HR121 targeting HR2 domain in S2 subunit of spike protein can serve as a broad-spectrum SARS-CoV-2 inhibitor via intranasal administration.

The continuously emerging SARS-CoV-2 variants pose a great challenge to the efficacy of current drugs, this necessitates the development of broad-spectrum antiviral drugs. In the previous study, we designed a recombinant protein, heptad repeat (HR) 121, as a variant-proof vaccine. Here, we found it can act as a fusion inhibitor and demonstrated broadly neutralizing activities against SARS-CoV-2 and its main variants. Structure analysis suggested that HR121 targets the HR2 domain in SARS-CoV-2 spike (S) 2 subunit to block virus-cell fusion. Functional experiments demonstrated that HR121 can bind HR2 at serological-pH and endosomal-pH, highlighting its inhibition capacity when SARS-CoV-2 enters via either cellular membrane fusion or endosomal route. Importantly, HR121 can effectively inhibit SARS-CoV-2 and Omicron variant pseudoviruses entering the cells, as well as block authentic SARS-CoV-2 and Omicron BA.2 replications in human pulmonary alveolar epithelial cells. After intranasal administration to Syrian golden hamsters, it can protect hamsters from SARS-CoV-2 and Omicron BA.2 infection. Together, our results suggest that HR121 is a potent drug candidate with broadly neutralizing activities against SARS-CoV-2 and its variants.

Chest‐Laminated and Sweat‐Permeable E‐Skin for Speech and Motion Artifact‐Insensitive Cough Detection

Assessment of the cough severity is essential when dealing with respiratory diseases such as chronic obstructive pulmonary disease and COVID‐19. Although a few wearable devices have been reported for cough detection, they mostly rely on microphones, accelerometers, or throat‐fixed flexible sensors, which suffer from key issues including privacy disclosure and speech/motion artifacts. This study presents a chest‐laminated electronic skin (e‐skin) for reliable cough detection. Mixed dumbbell‐like networks and through‐holes are engineered on hard‐to‐stretch composite films for high stretching force sensitivity and sweat permeation, respectively. The e‐skin can effectively reduce speech‐signal and motion artifacts owing to firm adhesion and conformal contact with the chest even on sweaty skin. Experimental results show that the specificity for cough identification is as high as 99.75% through machine learning of automated acoustic analysis, even in the presence of hard‐to‐distinguish daily activities such as throat clearing. The developed chest‐laminated e‐skin is a simple, comfortable, yet reliable method to detect cough for the primary diagnosis of respiratory diseases by extracting subtle acoustic information from cough. [ABSTRACT FROM AUTHOR] Copyright of Advanced Materials Technologies is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Significant neutralizing escapes of Omicron and its sublineages in SARS-CoV-2-infected individuals vaccinated with inactivated vaccines.

In China, most SARS-CoV-2-infected individuals had been vaccinated with inactivated vaccines. However, little is known about their immune resistances to the previous variants of concerns (VOCs) and the current Omicron sublineages. Here, we collected convalescent serum samples from SARS-CoV-2-infected individuals during the ancestral, Delta, and Omicron BA.1 waves, and evaluated their cross-neutralizing antibodies (nAbs) against the previous VOCs and the current Omicron sublineages using VSV-based pseudoviruses. In the convalescents who had been unvaccinated and vaccinated with two doses of inactivated vaccines, we found infections from either the ancestral or the Delta strain elicited moderate cross-nAbs to previous VOCs, but very few cross-nAbs to the Omicron sublineages, including BA.1, BA.2, BA.3, and BA.4/5. The individuals who had been vaccinated with two doses of inactivated vaccines before Omicron BA.1 infection had moderate nAbs to Omicron BA.1, but weak cross-nAbs to the other Omicron sublineages. While three doses of inactivated vaccines followed Omicron BA.1 infection induced elevated and still weak cross-nAbs to other Omicron sublineages. Our results indicate that the Omicron sublineages show significant immune escape in the previously SARS-CoV-2-infected individuals and thus highlights the importance of vaccine boosters in this population.

