A single intranasal administration of AdCOVID protects against SARS-CoV-2 infection in the upper and lower respiratory tracts.

The coronavirus disease 2019 (COVID-19) pandemic has illustrated the critical need for effective prophylactic vaccination to prevent the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Intranasal vaccination is an attractive approach for preventing COVID-19 as the nasal mucosa is the site of initial SARS-CoV-2 entry and viral replication prior to aspiration into the lungs. We previously demonstrated that a single intranasal administration of a candidate adenovirus type 5-vectored vaccine encoding the receptor-binding domain of the SARS-CoV-2 spike protein (AdCOVID) induced robust immunity in both the airway mucosa and periphery, and completely protected K18-hACE2 mice from lethal SARS-CoV-2 challenge. Here we show that a single intranasal administration of AdCOVID limits viral replication in the nasal cavity of K18-hACE2 mice. AdCOVID also induces sterilizing immunity in the lungs of mice as reflected by the absence of infectious virus. Finally, AdCOVID prevents SARS-CoV-2 induced pathological damage in the lungs of mice. These data show that AdCOVID not only limits viral replication in the respiratory tract, but it also prevents virus-induced inflammation and immunopathology following SARS-CoV-2 infection.

An Unexpected Occurrence of Malignancy in a Patient After a Fontan Operation.

An 18-year-old male with complex single ventricle physiology status post Fontan and Kawashima procedures who presented with progressive dyspnea was found to have severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rhinovirus, and a new retroperitoneal mass. Biopsy of the retroperitoneal mass revealed a mixed germ cell tumor with areas consistent with choriocarcinoma. Imaging showed metastatic disease, including to the lungs which ultimately led to worsening respiratory failure that required intubation and ultimately, death.

A combined optimization-simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities.

The timely handling of passengers is critical to efficient airport and airline operations. The pandemic requirements mandate adapted process designs and handling procedures to maintain and improve operational performance. Passenger activities in the confined aircraft cabin must be evaluated for potential virus transmission, and boarding procedures should be designed to minimize the negative impact on passengers and operations. In our approach, we generate an optimized seat allocation that considers passengers' physical activities when they store their hand luggage items in the overhead compartment. We proposed a mixed-integer programming formulation including the concept of shedding rates to determine and minimize the risk of virus transmission by solving the NP-hard seat assignment problem. We are improving the already efficient outside-in boarding, where passengers in the window seat board first and passengers in the aisle seat board last, taking into account COVID-19 regulations and the limited capacity of overhead compartments. To demonstrate and evaluate the improvements achieved in aircraft boarding, a stochastic agent-based model is used in which three operational scenarios with seat occupancy of 50%, 66%, and 80% are implemented. With our optimization approach, the average boarding time and the transmission risk are significantly reduced already for the general case, i.e., when no specific boarding order is specified (random boarding). If the already efficient outside-in boarding is used as a reference, the boarding time can be reduced by more than 30% by applying our approach, while keeping the transmission risk at the lowest level.

Leveraging OpenStreetMap and Multimodal Remote Sensing Data with Joint Deep Learning for Wastewater Treatment Plants Detection.

Humans rely on clean water for their health, well-being, and various socio-economic activities. During the past few years, the COVID-19 pandemic has been a constant reminder of about the importance of hygiene and sanitation for public health. The most common approach to securing clean water supplies for this purpose is via wastewater treatment. To date, an effective method of detecting wastewater treatment plants (WWTP) accurately and automatically via remote sensing is unavailable. In this paper, we provide a solution to this task by proposing a novel joint deep learning (JDL) method that consists of a fine-tuned object detection network and a multi-task residual attention network (RAN). By leveraging OpenStreetMap (OSM) and multimodal remote sensing (RS) data, our JDL method is able to simultaneously tackle two different tasks: land use land cover (LULC) and WWTP classification. Moreover, JDL exploits the complementary effects between these tasks for a performance gain. We train JDL using 4,187 WWTP features and 4,200 LULC samples and validate the performance of the proposed method over a selected area around Stuttgart with 723 WWTP features and 1,200 LULC samples to generate an LULC classification map and a WWTP detection map. Extensive experiments conducted with different comparative methods demonstrate the effectiveness and efficiency of our JDL method in automatic WWTP detection in comparison with single-modality/single-task or traditional survey methods. Moreover, lessons learned pave the way for future works to simultaneously and effectively address multiple large-scale mapping tasks (e.g., both mapping LULC and detecting WWTP) from multimodal RS data via deep learning.

NAD+ in COVID-19 and viral infections.

NAD+, as an emerging regulator of immune responses during viral infections, may be a promising therapeutic target for coronavirus disease 2019 (COVID-19). In this Opinion, we suggest that interventions that boost NAD+ levels might promote antiviral defense and suppress uncontrolled inflammation. We discuss the association between low NAD+ concentrations and risk factors for poor COVID-19 outcomes, including aging and common comorbidities. Mechanistically, we outline how viral infections can further deplete NAD+ and its roles in antiviral defense and inflammation. We also describe how coronaviruses can subvert NAD+-mediated actions via genes that remove NAD+ modifications and activate the NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) inflammasome. Finally, we explore ongoing approaches to boost NAD+ concentrations in the clinic to putatively increase antiviral responses while curtailing hyperinflammation.

Single-Dose Intranasal Administration of AdCOVID Elicits Systemic and Mucosal Immunity against SARS-CoV-2 and Fully Protects Mice from Lethal Challenge.

