Double-blind, randomised, placebo controlled, parallel group study of sativex® in the treatment of patients with peripheral neuropathic pain, associated with allodynia [poster]

No abstract available

Comparison of analgesic effects and patient tolerability of nabilone and dihydrocodeine for chronic neuropathic pain: randomised, crossover, double blind study

<b>Objective</b>: To compare the analgesic efficacy and side effects of the synthetic cannabinoid nabilone with those of the weak opioid dihydrocodeine for chronic neuropathic pain. <b>Design</b>: Randomised, double blind, crossover trial of 14 weeks’ duration comparing dihydrocodeine and nabilone. <b>Setting</b>: Outpatient units of three hospitals in the United Kingdom. <b>Participants</b>: 96 patients with chronic neuropathic pain, aged 23-84 years. <b>Main outcome measures</b>: The primary outcome was difference between nabilone and dihydrocodeine in pain, as measured by the mean visual analogue score computed over the last 2 weeks of each treatment period. Secondary outcomes were changes in mood, quality of life, sleep, and psychometric function. Side effects were measured by a questionnaire. <b>Intervention</b>: Patients received a maximum daily dose of 240 mg dihydrocodeine or 2 mg nabilone at the end of each escalating treatment period of 6 weeks. Treatment periods were separated by a 2 week washout period. <b>Results</b>: Mean baseline visual analogue score was 69.6 mm (range 29.4-95.2) on a 0-100 mm scale. 73 patients were included in the available case analysis and 64 patients in the per protocol analysis. The mean score was 6.0 mm longer for nabilone than for dihydrocodeine (95% confidence interval 1.4 to 10.5) in the available case analysis and 5.6 mm (10.3 to 0.8) in the per protocol analysis. Side effects were more frequent with nabilone. <b>Conclusion</b>: Dihydrocodeine provided better pain relief than the synthetic cannabinoid nabilone and had slightly fewer side effects, although no major adverse events occurred for either drug

Results from a randomized, controlled trial on efficacy and tolerability of a 5% Lidocaine-Medicated Plaster vs. Pregabalin in patients with Post-Herpetic Neuralgia (PHN) and Painful Diabetic Polyneuropathy (DPN) [poster]

No abstract available

Effects of HIV status and linguistic medium on the test performance of rural low-literacy adults: implications for neuropsychological test development in Zambia

Purpose of the study: The purpose of the study was to determine whether the familiar language (Chichewa version) could contribute to the early diagnosis of neurocognitive dysfunctions and develop a battery of locally valid tests capable of detecting early changes in the cognitive profile of neurocognitive dysfunctions among HIV positive patients.Research question one: What is the difference in performance between HIV negative and HIV positive individuals when they are subjected to the four verbal tests of the neuropsychological test battery using the English and Chichewa versions?Research question two: What is the interaction effect among the influences of HIV status, linguistic medium and gender on the four verbal tests of the neuropsychological test battery?Design: It was an experimental design that assessed the neuropsychological effects of HIV status and linguistic medium on the test performance. The Hopkins Verbal Learning Test (HVLT-R) for both immediate and delayed recall were used to test the verbal episodic memory from the Verbal Learning and Memory Recall Domain (Brandt and Benedict, 2001). Other tests included Animal and Action Naming. These tests were translated into Chichewa and administered to 28 HIV positive and 22 HIV negative rural low illiterate adults aged between 40 and 65 years.Results: On all the Neuropsychological tests administered, HIV positive respondents scored significantly lower than HIV negative respondents, and the mean scores on the English medium version were consistently lower than scores on the L1 (Chichewa) version across all tests and all groups.Conclusion: The study has shown that the primary language is best suited to test neurocognitive performance and especially when one is using test components that do not require reading or writing

N ‐Functionalised Imidazoles as Stabilisers for Metal Nanoparticles in Catalysis and Anion Binding

Metal nanoparticles (NPs) have physicochemical properties which are distinct from both the bulk and molecular metal species, and provide opportunities in fields such as catalysis and sensing. NPs typically require protection of their surface to impede aggregation, but these coatings can also block access to the surface which would be required to take advantage of their unusual properties. Here, we show that alkyl imidazoles can stabilise Pd, Pt, Au, and Ag NPs, and delineate the limits of their synthesis. These ligands provide an intermediate level of surface protection, for which we demonstrate proof‐of‐principle in catalysis and anion binding

The helix-hairpin-helix DNA-binding motif: A structural basis for non-sequence-specific recognition of DNA

