Is liraglutide or exenatide better in type 2 diabetes?

Background: Subjects with type 2 diabetes have high circulating levels of glucose. Glucagon-like peptide-1 (GLP-1) is an intestinal hormone that has a major role in glucose homeostasis. Exenatide and liraglutide are both agonists at the GLP-1 receptor, and are effective at reducing circulating glucose levels (measured as HbA1c levels), but they have not been compared. Objectives/methods: This evaluation is of a clinical trial comparing liraglutide once a day with exenatide twice a day in subjects with type 2 diabetes. Results: In the Liraglutide Effect and Action in Diabetes (LEAD)-6 trial, subcutaneous liraglutide 1.8 mg once a day was compared with exenatide 10 μg twice a day. The primary efficacy outcome was change in HbA1c levels, and this was significantly greater with liraglutide (1.12%) than with exenatide (0.79%). Liraglutide and exenatide had similar small abilities to reduce body weight, blood pressure and LDL-cholesterol. Conclusions: Liraglutide was more effective than exenatide for overall glycaemic control in subjects with type 2 diabetes. However, this is only true for the preparations and doses tested, that is liraglutide 1.8 mg once weekly and exenatide 10 μg b.i.d., and may not apply when the comparison is undertaken with the new longer-lasting preparation of exenatide once weekly.

Resistance training for obese, type 2 diabetic adults: A review of the evidence

In both developed and developing countries, increased prevalence of obesity has been strongly associated with increased incidence of type 2 diabetes mellitus (T2DM) in the adult population. Previous research has emphasized the importance of physical activity in the prevention and management of obesity and T2DM, and generic exercise guidelines originally developed for the wider population have been adapted for these specific populations. However, the guidelines traditionally focus on aerobic training without due consideration to other exercise modalities. Recent reviews on resistance training in the T2DM population have not compared this modality with others including aerobic training, or considered the implications of resistance training for individuals suffering from both obesity and T2DM. In short, the optimal mix of exercise modalities in the prescription of exercise has not been identified for it benefits to the metabolic, body composition and muscular health markers common in obesity and T2DM. Similarly, the underlying physical, social and psychological barriers to adopting and maintaining exercise, with the potential to undermine the efficacy of exercise interventions, have not been addressed in earlier reviews. Because it is well established that aerobic exercise has profound effects on obesity and T2DM risk, the purpose of this review was to address the importance of resistance training to obese adults with T2DM.

Type Test Elimination using Typeflow Analysis

Programs written in languages of the Oberon family usually contain runtime tests on the dynamic type of variables. In some cases it may be desirable to reduce the number of such tests. Typeflow analysis is a static method of determining bounds on the types that objects may possess at runtime. We show that this analysis is able to reduce the number of tests in certain plausible circumstances. Furthermore, the same analysis is able to detect certain program errors at compile time, which would normally only be detected at program execution. This paper introduces the concepts of typeflow analysis and details its use in the reduction of runtime overhead in Oberon-2.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

The impact of delayed blood centrifuging, choice of collection tube, and type of assay on 25-hydroxyvitamin D concentrations

Studies have examined the associations between cancers and circulating 25-hydroxyvitamin D [25(OH)D], but little is known about the impact of different laboratory practices on 25(OH)D concentrations. We examined the potential impact of delayed blood centrifuging, choice of collection tube, and type of assay on 25(OH)D concentrations. Blood samples from 20 healthy volunteers underwent alternative laboratory procedures: four centrifuging times (2, 24, 72, and 96 h after blood draw); three types of collection tubes (red top serum tube, two different plasma anticoagulant tubes containing heparin or EDTA); and two types of assays (DiaSorin radioimmunoassay [RIA] and chemiluminescence immunoassay [CLIA/LIAISON®]). Log-transformed 25(OH)D concentrations were analyzed using the generalized estimating equations (GEE) linear regression models. We found no difference in 25(OH)D concentrations by centrifuging times or type of assay. There was some indication of a difference in 25(OH)D concentrations by tube type in CLIA/LIAISON®-assayed samples, with concentrations in heparinized plasma (geometric mean, 16.1 ng ml−1) higher than those in serum (geometric mean, 15.3 ng ml−1) (p = 0.01), but the difference was significant only after substantial centrifuging delays (96 h). Our study suggests no necessity for requiring immediate processing of blood samples after collection or for the choice of a tube type or assay.

