOCs) and SARS-related-CoVs (SARSr-CoVs), with half maximal effective concentration (EC50) of 0.028-3.444 nM and 50% inhibitory concentration (IC50) of 0.008-0.800 nM. In addition, it retained neutralization potency against the peudotyped virus of recently prevalent JN.1 strain (IC50, 12.74 nM). We found that the parental antibodies showed weak or no binding to the receptor binding domain (RBD) of the SARS-CoV, EG.5.1, and JN.1. However, the 5-HI maintained the binding with RBD and prevented the binding between hACE2 and RBD (IC50 for the RBD of SARS-CoV, 1.067 nM; EG.5.1, 0.423 nM; JN.1, 0.223 nM). In neutralization assays with the authentic virus, we found that the 5-HI effectively neutralized Omicron variants XBB.1.5 (IC50, 0.308 nM), EG.5.1 (IC50, 0.129 nM), and JN.1 (IC50, 13.692 nM), while its parental antibodies showed weakened or no neutralization. Therefore, the 5-HI represents a promising candidate for further development in the treatment and prevention of ongoing evolved SARS-CoV-2 VOCs and other SARSr-CoVs that potentially emerge in the future.

Local effects of non-pharmaceutical interventions on mitigation of COVID-19 spread through decreased human mobilities in Japan: a prefecture-level mediation analysis

To control the COVID-19 epidemic, the Japanese government and the local governments have repeatedly implemented non-pharmaceutical interventions (NPIs) throughout 2020-2022. Using Bayesian state-space mediation models, we examined the effect of repeated NPIs on infection spread mitigation, mediated by human mobility changes in each prefecture during three epidemic phases: from April 1, 2020 to February 28, 2021; from March 1, 2021 to December 16, 2021; and from December 17, 2021 to December 31, 2022. In the first phase, controlling downtown populations at nighttime was effective in mitigating the infection spread in almost all prefectures. In the second and third phases, the effect was not clear, especially in metropolitan prefectures. Controlling visitors from the central prefectures of metropolitan areas was effective in mitigating infection spread in the surrounding prefectures during all phases. These results suggest that the local spread of infection can be mitigated by focusing on nighttime human mobility control in downtown areas before the epidemic spreads widely and transmission routes become more diverse, and that the geospatial spread of infection can be prevented by controlling the flows of people from large cities to other areas.

Characteristics and outcomes of ICU-admitted COVID-19 patients in the Omicron and Alpha-dominated periods

BACKGROUND

Comparing the outcomes of intensive care unit (ICU) admitted COVID-19 patients during the Alpha and Omicron-dominated periods.

METHODS

Patients with critical COVID-19 disease, requiring ICU admission from May to September 2021 and February to August 2022, were enrolled from a single medical center in Northern Taiwan. Clinical demographics, comorbidities, disease severity, and management strategies were recorded. The 28-day mortality from the two periods were compared both in the original and propensity score (PS)-matched cohort.

RESULTS

Of 231 patients, 72 (31.2%) were from the Alpha period and 159 (68.8%) from the Omicron period. Patients in the Omicron period were older, had a lower body mass index, more comorbidities, higher disease severities, and increased 28-day mortality (26.4% vs. 13.9%, p = 0.035). In multivariable analysis, the Omicron-dominated period was not identified as an independent factor associated with increased 28-day mortality. COVID-19 patients in Alpha- and Omicron-dominated periods had comparable 28-day mortality in PS-matched cohort (12.1% vs. 18.2%, p = 0.733). Independent factors associated with 28-day mortality were a lower PF ratio (PF ratio <100, adjusted odds ratio [aOR] 2.68, 95% confidence interval, CI 1.21-5.94), septic shock ([aOR] 2.39, 95% CI 1.12-5.09) and absence of remdesivir ([aOR] 0.36, 95% CI 0.16-0.83).

CONCLUSION

While patients in the Omicron period exhibited greater severity, the variant was not independently linked to higher 28-day mortality in ICU-admitted patients.

