The corporatization of community pharmacy:implications for service provision, the public health function, and pharmacy's claims to professional status in the United Kingdom

Background Pharmacy has experienced both incomplete professionalization and deprofessionalization. Since the late 1970s, a concerted attempt has been made to re-professionalize pharmacy in the United Kingdom (UK) through role extension—a key feature of which has been a drive for greater pharmacy involvement in public health. However, the continual corporatization of the UK community pharmacy sector may reduce the professional autonomy of pharmacists and may threaten to constrain attempts at reprofessionalization. Objectives The objectives of the research: to examine the public health activities of community pharmacists in the UK; to explore the attitudes of community pharmacists toward recent relevant UK policy and barriers to the development of their public health function; and, to investigate associations between activity, attitudes, and the type of community pharmacy worked in (eg, supermarket, chain, independent). Methods A self-completion postal questionnaire was sent to a random sample of practicing community pharmacists, stratified for country and sex, within Great Britain (n = 1998), with a follow-up to nonresponders 4 weeks later. Data were analyzed using SPSS (SPSS Inc., Chicago, IL, USA) (v12.0). A final response rate of 51% (n = 1023/1998) was achieved. Results The level of provision of emergency hormonal contraception on a patient group direction, supervised administration of medicines, and needle-exchange schemes was lower in supermarket pharmacies than in the other types of pharmacy. Respondents believed that supermarkets and the major multiple pharmacy chains held an advantageous position in terms of attracting financing for service development despite suggesting that the premises of such pharmacies may not be the most suitable for the provision of such services. Conclusions A mixed market in community pharmacy may be required to maintain a comprehensive range of pharmacy-based public health services and provide maximum benefit to all patients. Longitudinal monitoring is recommended to ensure that service provision is adequate across the pharmacy network.

NT2 derived neuronal and astrocytic network signalling

A major focus of stem cell research is the generation of neurons that may then be implanted to treat neurodegenerative diseases. However, a picture is emerging where astrocytes are partners to neurons in sustaining and modulating brain function. We therefore investigated the functional properties of NT2 derived astrocytes and neurons using electrophysiological and calcium imaging approaches. NT2 neurons (NT2Ns) expressed sodium dependent action potentials, as well as responses to depolarisation and the neurotransmitter glutamate. NT2Ns exhibited spontaneous and coordinated calcium elevations in clusters and in extended processes, indicating local and long distance signalling. Tetrodotoxin sensitive network activity could also be evoked by electrical stimulation. Similarly, NT2 astrocytes (NT2As) exhibited morphology and functional properties consistent with this glial cell type. NT2As responded to neuronal activity and to exogenously applied neurotransmitters with calcium elevations, and in contrast to neurons, also exhibited spontaneous rhythmic calcium oscillations. NT2As also generated propagating calcium waves that were gap junction and purinergic signalling dependent. Our results show that NT2 derived astrocytes exhibit appropriate functionality and that NT2N networks interact with NT2A networks in co-culture. These findings underline the utility of such cultures to investigate human brain cell type signalling under controlled conditions. Furthermore, since stem cell derived neuron function and survival is of great importance therapeutically, our findings suggest that the presence of complementary astrocytes may be valuable in supporting stem cell derived neuronal networks. Indeed, this also supports the intriguing possibility of selective therapeutic replacement of astrocytes in diseases where these cells are either lost or lose functionality.

