The Effect of Iron and Erythropoietin Treatment on the A1C of Patients With Diabetes and Chronic Kidney Disease

OBJECTIVE - To examine the effect of intravenous iron and erythropoietin-stimulating agents (ESAs) on glycemic control and A1C of patients with diabetes and chronic kidney disease (CKD). RESEARCH DESIGN AND METHODS- This was a prospective study of patients with type 2 diabetes and CKD stage IIIB or IV undergoing intravenous iron (group A) and/or ESA (group B). Full blood profiles were determined over the study period. Glycemic control was monitored using A1C, seven-point daily glucose three times weekly, and continuous glucose monitoring (CGM). RESULTS - There were 15 patients in both group A and group B. Mean A1C (95% CI) values fell in both groups (7.40% [6.60-8.19] to 6.96% [6.27-7.25] , P < 0.01, with intravenous iron and 7.31% [6.42-8.54] to 6.63% [6.03-7.36] , P = 0.013, ESA). There was no change in mean blood glucose in group A (9.55 mmol/l [8.20-10.90] vs. 9.71 mmol/l [8.29-11.13] , P = 0.07) and in group B (8.72 mmol/l [7.31-10.12] vs. 8.78 mmol/l [7.47-9.99] , P=0.61) over the study period. Hemoglobin and hematocrit values significantly increased following both treatments. There was no linear relationship found between the change in A1C values and the rise of hemoglobin following either treatment. CONCLUSIONS - Both iron and ESA cause a significant fall in A1C values without a change to glycemic control in patients with diabetes and CKD. At the present time, regular capillary glucose measurements and the concurrent use of CGM remain the best alternative measurements of glycemic control in this patient group

Amortized Rejection Sampling in Universal Probabilistic Programming

Existing approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. An instance of this is importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure. In this paper we develop a new and efficient amortized importance sampling estimator. We prove finite variance of our estimator and empirically demonstrate our method's correctness and efficiency compared to existing alternatives on generative programs containing rejection sampling loops and discuss how to implement our method in a generic probabilistic programming framework

Shifting Home Energy Consumption Through a Holistic Understanding of the Home System of Practice

This chapter describes the concept of the home system of practice, which can be used to provide a deeper understanding of the social system of the home and inform long-term solutions for enabling domestic energy reduction. More traditional methods have attempted to persuade occupants to change behaviour through the use of information campaigns and feedback technology. However, these interventions are usually short lived as they ignore the underlying reasons for practices to occur. A more effective solution is through practice-oriented design, which co-creates innovative technology with the user. In addition, the emergence and use of automated technology enables practices to act independently of the user. Yet, the success of automation is also reliant on an understanding of the home system of practice, occupant needs and skills

POSIWID and determinism in design for behaviour change

Copyright @ 2012 Social Services Research GroupWhen designing to influence behaviour for social or environmental benefit, does designers' intent matter? Or are the effects on behaviour more important, regardless of the intent involved? This brief paper explores -- in the context of design for behaviour change -- some treatments of design, intentionality, purpose and responsibility from a variety of fields, including Stafford Beer's "The purpose of a system is what it does" and Maurice Broady's perspective on determinism. The paper attempts to extract useful implications for designers working on behaviour-related problems, in terms of analytical or reflective questions to ask during the design process