Metabolic control of type 1 diabetic patients followed at the University Children's Hospital in Berne: Have we reached the goal?

In type 1 diabetes (T1DM), a good metabolic control is important to reduce and/or postpone complications. Guidelines regarding how to achieve this goal are published by the American Diabetes Association (ADA) and the International Society of Paediatric and Adolescence Diabetes (ISPAD). The aims of this study were to determine the current level of metabolic control in T1DM patients on different treatment regimens, followed at the diabetes outpatient unit of the University Children's Hospital Bern, Switzerland, and to compare it with both the reported data from ten years ago (1998) and with the current guidelines of the ADA and ISPAD.

Protein level expression of Toll-like receptors 2, 4 and 9 in renal disease

Toll-like receptors (TLR) recognize a variety of ligands, including pathogen-associated molecular patterns and link innate and adaptive immunity. Individual receptors can be up-regulated during infection and inflammation. We examined the expression of selected TLRs at the protein level in various types of renal disease.

The Bernese Hypothermia Algorithm: a consensus paper on in-hospital decision-making and treatment of patients in hypothermic cardiac arrest at an alpine level 1 trauma centre

INTRODUCTION: Guidelines for the treatment of patients in severe hypothermia and mainly in hypothermic cardiac arrest recommend the rewarming using the extracorporeal circulation (ECC). However,guidelines for the further in-hospital diagnostic and therapeutic approach of these patients, who often suffer from additional injuries—especially in avalanche casualties, are lacking. Lack of such algorithms may relevantly delay treatment and put patients at further risk. Together with a multidisciplinary team, the Emergency Department at the University Hospital in Bern, a level I trauma centre, created an algorithm for the in-hospital treatment of patients with hypothermic cardiac arrest. This algorithm primarily focuses on the decision-making process for the administration of ECC. THE BERNESE HYPOTHERMIA ALGORITHM: The major difference between the traditional approach, where all hypothermic patients are primarily admitted to the emergency centre, and our new algorithm is that hypothermic cardiac arrest patients without obvious signs of severe trauma are taken to the operating theatre without delay. Subsequently, the interdisciplinary team decides whether to rewarm the patient using ECC based on a standard clinical trauma assessment, serum potassium levels, core body temperature, sonographic examinations of the abdomen, pleural space, and pericardium, as well as a pelvic X-ray, if needed. During ECC, sonography is repeated and haemodynamic function as well as haemoglobin levels are regularly monitored. Standard radiological investigations according to the local multiple trauma protocol are performed only after ECC. Transfer to the intensive care unit, where mild therapeutic hypothermia is maintained for another 12 h, should not be delayed by additional X-rays for minor injuries. DISCUSSION: The presented algorithm is intended to facilitate in-hospital decision-making and shorten the door-to-reperfusion time for patients with hypothermic cardiac arrest. It was the result of intensive collaboration between different specialties and highlights the importance of high-quality teamwork for rare cases of severe accidental hypothermia. Information derived from the new International Hypothermia Registry will help to answer open questions and further optimize the algorithm.

Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with Gaussian process models

Several methods based on Kriging have recently been proposed for calculating a probability of failure involving costly-to-evaluate functions. A closely related problem is to estimate the set of inputs leading to a response exceeding a given threshold. Now, estimating such a level set—and not solely its volume—and quantifying uncertainties on it are not straightforward. Here we use notions from random set theory to obtain an estimate of the level set, together with a quantification of estimation uncertainty. We give explicit formulae in the Gaussian process set-up and provide a consistency result. We then illustrate how space-filling versus adaptive design strategies may sequentially reduce level set estimation uncertainty.