Extracellular vesicles mediate antibody-resistant transmission of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. Antibody resistance dampens neutralizing antibody therapy and threatens current global Coronavirus (COVID-19) vaccine campaigns. In addition to the emergence of resistant SARS-CoV-2 variants, little is known about how SARS-CoV-2 evades antibodies. Here, we report a novel mechanism of extracellular vesicle (EV)-mediated cell-to-cell transmission of SARS-CoV-2, which facilitates SARS-CoV-2 to escape from neutralizing antibodies. These EVs, initially observed in SARS-CoV-2 envelope protein-expressing cells, are secreted by various SARS-CoV-2-infected cells, including Vero E6, Calu-3, and HPAEpiC cells, undergoing infection-induced pyroptosis. Various SARS-CoV-2-infected cells produce similar EVs characterized by extra-large sizes (1.6-9.5 µm in diameter, average diameter > 4.2 µm) much larger than previously reported virus-generated vesicles. Transmission electron microscopy analysis and plaque assay reveal that these SARS-CoV-2-induced EVs contain large amounts of live virus particles. In particular, the vesicle-cloaked SARS-CoV-2 virus is resistant to neutralizing antibodies and able to reinfect naïve cells independent of the reported receptors and cofactors. Consistently, the constructed 3D images show that intact EVs could be taken up by recipient cells directly, supporting vesicle-mediated cell-to-cell transmission of SARS-CoV-2. Our findings reveal a novel mechanism of receptor-independent SARS-CoV-2 infection via cell-to-cell transmission, provide new insights into antibody resistance of SARS-CoV-2 and suggest potential targets for future antiviral therapeutics.

A variant-proof SARS-CoV-2 vaccine targeting HR1 domain in S2 subunit of spike protein.

The emerging SARS-CoV-2 variants, commonly with many mutations in S1 subunit of spike (S) protein are weakening the efficacy of the current vaccines and antibody therapeutics. This calls for the variant-proof SARS-CoV-2 vaccines targeting the more conserved regions in S protein. Here, we designed a recombinant subunit vaccine, HR121, targeting the conserved HR1 domain in S2 subunit of S protein. HR121 consisting of HR1-linker1-HR2-linker2-HR1, is conformationally and functionally analogous to the HR1 domain present in the fusion intermediate conformation of S2 subunit. Immunization with HR121 in rabbits and rhesus macaques elicited highly potent cross-neutralizing antibodies against SARS-CoV-2 and its variants, particularly Omicron sublineages. Vaccination with HR121 achieved near-full protections against prototype SARS-CoV-2 infection in hACE2 transgenic mice, Syrian golden hamsters and rhesus macaques, and effective protection against Omicron BA.2 infection in Syrian golden hamsters. This study demonstrates that HR121 is a promising candidate of variant-proof SARS-CoV-2 vaccine with a novel conserved target in the S2 subunit for application against current and future SARS-CoV-2 variants.

Chest‐Laminated and Sweat‐Permeable E‐Skin for Speech and Motion Artifact‐Insensitive Cough Detection

Assessment of the cough severity is essential when dealing with respiratory diseases such as chronic obstructive pulmonary disease and COVID‐19. Although a few wearable devices have been reported for cough detection, they mostly rely on microphones, accelerometers, or throat‐fixed flexible sensors, which suffer from key issues including privacy disclosure and speech/motion artifacts. This study presents a chest‐laminated electronic skin (e‐skin) for reliable cough detection. Mixed dumbbell‐like networks and through‐holes are engineered on hard‐to‐stretch composite films for high stretching force sensitivity and sweat permeation, respectively. The e‐skin can effectively reduce speech‐signal and motion artifacts owing to firm adhesion and conformal contact with the chest even on sweaty skin. Experimental results show that the specificity for cough identification is as high as 99.75% through machine learning of automated acoustic analysis, even in the presence of hard‐to‐distinguish daily activities such as throat clearing. The developed chest‐laminated e‐skin is a simple, comfortable, yet reliable method to detect cough for the primary diagnosis of respiratory diseases by extracting subtle acoustic information from cough. [ FROM AUTHOR]

From transparency to accountability of intelligent systems: Moving beyond aspirations

A number of governmental and nongovernmental organizations have made significant efforts to encourage the development of artificial intelligence in line with a series of aspirational concepts such as transparency, interpretability, explainability, and accountability. The difficulty at present, however, is that these concepts exist at a fairly level, whereas in order for them to have the tangible effects desired they need to become more concrete and specific. This article undertakes precisely this process of concretisation, mapping how the different concepts interrelate and what in particular they each require in order to move from being high-level aspirations to detailed and enforceable requirements. We argue that the key concept in this process is accountability, since unless an entity can be held accountable for compliance with the other concepts, and indeed more generally, those concepts cannot do the work required of them. There is a variety of taxonomies of accountability in the literature. However, at the core of each account appears to be a sense of “answerability”;a need to explain or to give an account. It is this ability to call an entity to account which provides the impetus for each of the other concepts and helps us to understand what they must each require.