Beta frequency neuronal network activity in the primary motor cortex

In this study I investigated the mechanisms of neuronal network oscillatory activity in rat M1 using pharmacological manipulations and electrical stimulation protocols, employing the in vitro brain slice technique in rat and magnetoencephalography (MEG) in man. Co-application of kainic acid and carbachol generated in vitro beta oscillatory activity in all layers in M1. Analyses indicated that oscillations originated from deep layers and indicated significant involvement of GABAA receptors and gap junctions. A modulatory role of GABAB, NMDA, and dopamine receptors was also evident. Intracellular recordings from fast-spiking (FS) GABAergic inhibitory cells revealed phase-locked action potentials (APs) on every beta cycle. Glutamatergic excitatory regular-spiking (RS) and intrinsically-bursting (IB) cells both received phase locked inhibitory postsynaptic potentials, but did not fire APs on every cycle, suggesting the dynamic involvement of different pools of neurones in the overall population oscillations. Stimulation evoked activity at high frequency (HFS; 125Hz) evoked gamma oscillations and reduced ongoing beta activity. 20Hz stimulation promoted theta or gamma oscillations whilst 4Hz stimulation enhanced beta power at theta frequency. I also investigated the modulation of pathological slow wave (theta and beta) oscillatory activity using magnetoencephalography. Abnormal activity was suppressed by sub-sedative doses of GABAA receptor modulator zolpidem and the observed desynchronising effect correlated well with improved sensorimotor function. These studies indicate a fundamental role for inhibitory neuronal networks in the patterning beta activity and suggest that cortical HFS in PD re-patterns abnormally enhanced M1 network activity by modulating the activity of FS cells. Furthermore, pathological oscillation may be common to many neuropathologies and may be an important future therapeutic target.

Finding structures in information networks using the affinity network

This thesis proposes a novel graphical model for inference called the Affinity Network,which displays the closeness between pairs of variables and is an alternative to Bayesian Networks and Dependency Networks. The Affinity Network shares some similarities with Bayesian Networks and Dependency Networks but avoids their heuristic and stochastic graph construction algorithms by using a message passing scheme. A comparison with the above two instances of graphical models is given for sparse discrete and continuous medical data and data taken from the UCI machine learning repository. The experimental study reveals that the Affinity Network graphs tend to be more accurate on the basis of an exhaustive search with the small datasets. Moreover, the graph construction algorithm is faster than the other two methods with huge datasets. The Affinity Network is also applied to data produced by a synchronised system. A detailed analysis and numerical investigation into this dynamical system is provided and it is shown that the Affinity Network can be used to characterise its emergent behaviour even in the presence of noise.

Modulation of network oscillatory activity and GABAergic synaptic transmission by CB1 cannabinoid receptors in the rat medial entorhinal cortex

Cannabinoids modulate inhibitory GABAergic neurotransmission in many brain regions. Within the temporal lobe, cannabinoid receptors are highly expressed, and are located presynaptically at inhibitory terminals. Here, we have explored the role of type-1 cannabinoid receptors (CB1Rs) at the level of inhibitory synaptic currents and field-recorded network oscillations. We report that arachidonylcyclopropylamide, an agonist at CB1R, inhibits GABAergic synaptic transmission onto both superficial and deep medial entorhinal (mEC) neurones, but this has little effect on network oscillations in beta/gamma frequency bands. By contrast, the CB1R antagonist/inverse agonist LY320135 (500?nM), increased GABAergic synaptic activity and beta/gamma oscillatory activity in superficial mEC, was suppressed, whilst that in deep mEC was enhanced. These data indicate that cannabinoid-mediated effects on inhibitory synaptic activity may be constitutively active in vitro, and that modulation of CB1R activation using inverse agonists unmasks complex effects of CBR function on network activity.

An examination of the calcium and strontium site distribution in bioactive glasses through isomorphic neutron diffraction, X-ray diffraction, EXAFS and multinuclear solid state NMR.

