The Dialectics of Disembedding and Civil Society in Provincial Hungary.

This essay applies Karl Polanyi's concepts of embedding and countermovement to provincial Hungary during and after socialism. Comprehensive state socialist repression in the 1950s was a politics-led disembedding. An economy-led countermovement began in the 1960s, later augmented by elite discourses of civil society. The 1970s and 1980s were decades of socialist embeddedness. Neoliberal configurations after 1990 dislocated both economic and associational life. The illiberal democracy of Viktor Orbán is a more consequential countermovement than the earlier countermovements to state socialism. The argument is illustrated with data from long-term fieldwork in southern Hungary, in the region of the Danube-Tisza Interfluve.

Transition, tradition, and nostalgia; postsocialist transformations in a comparative framework.

The concepts of transition and tradition have not been the object of much original theoretical work in recent Anglophone socio-cultural anthropology. The term transition has been applied loosely to the demise of socialist regimes in Eastern Europe and their replacement by market economies and more pluralist forms of government. However, these objectives have proved elusive and most anthropologists therefore speak of open-ended "transformation processes" rather than a linear shift to capitalist democracy. Use of the concept of tradition has been much influenced by the work of historians on the "invention of tradition". This paper explores how societies, in particular elite groups, construct different types of tradition in the wake of historical caesurae. Paying particular attention to the concept of nostalgia, it compares perceptions of the past in Britain, where social change has been largely gradual, with those in empires and states which experienced sharp political discontinuities in the twentieth century. To explain and understand nostalgia for socialism in large sections of the population in many postsocialist states, it is necessary to investigate not only economic and social mobility and related material factors but also factors pertaining to identity, especially collective identities.

Development of blood glucose control for extremely premature infants.

Extremely premature neonates often experience hyperglycaemia, which has been linked to increased mortality and worsened outcomes. Insulin therapy can assist in controlling blood glucose levels and promoting needed growth. This study presents the development of a model-based stochastic targeted controller designed to adapt insulin infusion rates to match the unique and changing metabolic state and control parameters of the neonate. Long-term usage of targeted BG control requires successfully forecasting variations in neonatal metabolic state, accounting for differences in clinical practices between units, and demonstrating robustness to errors that can occur in everyday clinical usage. Simulation studies were used to evaluate controller ability to target several common BG ranges and evaluate controller sensitivity to missed BG measurements and delays in control interventions on a virtual patient cohort of 25 infants developed from retrospective data. Initial clinical pilot trials indicated model performance matched expected performance from simulations. Stochastic targeted glucose control developed using validated patient-specific virtual trials can yield effective protocols for this cohort. Long-term trials show fundamental success, however clinical interface design appears as a critical factor to ensuring good compliance and thus good control.

Modeling the glucose regulatory system in extreme preterm infants.

BACKGROUND: Premature infants represent a significant proportion of the neonatal intensive care population. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition. Hypo- and hyperglycemia are frequently reported in very low birth weight infants, and more mature infants often experience low levels of glycemia. A model capturing the unique fundamental dynamics of the neonatal glucose regulatory system could be used to develop better blood glucose control methods. METHODS: A metabolic system model is adapted from adult critical care to the unique physiological case of the neonate. Integral-based fitting methods were used to identify time-varying insulin sensitivity and non-insulin mediated glucose uptake profiles. The clinically important predictive ability of the model was assessed by assuming insulin sensitivity was constant over prediction intervals of 1, 2 and 4h forward and comparing model-simulated versus actual clinical glucose values for all recorded interventions. The clinical data included 1091 glucose measurements over 3567 total patient hours, along with all associated insulin and nutritional infusion data, for N=25 total cases. Ethics approval was obtained from the Upper South A Regional Ethics Committee for this study. RESULTS: The identified model had a median absolute percentage error of 2.4% [IQR: 0.9-4.8%] between model-fitted and clinical glucose values. Median absolute prediction errors at 1-, 2- and 4-h intervals were 5.2% [IQR: 2.5-10.3%], 9.4% [IQR: 4.5-18.4%] and 13.6% [IQR: 6.3-27.6%] respectively. CONCLUSIONS: The model accurately captures and predicts the fundamental dynamic behaviors of the neonatal metabolism well enough for effective clinical decision support in glycemic control. The adaptation from adult to a neonatal case is based on the data from the literature. Low prediction errors and very low fitting errors indicate that the fundamental dynamics of glucose metabolism in both premature neonates and critical care adults can be described by similar mathematical models.

Blood glucose controller for neonatal intensive care: virtual trials development and first clinical trials.

