1,514 research outputs found

    Metabolic health and vascular complications in type 1 diabetes

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    AIMS: Optimal glycaemic control benefits risk of microvascular and macrovascular complications in type 1 diabetes (T1DM) but the importance of other components of metabolic health is less certain, particularly in the context of routine clinical practice. METHODS: Data for this cross-sectional analysis derived from a database covering inner North West London adult diabetes clinics. People with T1DM and with complete information for height, weight, blood pressure and serum high and low-density lipoprotein cholesterol (HDL-c and LDL-c) and triglyceride concentration measurements were included. RESULTS: Among the 920 participants, those with complications were older and had longer duration of diabetes but had similar HbA1c to people without complications. Systolic hypertension and low HDL-c were independently associated with complications. From having 0 risk factors, the prevalence of micro and macrovascular disease increased with increasing number of risk factors. Relative to those with β‰₯1 risk factor, those with 0 risk factors (nβ€―=β€―179) were at lower risk of retinopathy (OR 0.6 (0.4-0.9), pβ€―=β€―0.01) and nephropathy [OR 0.1 (0.04-0.3), pβ€―=β€―0.002], independent of individual characteristics. CONCLUSIONS: In routine clinical management of T1DM, associations between lipid and blood pressure risk factors and prevalent micro and macrovascular disease remain, implying that more intensive risk factor management may be beneficial

    Rationale and protocol for the After Diabetes Diagnosis REsearch Support System (ADDRESS): an incident and high risk type 1 diabetes UK cohort study

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    INTRODUCTION: Type 1 diabetes is heterogeneous in its presentation and progression. Variations in clinical presentation between children and adults, and with ethnic group warrant further study in the UK to improve understanding of this heterogeneity. Early interventions to limit beta cell damage in type 1 diabetes are undergoing evaluation, but recruitment is challenging. The protocol presented describes recruitment of people with clinician-assigned, new-onset type 1 diabetes to understand the variation in their manner of clinical presentation, to facilitate recruitment into intervention studies and to create an open-access resource of data and biological samples for future type 1 diabetes research. METHODS AND ANALYSIS: Using the National Institute for Health Research Clinical Research Network, patients >5 years of age diagnosed clinically with type 1 diabetes (and their siblings) are recruited within 6 months of diagnosis. Participants agree to have their clinical, laboratory and demographic data stored on a secure database, for their clinical progress to be monitored using information held by NHS Digital, and to be contacted about additional research, in particular immunotherapy and other interventions. An optional blood sample is taken for islet autoantibody measurement and storage of blood and DNA for future analyses. Data will be analysed statistically to describe the presentation of incident type 1 diabetes in a contemporary UK population. ETHICS AND DISSEMINATION: Ethical approval was obtained from the independent NHS Research Ethics Service. Results will be presented at national and international meetings and submitted for publication to peer-reviewed journals.This work was supported by Diabetes UK grant number 09/0003919 and the Juvenile Diabetes Research Foundation grant number 9-2010-407. Recruitment is supported by staff at the National Institute for Health Research Clinical Research Network

    Characterizing Protein-Protein Interactions with the Fragment Molecular Orbital Method

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    Proteins are vital components of living systems, serving as building blocks, molecular machines, enzymes, receptors, ion channels, sensors, and transporters. Protein-protein interactions (PPIs) are a key part of their function. There are more than 645,000 reported disease-relevant PPIs in the human interactome, but drugs have been developed for only 2% of these targets. The advances in PPI-focused drug discovery are highly dependent on the availability of structural data and accurate computational tools for analysis of this data. Quantum mechanical approaches are often too expensive computationally, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. FMO provides essential information for PPI drug discovery, namely, identification of key interactions formed between residues of two proteins, including their strength (in kcal/mol) and their chemical nature (electrostatic or hydrophobic). In this chapter, we have demonstrated how three different FMO-based approaches (pair interaction energy analysis (PIE analysis), subsystem analysis (SA) and analysis of protein residue networks (PRNs)) have been applied to study PPI in three protein-protein complexes

    Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method

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    G-protein-coupled receptors (GPCRs) have enormous physiological and biomedical importance, and therefore it is not surprising that they are the targets of many prescribed drugs. Further progress in GPCR drug discovery is highly dependent on the availability of protein structural information. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanics (QM) approaches are often too computationally expensive to be of practical use in time-sensitive situations, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed, and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule toward ligand binding, including an analysis of their chemical nature. Such information is essential for an efficient structure-based drug design (SBDD) process. In this chapter, we describe how to use FMO in the characterization of GPCR-ligand interactions

    Scallop swimming kinematics and muscle performance: modelling the effects of "within-animal" variation in temperature sensitivity

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    Escape behaviour was investigated in Queen scallops (Aequipecten opercularis) acclimated to 5, 10 or 15 degrees C and tested at their acclimation temperature. Scallops are active molluscs, able to escape from predators by jet-propelled swimming using a striated muscle working in opposition to an elastic hinge ligament. The first cycle of the escape response was recorded using high-speed video ( 250 Hz) and whole-animal velocity and acceleration determined. Muscle shortening velocity, force and power output were calculated using measurements of valve movement and jet area, and a simple biomechanical model. The average shortening speed of the adductor muscle had a Q(10) of 2.04, significantly reducing the duration of the jetting phase of the cycle with increased temperature. Muscle lengthening velocity and the overall duration of the clap cycle were changed little over the range 5 - 15 degrees C, as these parameters were controlled by the relatively temperature-insensitive, hinge ligament. Improvements in the average power output of the adductor muscle over the first clap cycle ( 222 vs. 139 W kg(-1) wet mass at 15 and 5 degrees C respectively) were not translated into proportional increases in overall swimming velocity, which was only 32% higher at 15 degrees C ( 0.37m s(-1)) than 5 degrees C (0.28 m s(-1))
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