Strontium has been substituted for calcium in the glass series (SiO2)49.46(Na2O)26.38(P2O5)1.07(CaO)23.08x(SrO)x (where x = 0, 11.54, 23.08) to elucidate their underlying atomic-scale structural characteristics as a basis for understanding features related to the bioactivity. These bioactive glasses have been investigated using isomorphic neutron and X-ray diffraction, Sr K-edge EXAFS and solid state 17O, 23Na, 29Si, 31P and 43Ca magic-angle-spinning (MAS) NMR. An effective isomorphic substitution first-order difference function has been applied to the neutron diffraction data, confirming that Ca and Sr behave in a similar manner within the glass network, with residual differences attributed to solely the variation in ionic radius between the two species. The diffraction data provides the first direct experimental evidence of split Ca–O nearest-neighbour correlations in these melt quench bioactive glasses, together with an analogous splitting of the Sr–O correlations; the correlations are attributed to the metal ions correlated either to bridging or to non-bridging oxygen atoms. Triple quantum (3Q) 43Ca MAS NMR corroborates the split Ca–O correlations. Successful simplification of the 2 < r (A) < 3 region via the difference method has also revealed two distinct Na environments. These environments are attributed to sodium correlated either to bridging or to nonbridging oxygen atoms. Complementary multinuclear MAS NMR, Sr K-edge EXAFS and X-ray diffraction data supports the structural model presented. The structural sites present will be intimately related to their release properties in physiological fluids such as plasma and saliva, and hence the bioactivity of the material. Detailed structural knowledge is therefore a prerequisite for optimising material design.

A network coding based interference cancelation scheme for wireless ad hoc networks

The performance of wireless networks is limited by multiple access interference (MAI) in the traditional communication approach where the interfered signals of the concurrent transmissions are treated as noise. In this paper, we treat the interfered signals from a new perspective on the basis of additive electromagnetic (EM) waves and propose a network coding based interference cancelation (NCIC) scheme. In the proposed scheme, adjacent nodes can transmit simultaneously with careful scheduling; therefore, network performance will not be limited by the MAI. Additionally we design a space segmentation method for general wireless ad hoc networks, which organizes network into clusters with regular shapes (e.g., square and hexagon) to reduce the number of relay nodes. The segmentation methodworks with the scheduling scheme and can help achieve better scalability and reduced complexity. We derive accurate analytic models for the probability of connectivity between two adjacent cluster heads which is important for successful information relay. We proved that with the proposed NCIC scheme, the transmission efficiency can be improved by at least 50% for general wireless networks as compared to the traditional interference avoidance schemes. Numeric results also show the space segmentation is feasible and effective. Finally we propose and discuss a method to implement the NCIC scheme in a practical orthogonal frequency division multiplexing (OFDM) communications networks. Copyright © 2009 John Wiley & Sons, Ltd.

Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an internet social network

Background: Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective: To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods: Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results: Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions: This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

Design and analysis of distributed utility maximization algorithm for multihop wireless network with inaccurate feedback

Distributed network utility maximization (NUM) is receiving increasing interests for cross-layer optimization problems in multihop wireless networks. Traditional distributed NUM algorithms rely heavily on feedback information between different network elements, such as traffic sources and routers. Because of the distinct features of multihop wireless networks such as time-varying channels and dynamic network topology, the feedback information is usually inaccurate, which represents as a major obstacle for distributed NUM application to wireless networks. The questions to be answered include if distributed NUM algorithm can converge with inaccurate feedback and how to design effective distributed NUM algorithm for wireless networks. In this paper, we first use the infinitesimal perturbation analysis technique to provide an unbiased gradient estimation on the aggregate rate of traffic sources at the routers based on locally available information. On the basis of that, we propose a stochastic approximation algorithm to solve the distributed NUM problem with inaccurate feedback. We then prove that the proposed algorithm can converge to the optimum solution of distributed NUM with perfect feedback under certain conditions. The proposed algorithm is applied to the joint rate and media access control problem for wireless networks. Numerical results demonstrate the convergence of the proposed algorithm. © 2013 John Wiley & Sons, Ltd.

The impact of the CACNA1C gene polymorphism on frontolimbic function in bipolar disorder

Genome-wide association studies in bipolar disorder (BD)1 have implicated a single-nucleotide polymorphism (rs1006737, G right arrow A) in the CACNA1C gene, which encodes for the alpha 1c (CAV1.2) subunit of the voltage-gated, L-type calcium channel. Neuroimaging studies of healthy individuals report that this risk allele modulates brain function within limbic (amygdala, anterior cingulate gyrus) and hippocampal regions during tasks of reward processing2, 3 and episodic memory. Moreover, animal studies suggest that the CaV1.2 L-type calcium channels influence emotional behaviour through enhanced neurotransmission via the lateral amygdala pathway. On the basis of this evidence, we tested the hypotheses that the CACNA1C rs1006737 risk allele will modulate neural responses within predefined prefrontal and subcortical regions of interest during emotional face processing and that this effect would be amplified in BD patients.