Serious Game Development for Public Health: Participatory Design Approach to COVID-19 Quarantine Policy Education

Background

Public health education plays a crucial role in effectively addressing infectious diseases such as COVID-19. However, existing educational materials often provide only foundational information, and traditional group education faces challenges due to social distancing policies.

Objective

Addressing these gaps, our study introduces a serious game called "Flattening the Curve." This interactive experience immerses learners in the role of quarantine policy managers, offering unique insights into the effects and challenges of social distancing policies.

Methods

The development of the game adhered to the SERES framework, ensuring a scientifically designed foundation. To achieve its learning objectives, the game incorporated learning and game mechanics including an agent-based infection model, a social distancing policy model, and an economic model, which were developed based on previous literature. After defining a broad concept of scientific and design foundations, we used a participatory design process. This study included 16 undergraduates and took place over one semester. Participants played the game, gave feedback, and answered surveys. The game was improved based on participants' feedback throughout the process. Participants' feedback was analyzed based on the Design, Play, and Experience framework. Surveys were conducted before and after the activity and analyzed to assess participants' evaluation of and satisfaction with the game.

Results

The game successfully achieved its learning objectives, encompassing a comprehensive understanding of infectious disease characteristics; the disease transmission process; the necessity and efficacy of quarantine policies and their delicate balance with economic factors; and the concept of flattening the curve. To achieve this, the game includes the following: (1) an agent-based infection model based on the modified Susceptible-Exposed-Infectious-Hospitalized-Recovered (SEIHR) model with five infectious disease scenarios; (2) a quarantine policy model with social distancing, travel control, and intensive care unit management; and (3) an economic model that allows users to consider the impact of quarantine policies on a community's economy. In response to participatory design feedback, the game underwent meticulous modifications, including refining game systems, parameters, design elements, the user interface, and interactions. Key feedback included requests for more scenarios and engaging yet simple game elements, as well as suggestions for improving the scoring system and design features. Notably, concerns about the fairness of the outcome evaluation system (star rating system), which could incentivize prioritizing economic activity over minimizing casualties, were raised and addressed by replacing the star rating system with a progress-based vaccine development system. Quantitative evaluation results reflect participants' positive assessments of the game through the learner-centric approach.

Conclusions

The serious game "Flattening the Curve," developed through a participatory design approach, emerges as a valuable tool for public health education, particularly concerning social distancing policies. The game and its source code are openly accessible online, enabling widespread use for research and educational purposes.

Younger and rural children are more likely to be hospitalized for SARS-CoV-2 infections

PURPOSE

To identify characteristics of SARS-CoV-2 infection that are associated with hospitalization in children initially evaluated in a Pediatric Emergency Department (ED).

METHODS

We identified cases of SARS-CoV-2 positive patients seen in the Arkansas Children's Hospital (ACH) ED or hospitalized between May 27, 2020, and April 28, 2022, using ICD-10 codes within the Pediatric Hospital Information System (PHIS) Database. We compared infection waves for differences in patient characteristics and used logistic regressions to examine which features led to a higher chance of hospitalization.

FINDINGS

We included 681 pre-Delta cases, 673 Delta cases, and 970 Omicron cases. Almost 17% of patients were admitted to the hospital. Compared to Omicron-infected children, pre-Delta and Delta-infected children were twice as likely hospitalized (OR = 2.2 and 2.0, respectively; p<0.0001). Infants under one year were >3 times as likely to be hospitalized than children ages 5-14 years regardless of wave (OR = 3.42; 95%CI = 2.36-4.94). Rural children were almost three times as likely than urban children to be hospitalized across all waves (OR = 2.73; 95%CI = 1.97-3.78). Finally, those with a complex condition had nearly a 15-fold increase in odds of admission (OR = 14.6; 95%CI = 10.6-20.0).

CONCLUSIONS

Children diagnosed during the pre-Delta or Delta waves were more likely to be hospitalized than those diagnosed during the Omicron wave. Younger and rural patients were more likely to be hospitalized regardless of the wave. We suspect lower vaccination rates and larger distances from medical care influenced higher hospitalization rates.