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Type 2 diabetes mellitus (DM) patients are at high risk for the development of severe COVID-19. Euglycemic diabetic ketoacidosis (eu-DKA) is a rare life-threatening complication associated with the use of SGLT2 inhibitor that may be unnoticed, particularly in a pandemic setting, due to the absence of significant hyperglycemia, delaying its treatment. In this report, we describe a case of a 56-year-old patient who presented an elevated anion gap metabolic acidosis during a SARS-CoV-2 infection and was diagnosed with SGLT2-associated euglycemic diabetic ketoacidosis. COVID-19 may increase patients’ insulin demand, present gastrointestinal symptoms, and increase the production of ketone bodies. This situation can be worsened in susceptible diabetic patients on SLGT2 inhibitors, due to the persistent glycosuria, which can cause volume depletion. Recently some authors recommended that insulin-deficient patients or those using SGLT2 inhibitors should monitor for ketosis using available home testing kits in case of infections and should discontinue the medication in case of COVID-19. Given the increased use of this drug class in the management of type 2 DM patients due to its reduction of cardiovascular risk, we set out to emphasize the importance for the medical community to consider the possibility of eu-DKA on SARS-CoV-2-infected patients using SLGT2 inhibitors, so physicians can provide these patients with appropriate therapy promptly.

Central to grid processing is the scheduling of application tasks to resources. Schedulers need to consider heterogeneous computational and communication resources, producing the shortest possible schedule under time constraints dictated by both the application needs and the frequency of fluctuation of resource availability. This paper introduces a set of schedulers with such characteristics.

Cloud Radio Access Networks (CRAN) allow to reduce power consumption in future 5G networks by decoupling BaseBand Units (BBU) from cell sites and centralizing the baseband processing from Remote Radio-Heads (RRH) in BBUs pools in a cloud. Although this centralization can enable power savings, it imposes much higher traffic on the optical transport network used to connect RRHs to the BBU pool, i.e., the fronthaul. In this paper we propose a hybrid Cloud-Fog RAN (CF-RAN) architecture that resorts to fog computing and to Network Functions Virtualization (NFV) to replicate the processing capacity of CRAN in local fog nodes closer to the RRHs that can be activated on demand to process surplus fronthaul/cloud traffic. We devise an ILP formulation and graph-based heuristics to decide when to activate fog nodes and how to dimension wavelengths on a Timeand-Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) to support the fronthaul. Our results show that our architecture can consume up to 96% less energy than a traditional Distributed RAN (DRAN), providing a maximum transmission latency of about 20µs between RRHs and BBUs even in large traffic scenarios. Moreover, we demonstrate that our graph-based heuristics can achieve same optimal solutions of the ILP formulation but with a reduction of 99.86% in the execution time.

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