Independent modulation of engagement and connectivity of the facial network during affect processing by CACNA1C and ANK3 risk genes for bipolar disorder

IMPORTANCE Genome-wide association studies (GWASs) indicate that single-nucleotide polymorphisms in the CACNA1C and ANK3 genes increase the risk for bipolar disorder (BD). The genes influence neuronal firing by modulating calcium and sodium channel functions, respectively. Both genes modulate ?-aminobutyric acid-transmitting interneuron function and can thus affect brain regional activation and interregional connectivity. OBJECTIVE To determine whether the genetic risk for BD associated with 2 GWAS-supported risk single-nucleotide polymorphisms at CACNA1C rs1006737 and ANK3 rs10994336 is mediated through changes in regional activation and interregional connectivity of the facial affect-processing network. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional functional magnetic resonance imaging study at a research institute of 41 euthymic patients with BD and 46 healthy participants, all of British white descent. MAIN OUTCOMES AND MEASURES Blood oxygen level-dependent signal and effective connectivity measures during the facial affect-processing task. RESULTS In healthy carriers, both genetic risk variants were independently associated with increased regional engagement throughout the facial affect-processing network and increased effective connectivity between the visual and ventral prefrontal cortical regions. In contrast, BD carriers of either genetic risk variant exhibited pronounced reduction in ventral prefrontal cortical activation and visual-prefrontal effective connectivity. CONCLUSIONS AND RELEVANCE Our data demonstrate that the effect of CACNA1C rs1006737 and ANK3 rs10994336 (or genetic variants in linkage disequilibrium) on the brain converges on the neural circuitry involved in affect processing and provides a mechanism linking BD to genome-wide genetic risk variants.

Hypoxia-inducible factor (HIF) network:insights from mathematical models

Oxygen is a crucial molecule for cellular function. When oxygen demand exceeds supply, the oxygen sensing pathway centred on the hypoxia inducible factor (HIF) is switched on and promotes adaptation to hypoxia by up-regulating genes involved in angiogenesis, erythropoiesis and glycolysis. The regulation of HIF is tightly modulated through intricate regulatory mechanisms. Notably, its protein stability is controlled by the oxygen sensing prolyl hydroxylase domain (PHD) enzymes and its transcriptional activity is controlled by the asparaginyl hydroxylase FIH (factor inhibiting HIF-1).To probe the complexity of hypoxia-induced HIF signalling, efforts in mathematical modelling of the pathway have been underway for around a decade. In this paper, we review the existing mathematical models developed to describe and explain specific behaviours of the HIF pathway and how they have contributed new insights into our understanding of the network. Topics for modelling included the switch-like response to decreased oxygen gradient, the role of micro environmental factors, the regulation by FIH and the temporal dynamics of the HIF response. We will also discuss the technical aspects, extent and limitations of these models. Recently, HIF pathway has been implicated in other disease contexts such as hypoxic inflammation and cancer through crosstalking with pathways like NF?B and mTOR. We will examine how future mathematical modelling and simulation of interlinked networks can aid in understanding HIF behaviour in complex pathophysiological situations. Ultimately this would allow the identification of new pharmacological targets in different disease settings.

A loss-of-function screen reveals SNX5 and SNX6 as potential components of the mammalian retromer

