Disease Progression in MRL/lpr Lupus-Prone Mice Is Reduced by NCS 613, a Specific Cyclic Nucleotide Phosphodiesterase Type 4 (PDE4) Inhibitor

Systemic lupus erythematosus is a polymorphic and multigenic inflammatory autoimmune disease. Cyclic AMP (cAMP) modulates inflammation and the inhibition of cyclic nucleotide phosphodiesterase type 4 (PDE4), which specifically hydrolyzes cAMP, inhibits TNFα secretion. This study was aimed at investigating the evolution of PDE activity and expression levels during the course of the disease in MRL/lpr lupus-prone mice, and to evaluate in these mice the biological and clinical effects of treatments with pentoxifylline, denbufylline and NCS 613 PDE inhibitors. This study reveals that compared to CBA/J control mice, kidney PDE4 activity of MRL/lpr mice increases with the disease progression. Furthermore, it showed that the most potent and selective PDE4 inhibitor NCS 613 is also the most effective molecule in decreasing proteinuria and increasing survival rate of MRL/lpr mice. NCS 613 is a potent inhibitor, which is more selective for the PDE4C subtype (IC50 = 1.4 nM) than the other subtypes (PDE4A, IC50 = 44 nM; PDE4B, IC50 = 48 nM; and PDE4D, IC50 = 14 nM). Interestingly, its affinity for the High Affinity Rolipram Binding Site is relatively low (Ki = 148 nM) in comparison to rolipram (Ki = 3 nM). Finally, as also observed using MRL/lpr peripheral blood lymphocytes (PBLs), NCS 613 inhibits basal and LPS-induced TNFα secretion from PBLs of lupus patients, suggesting a therapeutic potential of NCS 613 in systemic lupus. This study reveals that PDE4 represent a potential therapeutic target in lupus disease

LDAP: Lightweight Dynamic Auto-Reconfigurable Protocol in an IoT-Enabled WSN for Wide-Area Remote Monitoring

IoT (Internet of Things)-based remote monitoring and controlling applications are increasing in dimensions and domains day by day. Sensor-based remote monitoring using a Wireless Sensor Network (WSN) becomes challenging for applications when both temporal and spatial data from widely spread sources are acquired in real time. In applications such as environmental, agricultural, and water quality monitoring, the data sources are geographically distributed, and have little or no cellular connectivity. These applications require long-distance wireless or satellite connections for IoT connectivity. Present WSNs are better suited for densely populated applications and require a large number of sensor nodes and base stations for wider coverage but at the cost of added complexity in routing and network organization. As a result, real time data acquisition using an IoT connected WSN is a challenge in terms of coverage, network lifetime, and wireless connectivity. This paper proposes a lightweight, dynamic, and auto-reconfigurable communication protocol (LDAP) for Wide-Area Remote Monitoring (WARM) applications. It has a mobile data sink for wider WSN coverage, and auto-reconfiguration capability to cope with the dynamic network topology required for device mobility. The WSN coverage and lifetime are further improved by using a Long-Range (LoRa) wireless interface. We evaluated the performance of the proposed LDAP in the field in terms of the data delivery rate, Received Signal Strength (RSS), and Signal to Noise Ratio (SNR). All experiments were conducted in a field trial for a water quality monitoring application as a case study. We have used both static and mobile data sinks with static sensor nodes in an IoT-connected environment. The experimental results show a significant reduction (up to 80%) of the number of data sinks while using the proposed LDAP. We also evaluated the energy consumption to determine the lifetime of the WSN using the LDAP algorithm

Dimensioning of Wide-Area Alternate Wetting and Drying (AWD) System for IoT-Based Automation

Water, one of the most valuable resources, is underutilized in irrigated rice production. The yield of rice, a staple food across the world, is highly dependent on having proper irrigation systems. Alternate wetting and drying (AWD) is an effective irrigation method mainly used for irrigated rice production. However, unattended, manual, small-scale, and discrete implementations cannot achieve the maximum benefit of AWD. Automation of large-scale (over 1000 acres) implementation of AWD can be carried out using wide-area wireless sensor network (WSN). An automated AWD system requires three different WSNs: one for water level and environmental monitoring, one for monitoring of the irrigation system, and another for controlling the irrigation system. Integration of these three different WSNs requires proper dimensioning of the AWD edge elements (sensor and actuator nodes) to reduce the deployment cost and make it scalable. Besides field-level monitoring, the integration of external control parameters, such as real-time weather forecasts, plant physiological data, and input from farmers, can further enhance the performance of the automated AWD system. Internet of Things (IoT) can be used to interface the WSNs with external data sources. This research focuses on the dimensioning of the AWD system for the multilayer WSN integration and the required algorithms for the closed loop control of the irrigation system using IoT. Implementation of the AWD for 25,000 acres is shown as a possible use case. Plastic pipes are proposed as the means to transport and control proper distribution of water in the field, which significantly helps to reduce conveyance loss. This system utilizes 250 pumps, grouped into 10 clusters, to ensure equal water distribution amongst the users (field owners) in the wide area. The proposed automation algorithm handles the complexity of maintaining proper water pressure throughout the pipe network, scheduling the pump, and controlling the water outlets. Mathematical models are presented for proper dimensioning of the AWD. A low-power and long-range sensor node is developed due to the lack of cellular data coverage in rural areas, and its functionality is tested using an IoT platform for small-scale field trials

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this article, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk