Biological Functions and Metabolism of Oleoylethanolamide

The present review is focused on the metabolism and the emerging roles of oleoylethanolamide (OEA) with emphasis on its effects on food intake control and lipid metabolism. The biological mechanism of action, including a non-genomic effect mediated through peroxisome proliferator-activated receptor alpha (PPAR-alpha) and transient receptor potential vanilloid type 1 (TRPV1) receptor, is discussed. The research related to fatty acid ethanolamides has been focused until recently on anandamide and its interaction with cannabinoid receptor subtype 1. The roles of other N-acyl ethanolamine fatty acid derivatives have been neglected until it was demonstrated that OEA can modulate food intake control through interaction with PPAR-alpha. Further investigations demonstrated that OEA modulates lipid and glucose metabolism, and recent study confirmed that OEA is an antagonist of TRVP1. It has been demonstrated that OEA has beneficial effects on health by inducing food intake control, lipid beta-oxidation, body weight loss and analgesic effects. The investigation of the mechanism of action revealed that OEA activates PPAR-alpha and stimulates the vagal nerve through the capsaicin receptor TRPV1. Pre-clinical studies showed that OEA remains active when administered orally.     

Quantitative images of large biological bodies in microwavetomography by using numerical and real data

A new inverse microwave imaging algorithm is presented which has the ability to obtain quantitative dielectric maps of large biological bodies. By using a priori information, obtained with a first order algorithm, the final image is obtained by solving the direct problem and an ill-conditioned system of equations into an iterative procedure. The algorithm has been successfully tested with real data from an experimental scanner     

First pilot trial of the STAR-Liege protocol for tight glycemic control in critically ill patients

Tight glycemic control (TGC) has shown benefits in ICU patients, but been difficult to achieve consistently due to inter- and intra- patient variability that requires more adaptive, patient-specific solutions. STAR (Stochastic TARgeted) is a flexible model-based TGC framework accounting for patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 72mg/dL. This research describes the first clinical pilot trial of the STAR approach and the post-trial analysis of the models and methods that underpin the protocol. The STAR framework works with clinically specified targets and intervention guidelines. The clinically specified glycemic target was 125mg/dL. Each trial was 24h with BG measured 1-2hourly. Two-hourly measurement was used when BG was between 110-135mg/dL for 3h. In the STAR approach, each intervention leads to a predicted BG level and outcome range (5-95th percentile) based on a stochastic model of metabolic patient variability. Carbohydrate intake (all sources) was monitored, but not changed from clinical settings except to prevent BG<100mg/dL when no insulin was given. Insulin infusion rates were limited (6U/h maximum), with limited increases based on current infusion rate (0.5-2.0U/h), making this use of the STAR framework an insulin-only TGC approach. Approval was granted by the Ethics Committee of the Medical Faculty of the University of Liege (Liege, Belgium). Nine patient trials were undertaken after obtaining informed consent. There were 205 measurements over all 9 trials. Median [IQR] per-patient results were: BG: 138.5 [130.6-146.0]mg/dL; carbohydrate administered: 2-11g/h; median insulin:1.3 [0.9-2.4]U/h with a maximum of 6.0 [4.7-6.0]U/h. Median [IQR] time in the desired 110-140mg/dL band was: 50.0 [31.2-54.2]%. Median model prediction errors ranged: 10-18%, with larger errors due to small meals and other clinical events. The minimum BG was 63mg/dL and no other measurement was below 72mg/dL, so only 1 measurement (0.5%) was below the 5% guaranteed minimum risk level. Post-trial analysis showed that patients were more variable than predicted by the stochastic model used for control, resulting in some of the prediction errors seen. Analysis and (validated) virtual trial re-simulating the clinical trial using stochastic models relevant to the patient's particular day of ICU stay were seen to be more accurate in capturing the observed variability. This analysis indicated that equivalent control and safety could be obtained with similar or lower glycemic variability in control using more specific stochastic models. STAR effectively controlled all patients to target. Observed patient variability in response to insulin and thus prediction errors were higher than expected, likely due to the recent insult of cardiac surgery or a major cardiac event, and their immediate recovery. STAR effectively managed this variability with no hypoglycemia. Improved stochastic models will be used to prospectively test these outcomes in further ongoing clinical pilot trials in this and other units.     

On the relation between the MXL family of algorithms and Gröbner basis algorithms

The computation of Gröbner bases remains one of the most powerful methods for tackling the Polynomial System Solving (PoSSo) problem. The most efficient known algorithms reduce the Gröbner basis computation to Gaussian eliminations on several matrices. However, several degrees of freedom are available to generate these matrices. It is well known that the particular strategies used can drastically affect the efficiency of the computations. In this work, we investigate a recently-proposed strategy, the so-called “Mutant strategy”, on which a new family of algorithms is based (MXL, MXL₂ and MXL₃). By studying and describing the algorithms based on Gröbner basis concepts, we demonstrate that the Mutant strategy can be understood to be equivalent to the classical Normal Selection Strategy currently used in Gröbner basis algorithms. Furthermore, we show that the “partial enlargement” technique can be understood as a strategy for restricting the number of S-polynomials considered in an iteration of the _F4$F_4$ Gröbner basis algorithm, while the new termination criterion used in MXL₃ does not lead to termination at a lower degree than the classical Gebauer–Möller installation of Buchberger’s criteria. We claim that our results map all novel concepts from the MXL family of algorithms to their well-known Gröbner basis equivalents. Using previous results that had shown the relation between the original XL algorithm and _F4$F_4$, we conclude that the MXL family of algorithms can be fundamentally reduced to redundant variants of _F4$F_4$.     

Deciphering the human nucleolar proteome

Nucleoli are plurifunctional nuclear domains involved in the regulation of several major cellular processes such as ribosome biogenesis, the biogenesis of non-ribosomal ribonucleoprotein complexes, cell cycle, and cellular aging. Until recently, the protein content of nucleoli was poorly described. Several proteomic analyses have been undertaken to discover the molecular bases of the biological roles fulfilled by nucleoli. These studies have led to the identification of more than 700 proteins. Extensive bibliographic and bioinformatic analyses allowed the classification of the identified proteins into functional groups and suggested potential functions of 150 human proteins previously uncharacterized. The combination of improvements in mass spectrometry technologies, the characterization of protein complexes, and data mining will assist in furthering our understanding of the role of nucleoli in different physiological and pathological cell states.     

Tight glycemic control in critical care--the leading role of insulin sensitivity and patient variability: a review and model-based analysis

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts.This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols.A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000 h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S_I). Variation in S_I provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/S_I) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.     

Artificial discontinuities of single-parametric Gröbner bases

Artificial discontinuity is a kind of singularity at a parametric point in computing the Gröbner basis of a specialized parametric ideal w.r.t. a certain term order. When it occurs, though parameters change continuously at the point and the properties of the parametric ideal have no sudden changes, the Gröbner basis will still have a jump at the parametric point. This phenomenon can cause instabilities in computing approximate Gröbner bases.In this paper, we study artificial discontinuities in single-parametric case by proposing a solid theoretical foundation for them. We provide a criterion to recognize artificial discontinuities by comparing the zero point numbers of specialized parametric ideals. Moreover, we prove that for a single-parametric polynomial ideal with some restrictions, its artificially discontinuous specializations (ADS) can be locally repaired to continuous specializations (CS) by the TSV (Term Substitution with Variables) strategy.