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the urgent need for effective prophylactic vaccination to prevent the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Intranasal vaccination is an attractive strategy to prevent COVID-19 as the nasal mucosa represents the first-line barrier to SARS-CoV-2 entry. The current intramuscular vaccines elicit systemic immunity but not necessarily high-level mucosal immunity. Here, we tested a single intranasal dose of our candidate adenovirus type 5-vectored vaccine encoding the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein (AdCOVID) in inbred, outbred, and transgenic mice. A single intranasal vaccination with AdCOVID elicited a strong and focused immune response against RBD through the induction of mucosal IgA in the respiratory tract, serum neutralizing antibodies, and CD4+ and CD8+ T cells with a Th1-like cytokine expression profile. A single AdCOVID dose resulted in immunity that was sustained for over six months. Moreover, a single intranasal dose completely protected K18-hACE2 mice from lethal SARS-CoV-2 challenge, preventing weight loss and mortality. These data show that AdCOVID promotes concomitant systemic and mucosal immunity and represents a promising vaccine candidate.

High Incidence of Epileptiform Potentials During Continuous EEG Monitoring in Critically Ill COVID-19 Patients.

Objective: To analyze continuous 1- or 2-channel electroencephalograms (EEGs) of mechanically ventilated patients with coronavirus disease 2019 (COVID-19) with regard to occurrence of epileptiform potentials. Design: Single-center retrospective analysis. Setting: Intensive care unit of Hannover Medical School, Hannover, Germany. Patients: Critically ill COVID-19 patients who underwent continuous routine EEG monitoring (EEG monitor: Narcotrend-Compact M) during sedation. Measurements and Main Results: Data from 15 COVID-19 patients (11 men, four women; age: 19-75 years) were evaluated. Epileptiform potentials occurred in 10 of 15 patients (66.7%). Conclusions: The results of the evaluation regarding the occurrence of epileptiform potentials show that there is an unusually high percentage of cerebral involvement in patients with severe COVID-19. EEG monitoring can be used in COVID-19 patients to detect epileptiform potentials.

Analytical approach to solve the problem of aircraft passenger boarding during the coronavirus pandemic.

The corona pandemic significantly changes the processes of aircraft and passenger handling at the airport. In our contribution, we focus on the time-critical process of aircraft boarding, where regulations regarding physical distances between passengers will significantly increase boarding time. The passenger behavior is implemented in a field-validated stochastic cellular automata model, which is extended by a module to evaluate the transmission risk. We propose an improved boarding process by considering that most of the passengers are travel together and should be boarded and seated as a group. The NP-hard seat allocation of groups with minimized individual interactions between groups is solved with a genetic algorithm. Then, the improved seat allocation is used to derive an associated boarding sequence aiming at both short boarding times and low risk of virus transmission. Our results show that the consideration of groups will significantly contribute to a faster boarding (reduction of time by about 60%) and less transmission risk (reduced by 85%) compared to the standard random boarding procedures applied in the pandemic scenario.

Evaluation of Technology-Supported Distance Measuring to Ensure Safe Aircraft Boarding during COVID-19 Pandemic

With the rise of COVID-19, the sustainability of air transport is a major challenge, as there is limited space in aircraft cabins, resulting in a higher risk of virus transmission. In order to detect possible chains of infection, technology-supported apps are used for social distancing. These COVID-19 applications are based on the display of the received signal strength for distance estimation, which is strongly influenced by the spreading environment due to the signal multipath reception. Therefore, we evaluate the applicability of technology-based social distancing methods in an aircraft cabin environment using a radio propagation simulation based on a three-dimensional aircraft model. We demonstrate the susceptibility to errors of the conventional COVID-19 distance estimation, which can lead to large errors in the determination of distances and to the impracticability of traditional tracing approaches during passenger boarding/deboarding. In the context of the future connected cabin, a robust distance measurement must be implemented to ensure safe travel. Finally, our results can be transferred to similar fields of application, e.g., trains or public transport.

Future aircraft turnaround operations considering post-pandemic requirements.

Air Transportation is a major contributor to international mobility and has high requirements to ensure safe and secure operations. Aircraft ground operations are impacted significantly by the current pandemic situation so that standard operating procedures need a redesign to incorporate the upcoming sanitation requirements. In particular, the passenger boarding process is challenged with requirements for physical distances between passengers, while in addition to standard cleaning, the cabin has to be disinfected after each flight. We evaluate potential alterations of these two aircraft cabin processes with respect to a pre-pandemic reference aircraft turnaround. The implementation of microscopic approaches allows to consider individual interactions and a step-wise process adaptation aiming for an efficient operational design. We find a significant extension of boarding times (more than doubled) if the physical distance rule is applied. The new disinfection process further extends the critical path of the turnaround, so we see a high impact on airport and airline operations. To compensate for the increased workload and process times, we provide an integrated cleaning and disinfection procedure with additional personnel. Our results indicate that the pre-pandemic turnaround times cannot be maintained for the same seat load, even if the process adaptations are being implemented. However, a seat allocation scheme with empty middle-seats (seat load of 67%) and the use of an apron position (additional use of rear aircraft door for boarding) enable pre-pandemic turnaround times without additional cleaning personnel. Aircraft turnarounds at terminal positions require between 10% (with additional personnel) and 20% (without additional personnel) more ground time.

Can artificial intelligence identify effective COVID-19 therapies?

In this issue of EMBO Molecular Medicine, Stebbing et al (2020b) validate an artificial intelligence-assisted prediction that a drug used to treat rheumatoid arthritis could be a potent weapon against COVID-19. Using liver organoids infected with SARS-CoV-2, they confirm dual antiviral and anti-inflammatory activities and show that its administration in four COVID-19 patients is correlated with disease improvement, paving the way for more rigorous placebo-controlled trials.