BackgroudEffective communication of accurate information through social media constitutes an important component of public health interventions in modern time, when traditional public health approaches such as contact tracing, quarantine and isolation are among the few options for the containing the disease spread in the population. The success of control of COVID-19 outbreak started from Wuhan, the capital city of Hubei Province of China relies heavily on the resilience of residents to follow public health interventions which induce substantial interruption of social-economic activities, and evidence shows that opinion leaders have been playing significant roles in the propagation of epidemic information and public health policy and implementations.MethodsWe design a mathematical model to quantify the roles of information superspreaders in single specific information which outbreaks rapidly and usually has a short duration period, and to examine the information propagation dynamics in the Chinese Sina-microblog. Our opinion-leader susceptible-forwarding-immune (OL-SFI) model is formulated to track the temporal evolution of forwarding quantities generated by opinion leaders and normal users.ResultsData fitting from the real data of COVID-19 obtained from Chinese Sina-microblog can identify the different contact rates and forwarding probabilities (and hence calculate the basic information forwarding reproduction number of superspreaders), and can be used to evaluate the roles of opinion leaders in different stages of the information propagation and the outbreak unfolding.ConclusionsThe parameterized model can be used to nearcast the information propagation trend, and the model-based sensitivity analysis can help to explore important factors for the roles of opinion leaders.
Rapid, mass diagnosis of the coronavirus disease 2019 (COVID-19) is critical to stop the ongoing infection spread. The two standard screening methods to confirm the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are polymerase chain reaction (PCR), through the RNA of the virus, and serology by detecting antibodies produced as a response to the viral infection. However, given the detection complexity, cost and relatively long analysis times of these techniques, novel technologies are urgently needed. Here, we report an aptamer-based biosensor developed on a screen-printed carbon electrode platform for rapid, sensitive, and user-friendly detection of SARS-CoV-2. The aptasensor relies on an aptamer targeting the receptor-binding domain (RBD) in the spike protein (S-protein) of the SARS-CoV-2. The aptamer immobilization on gold nanoparticles, and the presence of S-protein in the aptamer-target complex, investigated for the first time by photo-induced force microscopy mapping between 770 and 1910 cm-1 of the electromagnetic spectrum, revealed abundant S-protein homogeneously distributed on the sensing probe. The detection of SARS-CoV-2 S-protein was achieved by electrochemical impedance spectroscopy after 40 min incubation with several analyte concentrations, yielding a limit of detection of 1.30 pM (66 pg/mL). Moreover, the aptasensor was successfully applied for the detection of a SARS-CoV-2 pseudovirus, thus suggesting it is a promising tool for the diagnosis of COVID-19.
AbstractDuring public health crises like the COVID-19 pandemic, ultraviolet-C (UV-C) decontamination of N95 respirators for emergency reuse has been implemented to mitigate shortages. Pathogen photoinactivation efficacy depends critically on UV-C dose, which is distance- and angle-dependent and thus varies substantially across N95 surfaces within a decontamination system. Due to nonuniform and system-dependent UV-C dose distributions, characterizing UV-C dose and resulting pathogen inactivation with sufficient spatial resolution on-N95 is key to designing and validating UV-C decontamination protocols. However, robust quantification of UV-C dose across N95 facepieces presents challenges, as few UV-C measurement tools have sufficient (1) small, flexible form factor, and (2) angular response. To address this gap, we combine optical modeling and quantitative photochromic indicator (PCI) dosimetry with viral inactivation assays to generate high-resolution maps of “on-N95” UV-C dose and concomitant SARS-CoV-2 viral inactivation across N95 facepieces within a commercial decontamination chamber. Using modeling to rapidly identify on-N95 locations of interest, in-situ measurements report a 17.4 ± 5.0-fold dose difference across N95 facepieces in the chamber, yielding 2.9 ± 0.2-log variation in SARS-CoV-2 inactivation. UV-C dose at several on-N95 locations was lower than the lowest-dose locations on the chamber floor, highlighting the importance of on-N95 dose validation. Overall, we integrate optical simulation with in-situ PCI dosimetry to relate UV-C dose and viral inactivation at specific on-N95 locations, establishing a versatile approach to characterize UV-C photoinactivation of pathogens contaminating complex substrates such as N95s.
COVID-19 has spread globally since its discovery in Hubei province, China in December 2019. A combination of computed tomography imaging, whole genome sequencing, and electron microscopy were initially used to screen and identify SARS-CoV-2, the viral etiology of COVID-19. The aim of this review article is to inform the audience of diagnostic and surveillance technologies for SARS-CoV-2 and their performance characteristics. We describe point-of-care diagnostics that are on the horizon and encourage academics to advance their technologies beyond conception. Developing plug-and-play diagnostics to manage the SARS-CoV-2 outbreak would be useful in preventing future epidemics.
Abstract Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease, and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become overwhelmed. To this end, we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from COVID-19. We trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal, pulmonary, immune, cardiac, and hepatic systems. To greatly enhance the speed and utility of our model, we applied an artificial intelligence method to segment and classify regions on CT imaging, from which interpretable data could be directly fed into the predictive machine learning model for overall recovery. Across all organ systems we achieved validation set area under the receiver operator characteristic curve (AUC) values for organ-specific recovery ranging from 0.80 to 0.89, and significant overall recovery prediction in Kaplan-Meier analyses. This demonstrates that the synergistic use of an artificial intelligence (AI) framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.
Coronavirus Disease 2019 (COVID-19) is caused by the novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) - the culprit of an ongoing pandemic responsible for the loss of over 3 million lives worldwide within a year and a half. While the majority of SARS-CoV-2 infected people develop no or mild symptoms, some become severely ill and may die from COVID-19-related complications. In this review, we compile and comment on a number of biomarkers that have been identified and are expected to enhance the detection, protection and treatment of individuals at high risk of developing severe illnesses, as well as enable the monitoring of COVID-19 prognosis and responsiveness to therapeutic interventions. Consistent with the emerging notion that the majority of COVID-19 deaths occur in older and frail individuals, we researched the scientific literature and report the identification of a subset of COVID-19 biomarkers indicative of increased vulnerability to developing severe COVID-19 in older and frail patients. Mechanistically, increased frailty results from reduced disease tolerance, a phenomenon aggravated by ageing and comorbidities. While biomarkers of ageing and frailty may predict COVID-19 severity, biomarkers of disease tolerance may predict resistance to COVID-19 with socio-economic factors such as access to adequate health care remaining as major non-biomolecular influencers of COVID-19 outcomes. Graphical Abstract Figure: Biomarkers of ageing and frailty may predict COVID-19 severity as both conditions are associated with reduced disease tolerance - the host’s defense mechanisms to limit tissue damage or reduce immunopathology induced by the infection with a pathogen. While these biomolecular markers inform about the baseline ground for exacerbated viral infection, inflammaging and pre-existing comorbidities, which are common at advanced ages, as well as socio-economic conditions that affect people in underdeveloped nations and underserved communities of developed nations appear to be strong influencers of COVID-19 trajectory - particularly in older and frail individuals.ga1
Protein synthesis can be segmented into distinct phases comprising mRNA translation initiation, elongation, and termination. Translation initiation is a highly regulated and rate-limiting step of protein synthesis that requires more than 12 eukaryotic initiation factors (eIFs). Extensive evidence shows that the transcriptome and corresponding proteome do not invariably correlate with each other in a variety of contexts. In particular, translation of mRNAs specific to angiogenesis, tumor development, and apoptosis is altered during physiological and pathophysiological stress conditions. In cancer cells, the expression and functions of eIFs are hampered, resulting in the inhibition of global translation and enhancement of translation of subsets of mRNAs by alternative mechanisms. A precise understanding of mechanisms involving eukaryotic initiation factors leading to differential protein expression can help us to design better strategies to diagnose and treat cancer. The high spatial and temporal resolution of translation control can have an immediate effect on the microenvironment of the cell in comparison with changes in transcription. The dysregulation of mRNA translation mechanisms is increasingly being exploited as a target to treat cancer. In this review, we will focus on this context by describing both canonical and noncanonical roles of eIFs, which alter mRNA translation.
