Extension of the EU 'Traditional Herbal Medicine' concept to an oral transmission context: the case of the 5 anti-infectious medicinal plants most widely used in Burundi

peer reviewedIntroduction: In Burundi, five plants, namely Urtica massaica Mildbr., Mikania natalensis DC., Senecio maranguensis O. Hoffm., Justicia nyassana Lindau and Helichrysum congolanum Schltr. & O. Foffm., are widely cited for the treatment of "diseases compatible with a microbial infection" i.e. probably infectious diseases. In view to derive a regional concept of plausible activity and safety, akin to the European Union notion of "Traditional Herbal Medicine", the present work aims to compare the local knowledge and uses of these five popular anti-infectious plants, including eventual recommendations and interdicts. Materials and Methods: A survey was carried out among 43 traditional healers from different regions of Burundi, including the city of Bujumbura, to fully repertory the uses of these five medicinal plants and define consensus in their uses through their fidelity levels and use values for each cited disease. Phytochemical analyses of these plants allowed to identify their main classes of secondary metabolites. Results: From their fidelity levels, the studied plants appear extensively reported for infectious diseases, except for U. massaica, that is mainly used in inflammatory conditions. M. natalensis has the highest use value for the treatment of skin diseases (use value, 1.65), digestive tract disorders (1.07) and gastrointestinal infections (0.51); U. massaica for inflammation (1.07), digestive tract (0.51) and metabolic disorders (0.42); J. nyassana for gastrointestinal infections (2.00), skin diseases (0.81) and circulatory system disorders (0.51); S. maranguensis (2.60) and H. congolanum (2.49) for skin diseases. Conclusions: Interviews are a quite interesting survey method to apprehend usages of herbal drugs, but the information on their efficacy, side effects and interdicts is particularly difficult to obtain. In the absence of clinical trial data, the marked convergence of some usages nevertheless indicates a plausibility of efficacy and safety, coherent with the EU concept of "Traditional Herbal Medicine", which point to possible rational recommendations of treatments. There however remains a need for a strategy to obtain reliable safety information and to legally define whether a given use can be considered as "traditional".3. Good health and well-bein

Antimalarial herbal remedies of Bukavu and Uvira areas in DR Congo: An ethnobotanical survey

peer reviewedEthnopharmacological relevance: The main objective of the present study was to collect and gather information on herbal remedies traditionally used for the treatment of malaria in Bukavu and Uvira, two towns of the South Kivu province in DRC. Material and methods: Direct interview with field enquiries allowed collecting ethnobotanical data; for each plant, a specimen was harvested in the presence of the interviewed traditional healers (THs). The recorded information includes vernacular names and parts of plants, methods of preparation and administration of remedies, dosage and treatment duration. Plants were identified with the help of botanists in the herbaria of INERA/KIPOPO (DRC) and the Botanic Garden of Meise (Belgium), where voucher specimens have been deposited. The Relative Frequencies of Citations (RFC) have allowed to evaluate the local importance of each plant species. Results: Interviewees cited 45 plant species belonging to 41 genera and 21 families used for the treatment of malaria. These plants participate in the preparation of 52 recipes including 25 multi-herbal recipes and 27 mono-herbal recipes. Apart of Cinchona officinalis L. (Rubiaceae), the plant with highest importance (RFC = 0.72), the study has highlighted that the most represented families are Compositae with 12 species (26 %), followed by Leguminosae with 7 species (16 %) and Rubiaceae with 4 species (9 %). For a majority of plants, herbal medicines are prepared from the leaves in the form of decoction and administered by oral route. Conclusion: The populations of Bukavu and Uvira have identified plants that are used for the treatment of malaria. Several of the highly cited plants should be investigated in details for the isolation and identification of the active ingredients, a contribution to the discovery of new possibly effective antimalarials

Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

Changes in synaptic efficacies need to be long-lasting in order to serve as a substrate for memory. Experimentally, synaptic plasticity exhibits phases covering the induction of long-term potentiation and depression (LTP/LTD) during the early phase of synaptic plasticity, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation during the late phase of synaptic plasticity. We present a mathematical model that describes these different phases of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency. The stabilization of potentiated synapses during the transition from early to late LTP occurs by protein synthesis dynamics that are shared by groups of synapses. The functional consequence of this shared process is that previously stabilized patterns of strong or weak synapses onto the same postsynaptic neuron are well protected against later changes induced by LTP/LTD protocols at individual synapses

Phenomenological models of synaptic plasticity based on spike timing

Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations

Calcium control of triphasic hippocampal STDP

Bush D, Jin Y. Calcium control of triphasic hippocampal STDP. Journal of Computational Neuroscience. 2012;33(3):495-514.Synaptic plasticity is believed to represent the neural correlate of mammalian learning and memory function. It has been demonstrated that changes in synaptic conductance can be induced by approximately synchronous pairings of pre- and post- synaptic action potentials delivered at low frequencies. It has also been established that NMDAr-dependent calcium influx into dendritic spines represents a critical signal for plasticity induction, and can account for this spike-timing dependent plasticity (STDP) as well as experimental data obtained using other stimulation protocols. However, subsequent empirical studies have delineated a more complex relationship between spike-timing, firing rate, stimulus duration and post-synaptic bursting in dictating changes in the conductance of hippocampal excitatory synapses. Here, we present a detailed biophysical model of single dendritic spines on a CA1 pyramidal neuron, describe the NMDAr-dependent calcium influx generated by different stimulation protocols, and construct a parsimonious model of calcium driven kinase and phosphatase dynamics that dictate the probability of stochastic transitions between binary synaptic weight states in a Markov model. We subsequently demonstrate that this approach can account for a range of empirical observations regarding the dynamics of synaptic plasticity induced by different stimulation protocols, under regimes of pharmacological blockade and metaplasticity. Finally, we highlight the strengths and weaknesses of this parsimonious, unified computational synaptic plasticity model, discuss differences between the properties of cortical and hippocampal plasticity highlighted by the experimental literature, and the manner in which further empirical and theoretical research might elucidate the cellular basis of mammalian learning and memory function

Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network

Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly’s halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support