SARS-CoV-2 Omicron lineage XBB spike structures, conformations, antigenicity, and receptor recognition (preprint)

A recombinant lineage of the SARS-CoV-2 Omicron variant, named XBB, appeared in late 2022 and evolved descendants that successively swept local and global populations. XBB lineage members were noted for their improved immune evasion and transmissibility. Here, we determine cryo-EM structures of XBB.1.5, XBB.1.16 and EG.5 spike (S) ectodomains to reveal enhanced occupancy of the receptor inaccessible closed state. Interprotomer receptor binding domain (RBD) interactions previously observed in BA.1 and BA.2 were retained to reinforce the 3-RBD-down state. Improved stability of XBB.1.5 and XBB.1.16 RBD compensated for loss of stability caused by early Omicron mutations, while the F456L substitution reduced EG.5 RBD stability. Long-range impacts of S1 subunit mutations affected conformation and epitope presentation in the S2 subunit. Taken together, our results feature a theme of iterative optimization of S protein stability as Omicron continues to evolve, while maintaining high affinity receptor binding and bolstering immune evasion.

Sensing the Pulse of the Pandemic: Geovisualizing the Demographic Disparities of Public Sentiment toward COVID-19 through Social Media (preprint)

Social media offers a unique lens to observe large-scale, spatial-temporal patterns of users reactions toward critical events. However, social media use varies across demographics, with younger users being more prevalent compared to older populations. This difference introduces biases in data representativeness, and analysis based on social media without proper adjustment will lead to overlooking the voices of digitally marginalized communities and inaccurate estimations. This study explores solutions to pinpoint and alleviate the demographic biases in social media analysis through a case study estimating the public sentiment about COVID-19 using Twitter data. We analyzed the pandemic-related Twitter data in the U.S. during 2020-2021 to (1) elucidate the uneven social media usage among demographic groups and the disparities of their sentiments toward COVID-19, (2) construct an adjusted public sentiment measurement based on social media, the Sentiment Adjusted by Demographics (SAD) index, to evaluate the spatiotemporal varying public sentiment toward COVID-19. The results show higher proportions of female and adolescent Twitter users expressing negative emotions to COVID-19. The SAD index unveils that the public sentiment toward COVID-19 was most negative in January and February 2020 and most positive in April 2020. Vermont and Wyoming were the most positive and negative states toward COVID-19.

In silico analysis and preclinical findings uncover potential targets of anti-cervical carcinoma and COVID-19 in laminarin, a promising nutraceutical

Until today, the coronavirus disease 2019 (COVID-19) pandemic has caused 6,043,094 deaths worldwide, and most of the mortality cases have been related to patients with long-term diseases, especially cancer. Autophagy is a cellular process for material degradation. Recently, studies demonstrated the association of autophagy with cancer development and immune disorder, suggesting autophagy as a possible target for cancer and immune therapy. Laminarin is a polysaccharide commonly found in brown algae and has been reported to have pharmaceutic roles in treating human diseases, including cancers. In the present report, we applied network pharmacology with systematic bioinformatic analysis, including gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, reactome pathway analysis, and molecular docking to determine the pharmaceutic targets of laminarin against COVID-19 and cervical cancer via the autophagic process. Our results showed that the laminarin would target ten genes: CASP8, CFTR, DNMT1, HPSE, KCNH2, PIK3CA, PIK3R1, SERPINE1, TLR4, and VEGFA. The enrichment analysis suggested their involvement in cell death, immune responses, apoptosis, and viral infection. In addition, molecular docking further demonstrated the direct binding of laminarin to its target proteins, VEGFA, TLR4, CASP8, and PIK3R1. The present findings provide evidence that laminarin could be used as a combined therapy for treating patients with COVID-19 and cervical cancer.

