Pharmacology and therapeutic implications of current drugs for type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a global epidemic that poses a major challenge to health-care systems. Improving metabolic control to approach normal glycaemia (where practical) greatly benefits long-term prognoses and justifies early, effective, sustained and safety-conscious intervention. Improvements in the understanding of the complex pathogenesis of T2DM have underpinned the development of glucose-lowering therapies with complementary mechanisms of action, which have expanded treatment options and facilitated individualized management strategies. Over the past decade, several new classes of glucose-lowering agents have been licensed, including glucagon-like peptide 1 receptor (GLP-1R) agonists, dipeptidyl peptidase 4 (DPP-4) inhibitors and sodium/glucose cotransporter 2 (SGLT2) inhibitors. These agents can be used individually or in combination with well-established treatments such as biguanides, sulfonylureas and thiazolidinediones. Although novel agents have potential advantages including low risk of hypoglycaemia and help with weight control, long-term safety has yet to be established. In this Review, we assess the pharmacokinetics, pharmacodynamics and safety profiles, including cardiovascular safety, of currently available therapies for management of hyperglycaemia in patients with T2DM within the context of disease pathogenesis and natural history. In addition, we briefly describe treatment algorithms for patients with T2DM and lessons from present therapies to inform the development of future therapies.

The state of the nations:current and future developments in technology enhanced learning

Right across Europe technology is playing a vital part in enhancing learning for an increasingly diverse population of learners. Learning is increasingly flexible, social and mobile and supported by high quality multi-media resources. Institutional VLEs are seeing a shift towards open source products and these core systems are supplemented by a range of social and collaborative learning tools based on web 2.0 technologies. Learners undertaking field studies and those in the workplace are coming to expect that these off-campus experiences will also be technology-rich whether supported by institutional or user-owned devices. As well as keeping European businesses competitive, learning is seen as a means of increasing social mobility and supporting an agenda of social justice. For a number of years the EUNIS E-Learning Task Force (ELTF) has conducted snapshot surveys of e-learning across member institutions, collected case studies of good practice in e-learning see (Hayes, et al., 2009) in references, supported a group looking at the future of e-learning, and showcased the best of innovation in its e-learning Award. Now for the first time the ELTF membership has come together to undertake an analysis of developments in the member states and to assess what this might mean for the future. The group applied the techniques of World Café conversation and Scenario Thinking to develop its thoughts. The analysis is unashamedly qualitative and draws on expertise from leading universities across eight of the EUNIS member states. What emerges is interesting in terms of the common trends in developments in all of the nations and similarities in hopes and concerns about the future development of learning.

Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.