International audience; Overabundant species can have major impacts on their habitat and induce trophic cascades within ecosystems. In North America, the overabundant greater snow goose (Anser caerulescens atlanticus) has been successfully controlled through special spring hunting regulations since 1999. Hunting is a source of mortality but also of disturbance, which affects the behavior and nutrient storage dynamics of staging snow geese. In 2020, the lockdown imposed by the COVID19 pandemic reduced hunting activity during their migratory stopover in Québec by at least 31%. This provided a unique opportunity to assess the effects of a sudden reduction in hunting disturbance on geese. We used long-term data on body mass combined with movement data from GPS-tracked birds in 2019 and 2020 to assess the effects of the 2020 lockdown on the spring body condition and behavior of greater snow geese. Body condition was higher in 2020 than in all years since the inception of spring hunting in 1999, except for 2019. However, in 2020 geese reached maximal body condition earlier during the staging period than in any other year and reduced by half time spent feeding in highly profitable but risky agricultural habitat in late spring compared to 2019. Although our study was not designed to evaluate the effects of the lockdown, the associated reduction in disturbance in 2020 supports the hypothesis that hunting-related disturbance negatively affects foraging efficiency and body condition in geese. Since spring body condition is related to subsequent breeding success, the lockdown could increase productivity in this overabundant population.
Wayne Vuong; Conrad Fischer; Muhammad Bashir Khan; Marco J. van Belkum; Tess Lamer; Kurtis D. Willoughby; Jimmy Lu; Elena Arutyunova; Michael A. Joyce; Holly A. Saffran; +6 more
Wayne Vuong; Conrad Fischer; Muhammad Bashir Khan; Marco J. van Belkum; Tess Lamer; Kurtis D. Willoughby; Jimmy Lu; Elena Arutyunova; Michael A. Joyce; Holly A. Saffran; Justin Shields; Howard S. Young; James A. Nieman; D. Lorne Tyrrell; M. Joanne Lemieux; John C. Vederas;
Replication of SARS-CoV-2, the coronavirus causing COVID-19, requires a main protease (Mpro) to cleave viral proteins. Consequently, Mpro is a target for antiviral agents. We and others previously demonstrated that GC376, a bisulfite prodrug with efficacy as an anti-coronaviral agent in animals, is an effective inhibitor of Mpro in SARS-CoV-2. Here, we report structure-activity studies of improved GC376 derivatives with nanomolar affinities and therapeutic indices >200. Crystallographic structures of inhibitor-Mpro complexes reveal that an alternative binding pocket in Mpro, S4, accommodates the P3 position. Alternative binding is induced by polar P3 groups or a nearby methyl. NMR and solubility studies with GC376 show that it exists as a mixture of stereoisomers and forms colloids in aqueous media at higher concentrations, a property not previously reported. Replacement of its Na+ counter ion with choline greatly increases solubility. The physical, biochemical, crystallographic, and cellular data reveal new avenues for Mpro inhibitor design. Graphical abstract Image 1
Bo Pang; Jingyang Xu; Yanming Liu; Hanyong Peng; Wei Feng; Yiren Cao; Jinjun Wu; Huyan Xiao; Kanti Pabbaraju; Graham Tipples; +5 more
Bo Pang; Jingyang Xu; Yanming Liu; Hanyong Peng; Wei Feng; Yiren Cao; Jinjun Wu; Huyan Xiao; Kanti Pabbaraju; Graham Tipples; Michael A. Joyce; Holly A. Saffran; D. Lorne Tyrrell; Hongquan Zhang; X. Chris Le;
We have developed a single-tube assay for SARS-CoV-2 in patient samples. This assay combined advantages of reverse transcription (RT) loop-mediated isothermal amplification (LAMP) with clustered regularly interspaced short palindromic repeats (CRISPRs) and the CRISPR-associated (Cas) enzyme Cas12a. Our assay is able to detect SARS-CoV-2 in a single tube within 40 min, requiring only a single temperature control (62 °C). The RT-LAMP reagents were added to the sample vial, while CRISPR Cas12a reagents were deposited onto the lid of the vial. After a half-hour RT-LAMP amplification, the tube was inverted and flicked to mix the detection reagents with the amplicon. The sequence-specific recognition of the amplicon by the CRISPR guide RNA and Cas12a enzyme improved specificity. Visible green fluorescence generated by the CRISPR Cas12a system was recorded using a smartphone camera. Analysis of 100 human respiratory swab samples for the N and/or E gene of SARS-CoV-2 produced 100% clinical specificity and no false positive. Analysis of 50 samples that were detected positive using reverse transcription quantitative polymerase chain reaction (RT-qPCR) resulted in an overall clinical sensitivity of 94%. Importantly, this included 20 samples that required 30–39 threshold cycles of RT-qPCR to achieve a positive detection. Integration of the exponential amplification ability of RT-LAMP and the sequence-specific processing by the CRISPR-Cas system into a molecular assay resulted in improvements in both analytical sensitivity and specificity. The single-tube assay is beneficial for future point-of-care applications.