High speed DSC (hyper-DSC) as a tool to measure the solubility of a drug within a solid or semi-solid matrix

Conventional differential scanning calorimetry (DSC) techniques are commonly used to quantify the solubility of drugs within polymeric-controlled delivery systems. However, the nature of the DSC experiment, and in particular the relatively slow heating rates employed, limit its use to the measurement of drug solubility at the drug's melting temperature. Here, we describe the application of hyper-DSC (HDSC), a variant of DSC involving extremely rapid heating rates, to the calculation of the solubility of a model drug, metronidazole, in silicone elastomer, and demonstrate that the faster heating rates permit the solubility to be calculated under non-equilibrium conditions such that the solubility better approximates that at the temperature of use. At a heating rate of 400°C/min (HDSC), metronidazole solubility was calculated to be 2.16 mg/g compared with 6.16 mg/g at 20°C/min. © 2005 Elsevier B.V. All rights reserved.

Splines and vector splines

The thrust of this report concerns spline theory and some of the background to spline theory and follows the development in (Wahba, 1991). We also review methods for determining hyper-parameters, such as the smoothing parameter, by Generalised Cross Validation. Splines have an advantage over Gaussian Process based procedures in that we can readily impose atmospherically sensible smoothness constraints and maintain computational efficiency. Vector splines enable us to penalise gradients of vorticity and divergence in wind fields. Two similar techniques are summarised and improvements based on robust error functions and restricted numbers of basis functions given. A final, brief discussion of the application of vector splines to the problem of scatterometer data assimilation highlights the problems of ambiguous solutions.

Developments of the generative topographic mapping

The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput. 10(1), 215-234) as a probabilistic re- formulation of the self-organizing map (SOM). It offers a number of advantages compared with the standard SOM, and has already been used in a variety of applications. In this paper we report on several extensions of the GTM, including an incremental version of the EM algorithm for estimating the model parameters, the use of local subspace models, extensions to mixed discrete and continuous data, semi-linear models which permit the use of high-dimensional manifolds whilst avoiding computational intractability, Bayesian inference applied to hyper-parameters, and an alternative framework for the GTM based on Gaussian processes. All of these developments directly exploit the probabilistic structure of the GTM, thereby allowing the underlying modelling assumptions to be made explicit. They also highlight the advantages of adopting a consistent probabilistic framework for the formulation of pattern recognition algorithms.

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

Approximate Bayesian techniques for inference in stochastic dynamical systems

This thesis is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variant of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here two new extended frameworks are derived and presented that are based on basis function expansions and local polynomial approximations of a recently proposed variational Bayesian algorithm. It is shown that the new extensions converge to the original variational algorithm and can be used for state estimation (smoothing). However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new methods are numerically validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein-Uhlenbeck process, for which the exact likelihood can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz '63 (3-dimensional model). The algorithms are also applied to the 40 dimensional stochastic Lorenz '96 system. In this investigation these new approaches are compared with a variety of other well known methods such as the ensemble Kalman filter / smoother, a hybrid Monte Carlo sampler, the dual unscented Kalman filter (for jointly estimating the systems states and model parameters) and full weak-constraint 4D-Var. Empirical analysis of their asymptotic behaviour as a function of observation density or length of time window increases is provided.

Prevalence of subclinical thyroid dysfunction and its relation to socioeconomic deprivation in the elderly:a community-based cross-sectional survey