BACKGROUND: Premature neonates often experience hyperglycemia, which has been linked to worsened outcomes. Insulin therapy can assist in controlling blood glucose (BG) levels. However, a reliable, robust control protocol is required to avoid hypoglycemia and to ensure that clinically important nutrition goals are met. METHODS: This study presents an adaptive, model-based predictive controller designed to incorporate the unique metabolic state of the neonate. Controller performance was tested and refined in virtual trials on a 25-patient retrospective cohort. The effects of measurement frequency and BG sensor error were evaluated. A stochastic model of insulin sensitivity was used in control to provide a guaranteed maximum 4% risk of BG < 72 mg/dl to protect against hypoglycemia as well as account for patient variability over 1-3 h intervals when determining the intervention. The resulting controller is demonstrated in two 24 h clinical neonatal pilot trials at Christchurch Women's Hospital. RESULTS: Time in the 72-126 mg/dl BG band was increased by 103-161% compared to retrospective clinical control for virtual trials of the controller, with fewer hypoglycemic measurements. Controllers were robust to BG sensor errors. The model-based controller maintained glycemia to a tight target control range and accounted for interpatient variability in patient glycemic response despite using more insulin than the retrospective case, illustrating a further measure of controller robustness. Pilot clinical trials demonstrated initial safety and efficacy of the control method. CONCLUSIONS: A controller was developed that made optimum use of the very limited available BG measurements in the neonatal intensive care unit and provided robustness against BG sensor error and longer BG measurement intervals. It used more insulin than typical sliding scale approaches or retrospective hospital control. The potential advantages of a model-based approach demonstrated in simulation were applied to initial clinical trials.

Targeted glycemic reduction in critical care using closed-loop control.

BACKGROUND: Critically ill patients are often hyperglycemic and extremely diverse in their dynamics. Consequently, fixed protocols and sliding scales can result in error and poor control. Tight glucose control has been shown to significantly reduce mortality in critical care. An improved physiological system model of the glucose-insulin dynamics of a critical care patient is used to develop an adaptive tight glucose control protocol that accounts for variable patient dynamics, and is verified in limited clinical trials. METHODS: A physiologically based two-compartment system model that accounts for time-varying insulin sensitivity, time-varying endogenous glucose removal, and two saturation kinetics mechanisms is developed. A bolus-based adaptive control protocol is developed that monitors the physiological status of a critically ill patient, enabling tight glycemic regulation to preset glycemic targets. The model and protocol are verified in three, 5-h preliminary proof-of-concept clinical trials. Ethics approval was granted by the Canterbury Ethics Committee (Christchurch, New Zealand). RESULTS: Preset glycemic targets are achieved with an average absolute error of 9%, with 75% of all targets achieved within the 7% measurement error. Absolute errors greater than 7% ranged from 17% to 21%. CONCLUSIONS: Tight stepwise control was exhibited in all cases, and the adaptive system was able to match the model and observed patient dynamics. Most errors are associated with external perturbations such as drug therapies, or mismodeled parameters that can be easily adjusted with longer trials and/or more data per hour. The overall result is targeted stepwise tight glycemic regulation using insulin boluses.

Adaptive bolus-based targeted glucose regulation of hyperglycaemia in critical care.

Tight regulation of blood glucose can significantly reduce mortality in critical illness. Critically ill patients are extremely diverse in the dynamics of their hyperglycaemia. Hence, responses can vary significantly, due to variations in insulin levels, effective insulin utilization, glucose absorption and other factors. Consequently, fixed protocols and sliding scales can result in error, given this large variation in patient dynamics. A two-compartment glucose-insulin system model that accounts for time-varying insulin sensitivity and endogenous glucose removal, along with two different saturation kinetics, is developed and tested in preliminary proof-of-concept clinical trials for adaptive control of blood glucose levels. The adaptive control algorithm developed in this research monitors the physiological status of a critically ill patient, allowing real-time, tight glycaemic regulation. The bolus-based insulin administration provides a safe approach to glucose level management. The ability to track changing physiological status and account for insulin transport and effect saturation enabled targeted stepwise reduction in glycaemic levels in three test cases.

Derivative weighted active insulin control modelling and clinical trials for ICU patients.

Close control of blood glucose levels significantly reduces vascular complications in Type 1 and Type 2 diabetic individuals. Heavy derivative controllers using the data density available from emerging biosensors are developed to provide tight, optimal control of elevated blood glucose levels, while robustly handling variation in patient response. A two-compartment glucose regulatory system model is developed for intravenous infusion from physiologically verified subcutaneous infusion models enabling a proof-of-concept clinical trial at the Christchurch Hospital Department of Intensive Care Medicine. This clinical trial is the first of its kind to test a high sample rate feedback control algorithm for tight glucose regulation. The clinical trial results show tight control with reductions of 79-89% in blood glucose excursions for an oral glucose tolerance test. Experimental performance is very similar to modelled behaviour. Results include a clear need for an additional accumulator dynamic for insulin behaviour in transport to the blood and strong correlation of 10% or less between modelled insulin infused and the amounts used in clinical trials. Finally, the heavy derivative PD control approach is seen to be able to bring blood glucose levels below the (elevated) basal level, showing the potential for truly tight control.