Efficacy and Safety of Dapagliflozin versus Liraglutide in Patients with Overweight or Obesity and Type 2 Diabetes Mellitus: A Randomised Controlled Clinical Trial in Tianjin, China

Objective We aimed to clarify the efficacy of dapagliflozin versus liraglutide in patients with overweight or obesity and type 2 diabetes mellitus (T2DM) at the beginning of the coronavirus disease 2019 (COVID-19) pandemic. Methods T2DM patients with overweight or obesity who visited the Metabolic Disease Management Center at Tianjin Fourth Central Hospital from October 2019 to January 2020 were recruited and randomised to receive dapagliflozin or liraglutide for 24 weeks. Changes in blood glucose and lipid levels, blood pressure, and body weight, as well as the occurrence of hypoglycaemia and other adverse events, were compared. Results 309 patients completed the study (143 in liraglutide group and 166 in dapagliflozin group). After 24 weeks, HbA1c, fasting blood glucose (FPG), and 2 h postprandial blood glucose (2hPG) levels significantly decreased from 8.80% ± 1.41% to 7.02% ± 1.05%, 10.41 ± 3.13 to 7.59 ± 2.16 mmol/L, and 17.90 ± 4.39 to 10.12 ± 2.47 mmol/L, respectively, in the dapagliflozin group, and from 8.92% ± 1.49% to 6.78% ± 1.00%, 10.04 ± 2.99 to 7.20 ± 1.63 mmol/L, and 17.30 ± 4.39 to 10.13 ± 4.15 mmol/L, respectively, in the liraglutide group. Changes in HbA1c, FPG, and 2hPG levels between groups were not significantly different. Systolic blood pressure (SBP) and low-density lipoprotein cholesterol (LDL-C) level significantly decreased from 144.1 ± 19.1 to 139.7 ± 16.2 mmHg (p = 0.001) and from 3.21 ± 0.94 to 2.98 ± 0.89 mmol/L (p = 0.014), respectively, in the dapagliflozin group. After COVID-19 outbreak, the number of patients taking sleep-promoting drugs increased from 4.9% to 9.4% (p = 0.029). Conclusions Liraglutide and dapagliflozin had strong hypoglycaemic effects in patients with overweight or obesity and T2DM at the beginning of the COVID-19 pandemic. Dapagliflozin may be beneficial in improving SBP and LDL-C levels;however, further research is warranted.

Rational Development of a Polysaccharide–Protein-Conjugated Nanoparticle Vaccine Against SARS-CoV-2 Variants and Streptococcus pneumoniae (Adv. Mater. 21/2022)

Nanoparticle Vaccines In article number 2200443, Liangzhi Xie, Chengfeng Qin, and co-workers develop a novel bivalent nanoparticle vaccine that confers protection against infection of multiple SARS-CoV-2 variants and Streptococcus pneumoniae. This universal polysaccharide?protein-conjugated vaccine platform provides a powerful tool to fight against cocirculating viral and bacterial pathogens worldwide.

Clinical characteristics of hospitalized patients in Binzhou, China during periods of the SARS-CoV-2 Omicron variant BA.2 predominance: a single-centered, retrospective, observational study (preprint)

Background: Since the first identification of the novel SARS-CoV-2 variant of concern Omicron in South Africa, it has rapidly spread around the world. This study aimed to evaluate the clinical and laboratory characteristics of patients infected with the SARS-CoV-2 Omicron variant BA.2. Methods: In this retrospective study, we extracted data for 422 patients in Binzhou COVID-19 treatment centerl from March 11 to April 28, 2022. Cases were analyzed on the basis of demographic, clinical, and laboratory data as well as radiological features. Results: Of 422 hospitalized patients with SARS-CoV-2 Omicron Variant BA.2, there were 311 (73.7%) asymptomatic, 102 (24.1%) mild cases and 9 (2.1%) moderate cases. The median age was 38 years (IQR, 14 to 58) for all the participants, and the cohort included 207 men and 215 women. Compared with asymptomatic patients, moderate patients were older and had more chronic comorbidities (P<0.001). For all patients, Only 23 (5.5%) of 422 patients had never received any COVID-19 vaccine dose. Nonvaccination rate was significant difference between asymptomatic group and moderte group (4.5% vs 33.3%, p=0.001), respectively. The most common symptoms at onset of illness were fever, fatigue. Moderate patients had more ground-glass opacity, and patchy shadowing. Lymphopenia was present in 6.6% of all patients, which was more common in moderate patients than asymptomatic patients (44.4% vs 4.8%, P<0.001). Conclusion: Unvaccinated and older patients (>65 years) with comorbidities are at increased risk of moderate infection. Lymphopenia, increased D-dimer, ground-glass opacity, and patchy shadowing are common in moderate patients. 