Context: Population-based screening has been advocated for subclinical thyroid dysfunction in the elderly because the disorder is perceived to be common, and health benefits may be accrued by detection and treatment. Objective: The objective of the study was to determine the prevalence of subclinical thyroid dysfunction and unidentified overt thyroid dysfunction in an elderly population. Design, Setting, and Participants: A cross-sectional survey of a community sample of participants aged 65 yr and older registered with 20 family practices in the United Kingdom. Exclusions: Exclusions included current therapy for thyroid disease, thyroid surgery, or treatment within 12 months. Outcome Measure: Tests of thyroid function (TSH concentration and free T 4 concentration in all, with measurement of free T3 in those with low TSH) were conducted. Explanatory Variables: These included all current medical diagnoses and drug therapies, age, gender, and socioeconomic deprivation (Index of Multiple Deprivation, 2004) Analysis: Standardized prevalence rates were analyzed. Logistic regression modeling was used to determine factors associated with the presence of subclinical thyroid dysfunction Results: A total of 5960 attended for screening. Using biochemical definitions, 94.2% [95% confidence interval (CI) 93.8-94.6%] were euthyroid. Unidentified overt hyper- and hypothyroidism were uncommon (0.3, 0.4%, respectively). Subclinical hyperthyroidism and hypothyroidism were identified with similar frequency (2.1%, 95% CI 1.8-2.3%; 2.9%, 95% CI 2.6-3.1%, respectively). Subclinical thyroid dysfunction was more common in females (P < 0.001) and with increasing age (P < 0.001). After allowing for comorbidities, concurrent drug therapies, age, and gender, an association between subclinical hyperthyroidism and a composite measure of socioeconomic deprivation remained. Conclusions: Undiagnosed overt thyroid dysfunction is uncommon. The prevalence of subclinical thyroid dysfunction is 5%. We have, for the first time, identified an independent association between the prevalence of subclinical thyroid dysfunction and deprivation that cannot be explained solely by the greater burden of chronic disease and/or consequent drug therapies in the deprived population. Copyright © 2006 by The Endocrine Society.

The challenge of real-time automatic mapping for environmental monitoring network management

The automatic interpolation of environmental monitoring network data such as air quality or radiation levels in real-time setting poses a number of practical and theoretical questions. Among the problems found are (i) dealing and communicating uncertainty of predictions, (ii) automatic (hyper)parameter estimation, (iii) monitoring network heterogeneity, (iv) dealing with outlying extremes, and (v) quality control. In this paper we discuss these issues, in light of the spatial interpolation comparison exercise held in 2004.

Sporicidal activity of two disinfectants against Clostridium difficile spores

The sporicidal activity of an odour-free peracetic acid-based disinfectant (Wofasteril®) and a widely-used dichloroisocyanurate preparation (Chlor-clean®) was assessed against spores of the hyper-virulent strain of Clostridium difficile (ribotype 027), in the presence and absence of organic matter. In environmentally clean conditions, dichloroisocyanurate achieved a >3 log10 reduction in 3 minutes, but a minimum contact time of 9 minutes was required to reduce the viable spore load to below detection levels. Peracetic acid achieved a >3 log10 reduction in 30 minutes and was overall significantly less effective (P<0.05). However, in the presence of organic matter - which reflects the true clinical environment - there was no significant difference between the sporicidal activity of dichloroisocyanurate and peracetic acid over a 60-minute period (P=0.188). Given the greater occupational health hazards generally associated with chlorine-releasing agents, odour-free peracetic acid-based disinfectants may offer a suitable alternative for environmental disinfection.

Dissolution rate enhancement, in vitro evaluation and investigation of drug release kinetics of chloramphenicol and sulphamethoxazole solid dispersions

Formulation of solid dispersions is one of the effective methods to increase the rate of solubilization and dissolution of poorly soluble drugs. Solid dispersions of chloramphenicol (CP) and sulphamethoxazole (SX) as model drugs were prepared by melt fusion method using polyethylene glycol 8000 (PEG 8000) as an inert carrier. The dissolution rate of CP and SX were rapid from solid dispersions with low drug and high polymer content. Characterization was performed using fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC) and scanning electron microscopy (SEM). FTIR analysis for the solid dispersions of CP and SX showed that there was no interaction between PEG 8000 and the drugs. Hyper-DSC studies revealed that CP and SX were converted into an amorphous form when formulated as solid dispersion in PEG 8000. Mathematical analysis of the release kinetics demonstrated that drug release from the various formulations followed different mechanisms. Permeability studies demonstrated that both CP and SX when formulated as solid dispersions showed enhanced permeability across Caco-2 cells and CP can be classified as well-absorbed compound when formulated as solid dispersions. © 2013 Informa Healthcare USA, Inc.