Driving-related eye and head movements are changed by the type of presbyopic correction

Purpose: To investigate whether wearing different presbyopic vision corrections alters the pattern of eye and head movements when viewing dynamic driving-related traffic scenes. Methods: Participants included 20 presbyopes (mean age: 56±5.7 years) who had no experience of wearing presbyopic vision corrections (i.e. all were single vision wearers). Eye and head movements were recorded while wearing five different vision corrections: single vision lenses (SV), progressive addition spectacle lenses (PALs), bifocal spectacle lenses (BIF), monovision (MV) and multifocal contact lenses (MTF CL) in random order. Videotape recordings of traffic scenes of suburban roads and expressways (with edited targets) were presented as dynamic driving-related stimuli and digital numeric display panels included as near visual stimuli (simulating speedometer and radio). Eye and head movements were recorded using the faceLAB™ system and the accuracy of target identification was also recorded. Results: The magnitude of eye movements while viewing the driving-related traffic scenes was greater when wearing BIF and PALs than MV and MTF CL (p≤0.013). The magnitude of head movements was greater when wearing SV, BIF and PALs than MV and MTF CL (p<0.0001) and the number of saccades was significantly higher for BIF and PALs than MV (p≤0.043). Target recognition accuracy was poorer for all vision corrections when the near stimulus was located at eccentricities inferiorly and to the left, rather than directly below the primary position of gaze (p=0.008), and PALs gave better performance than MTF CL (p=0.043). Conclusions: Different presbyopic vision corrections alter eye and head movement patterns. In particular, the larger magnitude of eye and head movements and greater number of saccades associated with the spectacle presbyopic corrections, may impact on driving performance.

Trauma type and posttrauma outcomes : differences between survivors of motor vehicle accidents, sexual assault, and bereavement

Research examining post-trauma pathology indicates negative outcomes can differ as a function of the type of trauma experienced. Such research has yet to be published when looking at positive post-trauma changes. Ninety-Four survivors of trauma, forming three groups, completed the Posttraumatic Growth Inventory (PTGI) and Impact of Events Scale-Revised (IES-R). Groups comprised survivors of i) sexual abuse ii) motor vehicle accidents iii) bereavement. Results indicted differences in growth between the groups with the bereaved reporting higher levels of growth than other survivors and sexual abuse survivors demonstrated higher levels of PTSD symptoms than the other groups. However, this did not preclude sexual abuse survivors from also reporting moderate levels of growth. Results are discussed with relation to fostering growth through clinical practice.

Anxiety among high school students in India : comparisons across gender, school type, social strata, and perceptions of quality time with parents

The broad objective of the study was to better understand anxiety among adolescents in Kolkata city, India. Specifically, the study compared anxiety across gender, school type, socio-economic background and mothers’ employment status. The study also examined adolescents’ perceptions of quality time with their parents. A group of 460 adolescents (220 boys and 240 girls), aged 13-17 years were recruited to participate in the study via a multi-stage sampling technique. The data were collected using a self-report semi-structured questionnaire and a standardized psychological test, the State-Trait Anxiety Inventory. Results show that anxiety was prevalent in the sample with 20.1% of boys and 17.9% of girls found to be suffering from high anxiety. More boys were anxious than girls (p<0.01). Adolescents from Bengali medium schools were more anxious than adolescents from English medium schools (p<0.01). Adolescents belonging to the middle class (middle socio-economic group) suffered more anxiety than those from both high and low socio-economic groups (p<0.01). Adolescents with working mothers were found to be more anxious (p<0.01). Results also show that a substantial proportion of the adolescents perceived they did not receive quality time from fathers (32.1%) and mothers (21.3%). A large number of them also did not feel comfortable to share their personal issues with their parents (60.0% for fathers and 40.0% for mothers).

Self-efficacy, outcome expectations and self-care behaviour in people with type 2 diabetes in Taiwan

Aims. To explore differences in self-care behaviour according to demographic and illness characteristics; and relationships among self-care behaviour and demographic and illness characteristics, efficacy expectations and outcome expectations of people with type 2 diabetes in Taiwan. Background. Most people with diabetes do not control their disease appropriately in Taiwan. Enhanced self-efficacy towards managing diseases can be an effective way of improving disease control as proposed by the self-efficacy model which provides a useful framework for understanding adherence to self-care behaviours. Design and methods. The sample comprised 145 patients with type 2 diabetes aged 30 years or more from diabetes outpatient clinics in Taipei. Data were collected using a self-administered questionnaire for this study. One-way anova, t-tests, Pearson product moment correlation and hierarchical regression were analysed for the study. Results. Significant differences were found: between self-care behaviour and complications (t = −2·52, p < 0·01) and patient education (t = −1·96, p < 0·05). Self-care behaviour was significantly and positively correlated with duration of diabetes (r = 0·36, p < 0·01), efficacy expectations (r = 0·54, p < 0·01) and outcome expectations (r = 0·44, p < 0·01). A total of 39·1% of variance in self-care behaviour can be explained by duration of diabetes, efficacy expectations and outcome expectations. Conclusions. Findings support the use of the self-efficacy model as a framework for understanding adherence to self-care behaviour. Relevance to clinical practice. Using self-efficacy theory when designing patient education interventions for people with type 2 diabetes will enhance self-management routines and assist in reducing major complications in the future.