Cryo-EM structures of SARS-CoV-2 Omicron BA.2 spike (preprint)

The BA.2 lineage of the SARS-CoV-2 Omicron variant has gained in proportion relative to BA.1. As differences in their spike (S) proteins may underlie differences in their pathobiology, here we determined cryo-EM structures of a BA.2 S protein ectodomain and compared these to previously determined BA.1 S structures. BA.2 Receptor Binding Domain (RBD) mutations induced remodeling of the internal RBD structure resulting in its improved thermostability and tighter packing within the 3-RBD-down spike. In the S2 subunit, the fusion peptide in the BA.2 was less accessible to antibodies than in BA.1. Pseudovirus neutralization and spike binding assays revealed extensive immune evasion while defining epitopes of two RBD-directed antibodies, DH1044 and DH1193, that bound the outer RBD face to neutralize both BA.1 and BA.2. Taken together, our results indicate that stabilization of the 3-RBD-down state through interprotomer RBD-RBD packing is a hallmark of the Omicron lineages, and reveal differences in key functional regions in the BA.1 and BA.2 S proteins.

Structural diversity of the SARS-CoV-2 Omicron spike (preprint)

Aided by extensive spike protein mutation, the SARS-CoV-2 Omicron variant overtook the previously dominant Delta variant. Spike conformation plays an essential role in SARS-CoV-2 evolution via changes in receptor binding domain (RBD) and neutralizing antibody epitope presentation affecting virus transmissibility and immune evasion. Here, we determine cryo-EM structures of the Omicron and Delta spikes to understand the conformational impacts of mutations in each. The Omicron spike structure revealed an unusually tightly packed RBD organization with long range impacts that were not observed in the Delta spike. Binding and crystallography revealed increased flexibility at the functionally critical fusion peptide site in the Omicron spike. These results reveal a highly evolved Omicron spike architecture with possible impacts on its high levels of immune evasion and transmissibility.

Human Mobility Data in the COVID-19 Pandemic: Characteristics, Applications, and Challenges (preprint)

The COVID-19 pandemic poses unprecedented challenges around the world. Many studies indicate that human mobility data provide significant support for public health actions during the pandemic. Researchers have applied mobility data to explore spatiotemporal trends over time, investigate associations with other variables, and predict or simulate the spread of COVID-19. Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks. We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective. We identified three major sources of mobility data: public transit systems, mobile operators, and mobile phone applications. Four approaches have been commonly used to estimate human mobility: public transit-based flow, social activity patterns, index-based mobility data, and social media-derived mobility data. We compared mobility datasets’ characteristics by assessing data privacy, quality, space-time coverage, high-performance data storage and processing, and accessibility. We also present challenges and future directions of using mobility data. This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

Revealing public opinion towards COVID-19 vaccines using Twitter data in the United States: a spatiotemporal perspective (preprint)

Background: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States (US) and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective: The aim of this study is to investigate public opinion and perception on COVID-19 vaccines by investigating the spatiotemporal trends of their sentiment and emotion towards vaccines, as well as how such trends relate to popular topics on Twitter in the US. Methods: We collected over 300,000 geotagged tweets in the US from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified three phases along the pandemic timeline with the significant changes of public sentiment and emotion. We further linked the changes to eleven key events and major topics as the potential drivers to induce such changes via cloud mapping of keywords and topic modeling. Results: An increasing trend of positive sentiment in parallel with the decrease of negative sentiment are generally observed in most states, reflecting the rising confidence and anticipation of the public towards COVID-19 vaccines. The overall tendency of the eight types of emotion implies the trustiness and anticipation of the public to vaccination, accompanied by the mixture of fear, sadness and anger. Critical social/international events and/or the announcements of political leaders and authorities may have potential impacts on the public opinion on vaccines. These factors, along with important topics and manual reading of popular posts on eleven key events, help identify underlying themes and validate insights from the analysis. Conclusions: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics and promote the confidence of individuals within a certain region or community, towards vaccines.

ODT FLOW: A Scalable Platform for Extracting, Analyzing, and Sharing Multi-source Multi-scale Human Mobility (preprint)

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.

