Adrenal tropism of SARS-CoV-2 and adrenal findings in a post-mortem case series of patients with severe fatal COVID-19

Progressive respiratory failure and hyperinflammatory response is the primary cause of death in the coronavirus disease 2019 (COVID-19) pandemic. Despite mounting evidence of disruption of the hypothalamus-pituitary-adrenal axis in COVID-19, relatively little is known about the tropism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to adrenal glands and associated changes. Here we demonstrate adrenal viral tropism and replication in COVID-19 patients. Adrenal glands showed inflammation accompanied by inflammatory cell death. Histopathologic analysis revealed widespread microthrombosis and severe adrenal injury. In addition, activation of the glycerophospholipid metabolism and reduction of cortisone intensities were characteristic for COVID-19 specimens. In conclusion, our autopsy series suggests that SARS-CoV-2 facilitates the induction of adrenalitis. Given the central role of adrenal glands in immunoregulation and taking into account the significant adrenal injury observed, monitoring of developing adrenal insufficiency might be essential in acute SARS-CoV-2 infection and during recovery.</p

Driver-Vehicle Interaction in Automated Driving : Overcoming System Boundaries via Driver Involvement

Automated driving is becoming feasible in more and more situations. However, there remain situations automated systems are not able to manage at all or at least not in a similarly efficient way than a human driver can do. This thesis investigates interaction concepts to involve users when reaching system boundaries. Two different kinds of system boundaries are considered: first, inescapable system boundaries that require a handover of the control to the user; second, less severe system weaknesses are investigated—the automated system is still able to operate but less efficient than a human driver. I developed interaction concepts to overcome these system boundaries and weaknesses, implemented prototypes, evaluated these in driving simulator studies, and derived insights and lessons learned for the implementation of driver-vehicle interaction in automated driving. Handovers to the user have been investigated for situations in which an automated vehicle reaches boundaries that are inescapable for it. Automation changes the tasks a user has to perform while driving. As a consequence, human factor issues arise that can make such handovers challenging. I conducted an experiment that investigates whether users can manage unforeseen traffic situations right after they have taken over control. Participants of the driving simulator study took over control within three seconds and were able to pass a broken down car in dense fog, but taking an unexpected sharp curve was challenging for some participants. Consequently, handovers should be avoided if possible. A less binary approach—automated or manual driving—to overcome system weaknesses is driver-vehicle cooperation. Automated system and its user are perceived as a team combining each other's strengths. In contrast to the related work, in which cooperation is seen as a part of shared control, in this thesis' definition of cooperation, the interaction between the two agents is only implemented on higher levels of the driving task—lateral and longitudinal control is excluded explicitly. Consequently, the user is involved in the navigation and guidance level. This has the advantage that human factor issues that are particularly severe when the user controls the vehicle are avoided. I investigated the feasibility of this interaction paradigm in four different cooperation manifestations: object recognition, situation recognition and maneuver selection, situation evolvement prediction, and maneuver approval in complex and dynamic situations. The feasibility of cooperation as a means to overcome system weaknesses has been proven in six driving simulator studies that evaluated different prototypes. Besides, the interaction via speech and a touch screen in the center console also the integration of the cooperation interface into the phone of users was explored. Moreover, I investigated eye tracking as an indicator of users' responsible acting and cooperation readiness. The findings of the studies prove the willingness of users to help out automated vehicles as well as the good usability of the different prototypes and showcase how erroneous maneuver approvals can be canceled easily and quickly. Nevertheless, based on the observations made in the studies I recommend implementing driver monitoring to ensure that users behave as intended and to implement bidirectional cooperation in which the system can support the user for instance by guiding the attention to relevant entities in the environment. In conclusion, this thesis proves the feasibility of handovers to overcome system boundaries but also highlights their challenges. Consequently, control-level-free cooperation concepts are suggested to overcome system weaknesses. Finally, lessons learned are derived on which academics and practitioners can build upon

Stuck behind a truck

Even though automated driving seems to be a promising approach, an arising problem is the cars possible disability to fulfill the needed requirements efficiently in specific situations. Recent research is addressing driver take-over requests coping with these limitations. A more efficient way is to support the vehicle cooperatively and address the particular requirements that the car cannot meet sufficiently. Based on the examination of human information processing and behavioral execution processes a new interface framework, which relies on learned behavior patterns, is presented. A simulator study compared touch interaction with the proposed approach and a gesture control for maneuver initiation without a complete take-over. The new approach seems to improve the assurance behavior and proper situation awareness, whereas usability mistakes were found in the practical implementation of the new interaction concept