Estimating parameters in stochastic systems:a variational Bayesian approach

This work is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variation of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here a new extended framework is derived that is based on a local polynomial approximation of a recently proposed variational Bayesian algorithm. The paper begins by showing that the new extension of this variational algorithm can be used for state estimation (smoothing) and converges to the original algorithm. However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new approach is validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein–Uhlenbeck process, the exact likelihood of which can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz ’63 (3D model). As a special case the algorithm is also applied to the 40 dimensional stochastic Lorenz ’96 system. In our investigation we compare this new approach with a variety of other well known methods, such as the hybrid Monte Carlo, dual unscented Kalman filter, full weak-constraint 4D-Var algorithm and analyse empirically their asymptotic behaviour as a function of observation density or length of time window increases. In particular we show that we are able to estimate parameters in both the drift (deterministic) and the diffusion (stochastic) part of the model evolution equations using our new methods.

A new variational radial basis function approximation for inference in multivariate diffusions

In this paper we present a radial basis function based extension to a recently proposed variational algorithm for approximate inference for diffusion processes. Inference, for state and in particular (hyper-) parameters, in diffusion processes is a challenging and crucial task. We show that the new radial basis function approximation based algorithm converges to the original algorithm and has beneficial characteristics when estimating (hyper-)parameters. We validate our new approach on a nonlinear double well potential dynamical system.

Mechanisms contributing to the enhanced respiratory burst of neutrophils observed in periodontitis

The aim of this thesis is to investigate possible mechanisms that may contribute to neutrophil hyperactivity and hyper-reactivity. One possibility is the presence of a neutrophil priming factors within the peripheral circulation of periodontitis patients. To examine this possibility differentiated HL-60 cells and primary neutrophils were studied in the presence and absence of plasma from periodontitis patients. In independent experiments, plasma was depleted of IL-8, GM-CSF, interferon-a, immunoglobulins and albumin. This work demonstrated that plasma factors such as IL-8, GM-CSF, and interferon-a present during periodontitis may contribute towards the reported hyperactive neutrophil phenotype. Furthermore, this work demonstrated that products from Pg may regulate neutrophil accumulation at infected periodontal sites by promoting gingipain-dependent modification of IL-8-77 into a more biologically active chemokine. To elucidate whether the oxidatively stressed environment that neutrophils are exposed to in periodontitis could influence hyperactivity and hyper-reactivity, neutrophils were depleted of glutathione. This work showed that during oxidative stress, where cellular redox-levels have been altered, neutrophils exhibit an increased respiratory burst. In conclusion, this work highlights the multiple mechanisms that may contribute to neutrophil hyperactivity and hyperreactivity including gingipain-modulated activity of IL-8 variants, the effect of host factors such as IL-8, GM-CSF, interferon-a on neutrophils priming and activation, and the shift of neutrophil GSH:GSSG ratio in favour of a more oxidised environment as observed in periodontitis.

Activation of the neutrophil respiratory burst by plasma from periodontitis patients is mediated by pro-inflammatory cytokines

Aim: To determine the effect of periodontitis patients' plasma on the neutrophil oxidative burst and the role of albumin, immunoglobulins (Igs) and cytokines. Materials and Methods: Plasma was collected from chronic periodontitis patients (n=11) and periodontally healthy controls (n=11) and used with/without depletion of albumin and Ig or antibody neutralization of IL-8, GM-CSF or IFN-a to prime/stimulate peripheral blood neutrophils, isolated from healthy volunteers. The respiratory burst was measured by lucigenin-dependent chemiluminescence. Plasma cytokine levels were determined by ELISA. Results: Plasmas from patients were significantly more effective in both directly stimulating neutrophil superoxide production and priming for subsequent formyl-met-leu-phe (fMLP)-stimulated superoxide production than plasmas from healthy controls (p<0.05). This difference was maintained after depletion of albumin and Ig. Plasma from patients contained higher mean levels of IL-8, GM-CSF and IFN-a. Individual neutralizing antibodies against IL-8, GM-CSF or IFN-a inhibited the direct stimulatory effect of patients' plasma, whereas the ability to prime for fMLP-stimulated superoxide production was only inhibited by neutralization of IFN-a. The stimulating and priming effects of control plasma were unaffected by antibody neutralization. Conclusions: This study demonstrates that plasma cytokines may have a role in inducing the hyperactive (IL-8, GM-CSF, IFN-a) and hyper-reactive (IFN-a) neutrophil phenotype seen in periodontitis patients.