Measuring Global Multi-Scale Place Connectivity using Geotagged Social Media Data (preprint)

Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized. In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement. The multi-scale PCI, demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records (10 percent penetration in the US population) and Facebook's social connectedness index (SCI), a popular connectivity index based on social networks. We found that PCI has a strong boundary effect and that it generally follows the distance decay, although this force is weaker in more urbanized counties with a denser population. Our investigation further suggests that PCI has great potential in addressing real-world problems that require place connectivity knowledge, exemplified with two applications: 1) modeling the spatial spread of COVID-19 during the early stage of the pandemic and 2) modeling hurricane evacuation destination choice. The methodological and contextual knowledge of PCI, together with the launched visualization platform and open-sourced PCI datasets at various geographic levels, are expected to support research fields requiring knowledge in human spatial interactions.

ODT Flow Explorer: Extract, Query, and Visualize Human Mobility (preprint)

Understanding human mobility dynamics among places provides fundamental knowledge regarding their interactive gravity, benefiting a wide range of applications in need of prior knowledge in human spatial interactions. The ongoing COVID-19 pandemic uniquely highlights the need for monitoring and measuring fine-scale human spatial interactions. In response to the soaring needs of human mobility data under the pandemic, we developed an interactive geospatial web portal by extracting worldwide daily population flows from billions of geotagged tweets and United States (U.S.) population flows from SafeGraph mobility data. The web portal is named ODT (Origin-Destination-Time) Flow Explorer. At the core of the explorer is an ODT data cube coupled with a big data computing cluster to efficiently manage, query, and aggregate billions of OD flows at different spatial and temporal scales. Although the explorer is still in its early developing stage, the rapidly generated mobility flow data can benefit a wide range of domains that need timely access to the fine-grained human mobility records. The ODT Flow Explorer can be accessed via http://gis.cas.sc.edu/GeoAnalytics/od.html.

Time-series clustering for home dwell time during COVID-19: what can we learn from it? (preprint)

In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph, and further assess the statistical significance of sixteen demographic/socioeconomic variables from five major categories. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures. Policymakers need to carefully evaluate the inevitable trade-off among different groups, making sure the outcomes of their policies reflect interests of the socially disadvantaged groups. HighlightsO_LIWe perform a trend-driven analysis by conducting Kmeans time-series clustering using fine- grained home dwell time records from SafeGraph. C_LIO_LIWe find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order. C_LIO_LIThe results suggest that socially disadvantaged groups are less likely to follow the order to stay at home, potentially leading to more exposures to the COVID-19. C_LIO_LIPolicymakers need to make sure the outcomes of their policies reflect the interests of the disadvantaged groups. C_LI

The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic (preprint)

This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four sources: 1) Apple mobility trend reports, 2) Google community mobility reports, 3) mobility data from Descartes Labs, and 4) Twitter mobility calculated via weighted distance. We further design a Responsive Index (RI) based on the time series of mobility change percentages to quantify the general degree of mobility-based responsiveness to COVID-19 at the U.S. county level. We find statistically significant positive correlations in the RI between either two data sources, revealing their general similarity, albeit with varying Pearsons r coefficients. Despite the similarity, however, mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The positive correlation between RI and income at the county level is significant in all mobility datasets, suggesting that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. Most states present a positive difference in RI between their upper-income and lower-income counties, where diverging patterns in time series of mobility changes percentages can be found. To our best knowledge, this is the first study that cross-compares multi-source mobility datasets. The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity. HighlightsO_LIHuman mobility data provide valuable insight into how we adjust our travel behaviors during the COVID-19 pandemic. C_LIO_LIHuman mobility records from Descartes Labs, Apple, Google, and Twitter are compared. C_LIO_LIMulti-source mobility datasets well capture the general impact of COVID-19 pandemic on mobility in the U.S. but present unique and even contrasting characteristics C_LIO_LIThe proposed responsive index quantifies the level of mobility-based reaction in response to the COVID-19 pandemic C_LIO_LIAll selected mobility datasets suggest a statistically significant positive correlation between the responsive index and median income at the U.S. county level. C_LI

Twitter, human mobility, and COVID-19 (preprint)

The outbreak of COVID-19 highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has been proved to be associated with the viral transmission. In this study, we analyzed 587 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and the U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographical regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amendable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people's travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts varied substantially among states. The strong mobility recovering momentum is further fueled by the Black Lives Matter protests, potentially fostering the second wave of infections in the U.S.