One, two or four copies of the 'helix-hairpin-helix' (HhH) DNA-binding motif are predicted to occur in 14 homologous families of proteins. The predicted DNA-binding function of this motif is shown to be consistent with the crystallographic structure of rat polymerase ß, complexed with DNA template-primer and with biochemical data. Five crystal structures of predicted HhH motifs are currently known: two from rat pol ß and one each in endonuclease III, AlkA and the 5' nuclease domain of Taq pol I. These motifs are more structurally similar to each other than to any other structure in current databases, including helix-turn-helix motifs. The clustering of the five HhH structures separately from other bi-helical structures in searches indicates that all members of the 14 families of proteins described herein possess similar HhH structures. By analogy with the rat pol ß structure, it is suggested that each of these HhH motifs bind DNA in a non-sequence-specific manner, via the formation of hydrogen bonds between protein backbone nitrogens and DNA phosphate groups. This type of interaction contrasts with the sequence-specific interactions of other motifs, including helix-turn-helix structures. Additional evidence is provided that alphaherpesvirus virion host shutoff proteins are members of the polymerase I 5'-nuclease and FEN1-like endonuclease gene family, and that a novel HhH-containing DNA-binding domain occurs in the kinesin-like molecule nod, and in other proteins such as cnjB, emb-5 and SPT6

Quantification of amyloid fibril polymorphism by nano-morphometry reveals the individuality of filament assembly

Amyloid fibrils are highly polymorphic structures formed by many different proteins. They provide biological function but also abnormally accumulate in numerous human diseases. The physicochemical principles of amyloid polymorphism are not understood due to lack of structural insights at the single-fibril level. To identify and classify different fibril polymorphs and to quantify the level of heterogeneity is essential to decipher the precise links between amyloid structures and their functional and disease associated properties such as toxicity, strains, propagation and spreading. Employing gentle, force-distance curve-based AFM, we produce detailed images, from which the 3D reconstruction of individual filaments in heterogeneous amyloid samples is achieved. Distinctive fibril polymorphs are then classified by hierarchical clustering, and sample heterogeneity is objectively quantified. These data demonstrate the polymorphic nature of fibril populations, provide important information regarding the energy landscape of amyloid self-assembly, and offer quantitative insights into the structural basis of polymorphism in amyloid populations

Protein fiber linear dichroism for structure determination and kinetics in a low-volume, low-wavelength couette flow cell

High-resolution structure determination of soluble globular proteins relies heavily on x-ray crystallography techniques. Such an approach is often ineffective for investigations into the structure of fibrous proteins as these proteins generally do not crystallize. Thus investigations into fibrous protein structure have relied on less direct methods such as x-ray fiber diffraction and circular dichroism. Ultraviolet linear dichroism has the potential to provide additional information on the structure of such biomolecular systems. However, existing systems are not optimized for the requirements of fibrous proteins. We have designed and built a low-volume (200 μL), low-wavelength (down to 180 nm), low-pathlength (100 μm), high-alignment flow-alignment system (couette) to perform ultraviolet linear dichroism studies on the fibers formed by a range of biomolecules. The apparatus has been tested using a number of proteins for which longer wavelength linear dichroism spectra had already been measured. The new couette cell has also been used to obtain data on two medically important protein fibers, the all-β-sheet amyloid fibers of the Alzheimer's derived protein Aβ and the long-chain assemblies of α1-antitrypsin polymers

Self-adaptation of mutation operator and probability for permutation representations in genetic algorithms

The choice of mutation rate is a vital factor in the success of any genetic algorithm (GA), and for permutation representations this is compounded by the availability of several alternative mutation operators. It is now well understood that there is no one "optimal choice"; rather, the situation changes per problem instance and during evolution. This paper examines whether this choice can be left to the processes of evolution via selfadaptation, thus removing this nontrivial task fromtheGAuser and reducing the risk of poor performance arising from (inadvertent) inappropriate decisions. Self-adaptation has been proven successful for mutation step sizes in the continuous domain, and for the probability of applying bitwise mutation to binary encodings; here we examine whether this can translate to the choice and parameterisation of mutation operators for permutation encodings. We examine one method for adapting the choice of operator during runtime, and several different methods for adapting the rate at which the chosen operator is applied. In order to evaluate these algorithms, we have used a range of benchmark TSP problems. Of course this paper is not intended to present a state of the art in TSP solvers; rather, we use this well known problem as typical of many that require a permutation encoding, where our results indicate that self-adaptation can prove beneficial. The results show that GAs using appropriate methods to self-adapt their mutation operator and mutation rate find solutions of comparable or lower cost than algorithms with "static" operators, even when the latter have been extensively pretuned. Although the adaptive GAs tend to need longer to run, we show that is a price well worth paying as the time spent finding the optimal mutation operator and rate for the nonadaptive versions can be considerable. Finally, we evaluate the sensitivity of the self-adaptive methods to changes in the implementation, and to the choice of other genetic operators and population models. The results show that the methods presented are robust, in the sense that the performance benefits can be obtained in a wide range of host algorithms. © 2010 by the Massachusetts Institute of Technology

An artificial intelligence approach to predicting personality types in dogs

Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs’ behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable’s contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: “Excitable/Hyperattached”, “Anxious/Fearful”, “Aloof/Predatory”, “Reactive/Assertive”, and “Calm/Agreeable”. Four machine learning models including Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree were implemented to predict the personality traits of dogs based on the labelled data. The performance of the models was evaluated using fivefold cross validation method and the results demonstrated that the Decision Tree model provided the best performance with a substantial accuracy of 99%. The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles