Characterization of n-Hexane sub-fraction of Bridelia micrantha (Berth) and its antimycobacterium activity

<p>Abstract</p> <p>Background</p> <p>Tuberculosis, caused by <it>Mycobacterium tuberculosis </it>(MTB), is the most notified disease in the world. Development of resistance to first line drugs by MTB is a public health concern. As a result, there is the search for new and novel sources of antimycobacterial drugs for example from medicinal plants. In this study we determined the <it>in vitro </it>antimycobacterial activity of <it>n</it>-Hexane sub-fraction from <it>Bridelia micrantha </it>(Berth) against MTB H₃₇Ra and a clinical isolate resistant to all five first-line antituberculosis drugs.</p> <p>Methods</p> <p>The antimycobacterial activity of the <it>n</it>-Hexane sub-fraction of ethyl acetate fractions from acetone extracts of <it>B. micrantha </it>barks was evaluated using the resazurin microplate assay against two MTB isolates. Bioassay-guided fractionation of the ethyl acetate fraction was performed using 100% <it>n</it>-Hexane and Chloroform/Methanol (99:1) as solvents in order of increasing polarity by column chromatography and Resazurin microtiter plate assay for susceptibility tests.</p> <p>Results</p> <p>The <it>n</it>-Hexane fraction showed 20% inhibition of MTB H₃₇Ra and almost 35% inhibition of an MTB isolate resistant to all first-line drugs at 10 μg/mL. GC/MS analysis of the fraction resulted in the identification of twenty-four constituents representing 60.5% of the fraction. Some of the 24 compounds detected included Benzene, 1.3-bis (3-phenoxyphenoxy (13.51%), 2-pinen-4-one (10.03%), N(b)-benzyl-14-(carboxymethyl) (6.35%) and the least detected compound was linalool (0.2%).</p> <p>Conclusions</p> <p>The results show that the <it>n-</it>Hexane fraction of <it>B. micrantha </it>has antimycobacterial activity.</p

Projected Range Contractions of European Protected Oceanic Montane Plant Communities: Focus on Climate Change Impacts Is Essential for Their Future Conservation

Global climate is rapidly changing and while many studies have investigated the potential impacts of this on the distribution of montane plant species and communities, few have focused on those with oceanic montane affinities. In Europe, highly sensitive bryophyte species reach their optimum occurrence, highest diversity and abundance in the northwest hyperoceanic regions, while a number of montane vascular plant species occur here at the edge of their range. This study evaluates the potential impact of climate change on the distribution of these species and assesses the implications for EU Habitats Directive-protected oceanic montane plant communities. We applied an ensemble of species distribution modelling techniques, using atlas data of 30 vascular plant and bryophyte species, to calculate range changes under projected future climate change. The future effectiveness of the protected area network to conserve these species was evaluated using gap analysis. We found that the majority of these montane species are projected to lose suitable climate space, primarily at lower altitudes, or that areas of suitable climate will principally shift northwards. In particular, rare oceanic montane bryophytes have poor dispersal capacity and are likely to be especially vulnerable to contractions in their current climate space. Significantly different projected range change responses were found between 1) oceanic montane bryophytes and vascular plants; 2) species belonging to different montane plant communities; 3) species categorised according to different biomes and eastern limit classifications. The inclusion of topographical variables in addition to climate, significantly improved the statistical and spatial performance of models. The current protected area network is projected to become less effective, especially for specialised arctic-montane species, posing a challenge to conserving oceanic montane plant communities. Conservation management plans need significantly greater focus on potential climate change impacts, including models with higher-resolution species distribution and environmental data, to aid these communities’ long-term survival

Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns?

The objective of this study was to evaluate the performance of stacked species distribution models in predicting the alpha and gamma species diversity patterns of two important plant clades along elevation in the Andes. We modelled the distribution of the species in the Anthurium genus (53 species) and the Bromeliaceae family (89 species) using six modelling techniques. We combined all of the predictions for the same species in ensemble models based on two different criteria: the average of the rescaled predictions by all techniques and the average of the best techniques. The rescaled predictions were then reclassified into binary predictions (presence/absence). By stacking either the original predictions or binary predictions for both ensemble procedures, we obtained four different species richness models per taxa. The gamma and alpha diversity per elevation band (500 m) was also computed. To evaluate the prediction abilities for the four predictions of species richness and gamma diversity, the models were compared with the real data along an elevation gradient that was independently compiled by specialists. Finally, we also tested whether our richness models performed better than a null model of altitudinal changes of diversity based on the literature. Stacking of the ensemble prediction of the individual species models generated richness models that proved to be well correlated with the observed alpha diversity richness patterns along elevation and with the gamma diversity derived from the literature. Overall, these models tend to overpredict species richness. The use of the ensemble predictions from the species models built with different techniques seems very promising for modelling of species assemblages. Stacking of the binary models reduced the over-prediction, although more research is needed. The randomisation test proved to be a promising method for testing the performance of the stacked models, but other implementations may still be developed