The mammalian retromer is a multimeric protein complex involved in mediating endosome-to-trans-Golgi-network retrograde transport of the cation-independent mannose-6-phosphate receptor. The retromer is composed of two subcomplexes, one containing SNX1 and forming a membrane-bound coat, the other comprising VPS26, VPS29 and VPS35 and being cargo-selective. In yeast, an additional sorting nexin--Vps17p--is a component of the membrane bound coat. It remains unclear whether the mammalian retromer requires a functional equivalent of Vps17p. Here, we have used an RNAi loss-of-function screen to examine whether any of the other 30 mammalian sorting nexins are required for retromer-mediated endosome-to-trans-Golgi-network retrieval of the cation-independent mannose-6-phosphate receptor. Using this screen, we identified two proteins, SNX5 and SNX6, that, when suppressed, induced a phenotype similar to that observed upon suppression of known retromer components. Whereas SNX5 and SNX6 colocalised with SNX1 on early endosomes, in immunoprecipitation experiments only SNX6 appeared to exist in a complex with SNX1. Interestingly, suppression of SNX5 and/or SNX6 resulted in a significant loss of SNX1, an effect that seemed to result from post-translational regulation of the SNX1 level. Such data suggest that SNX1 and SNX6 exist in a stable, endosomally associated complex that is required for retromer-mediated retrieval of the cation-independent mannose-6-phosphate receptor. SNX5 and SNX6 may therefore constitute functional equivalents of Vps17p in mammals.

Learning and change in interorganizational networks:the case for network learning and network change

The ALBA 2002 Call for Papers asks the question ‘How do organizational learning and knowledge management contribute to organizational innovation and change?’. Intuitively, we would argue, the answer should be relatively straightforward as links between learning and change, and knowledge management and innovation, have long been commonly assumed to exist. On the basis of this assumption, theories of learning tend to focus ‘within organizations’, and assume a transfer of learning from individual to organization which in turn leads to change. However, empirically, we find these links are more difficult to articulate. Organizations exist in complex embedded economic, political, social and institutional systems, hence organizational change (or innovation) may be influenced by learning in this wider context. Based on our research in this wider interorganizational setting, we first make the case for the notion of network learning that we then explore to develop our appreciation of change in interorganizational networks, and how it may be facilitated. The paper begins with a brief review of lite rature on learning in the organizational and interorganizational context which locates our stance on organizational learning versus the learning organization, and social, distributed versus technical, centred views of organizational learning and knowledge. Developing from the view that organizational learning is “a normal, if problematic, process in every organization” (Easterby-Smith, 1997: 1109), we introduce the notion of network learning: learning by a group of organizations as a group. We argue this is also a normal, if problematic, process in organizational relationships (as distinct from interorganizational learning), which has particular implications for network change. Part two of the paper develops our analysis, drawing on empirical data from two studies of learning. The first study addresses the issue of learning to collaborate between industrial customers and suppliers, leading to the case for network learning. The second, larger scale study goes on to develop this theme, examining learning around several major change issues in a healthcare service provider network. The learning processes and outcomes around the introduction of a particularly controversial and expensive technology are described, providing a rich and contrasting case with the first study. In part three, we then discuss the implications of this work for change, and for facilitating change. Conclusions from the first study identify potential interventions designed to facilitate individual and organizational learning within the customer organization to develop individual and organizational ‘capacity to collaborate’. Translated to the network example, we observe that network change entails learning at all levels – network, organization, group and individual. However, presenting findings in terms of interventions is less meaningful in an interorganizational network setting given: the differences in authority structures; the less formalised nature of the network setting; and the importance of evaluating performance at the network rather than organizational level. Academics challenge both the idea of managing change and of managing networks. Nevertheless practitioners are faced with the issue of understanding and in fluencing change in the network setting. Thus we conclude that a network learning perspective is an important development in our understanding of organizational learning, capability and change, locating this in the wider context in which organizations are embedded. This in turn helps to develop our appreciation of facilitating change in interorganizational networks, both in terms of change issues (such as introducing a new technology), and change orientation and capability.

Network density and R&D spillovers

This paper models how the structure and function of a network of firms affects their aggregate innovativeness. Each firm has the potential to innovate, either from in-house R&D or from innovation spillovers from neighboring firms. The nature of innovation spillovers depends upon network density, the commonality of knowledge between firms, and the learning capability of firms. Innovation spillovers are modelled in detail using ideas from organizational theory. Two main results emerge: (i) the marginal effect on innovativeness of spillover intensity is non-monotonic, and (ii) network density can affect innovativeness but only when there are heterogeneous firms.