Supporting COVID-19 policy response with large-scale mobility-based modeling (preprint)

ABSTRACT Social distancing measures, such as restricting occupancy at venues, have been a primary intervention for controlling the spread of COVID-19. However, these mobility restrictions place a significant economic burden on individuals and businesses. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures.In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an intuitive dashboard interface that communicates our model’s predictions for thousands of potential policies, tailored to their jurisdiction. The resulting decision-support environment provides policymakers with much-needed analytical machinery to assess the tradeoffs between future infections and mobility restrictions.

Mobility network models of COVID-19 explain inequities and inform reopening.

The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.

Infection with novel coronavirus (SARS-CoV-2) causes pneumonia in Rhesus macaques.

The 2019 novel coronavirus (SARS-CoV-2) outbreak is a major challenge for public health. SARS-CoV-2 infection in human has a broad clinical spectrum ranging from mild to severe cases, with a mortality rate of ~6.4% worldwide (based on World Health Organization daily situation report). However, the dynamics of viral infection, replication and shedding are poorly understood. Here, we show that Rhesus macaques are susceptible to the infection by SARS-CoV-2. After intratracheal inoculation, the first peak of viral RNA was observed in oropharyngeal swabs one day post infection (1 d.p.i.), mainly from the input of the inoculation, while the second peak occurred at 5 d.p.i., which reflected on-site replication in the respiratory tract. Histopathological observation shows that SARS-CoV-2 infection can cause interstitial pneumonia in animals, characterized by hyperemia and edema, and infiltration of monocytes and lymphocytes in alveoli. We also identified SARS-CoV-2 RNA in respiratory tract tissues, including trachea, bronchus and lung; and viruses were also re-isolated from oropharyngeal swabs, bronchus and lung, respectively. Furthermore, we demonstrated that neutralizing antibodies generated from the primary infection could protect the Rhesus macaques from a second-round challenge by SARS-CoV-2. The non-human primate model that we established here provides a valuable platform to study SARS-CoV-2 pathogenesis and to evaluate candidate vaccines and therapeutics.

Mobility network modeling explains higher SARS-CoV-2 infection rates among disadvantaged groups and informs reopening strategies (preprint)

Fine-grained epidemiological modeling of the spread of SARS-CoV-2 -- capturing who is infected at which locations -- can aid the development of policy responses that account for heterogeneous risks of different locations as well as the disparities in infections among different demographic groups. Here, we develop a metapopulation SEIR disease model that uses dynamic mobility networks, derived from US cell phone data, to capture the hourly movements of millions of people from local neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants, grocery stores, or religious establishments. We simulate the spread of SARS-CoV-2 from March 1-May 2, 2020 among a population of 105 million people in 10 of the largest US metropolitan statistical areas. We show that by integrating these mobility networks, which connect 60k CBGs to 565k POIs with a total of 5.4 billion hourly edges, even a relatively simple epidemiological model can accurately capture the case trajectory despite dramatic changes in population behavior due to the virus. Furthermore, by modeling detailed information about each POI, like visitor density and visit length, we can estimate the impacts of fine-grained reopening plans: we predict that a small minority of "superspreader" POIs account for a large majority of infections, that reopening some POI categories (like full-service restaurants) poses especially large risks, and that strategies restricting maximum occupancy at each POI are more effective than uniformly reducing mobility. Our models also predict higher infection rates among disadvantaged racial and socioeconomic groups solely from differences in mobility: disadvantaged groups have not been able to reduce mobility as sharply, and the POIs they visit (even within the same category) tend to be smaller, more crowded, and therefore more dangerous. By modeling who is infected at which locations, our model supports fine-grained analyses that can inform more effective and equitable policy responses to SARS-CoV-2.