Xanthorrhizol (XNT) is a bisabolane-type sesquiterpenoid compound extracted from Curcuma xanthorrhiza Roxb. It has been well established to possess a variety of biological activities such as anticancer, antimicrobial, anti-inflammatory, antioxidant, antihyperglycemic, antihypertensive, antiplatelet, nephroprotective, hepatoprotective, estrogenic and anti-estrogenic effects. Since many synthetic drugs possess toxic side effects and are unable to support the increasing prevalence of disease, there is significant interest in developing natural product as new therapeutics. XNT is a very potent natural bioactive compound that could fulfil the current need for new drug discovery. Despite its importance, a comprehensive review of XNT’s pharmacological activities has not been published in the scientific literature to date. Here, the present review aims to summarize the available information in this area, focus on its anticancer properties and indicate the current status of the research. This helps to facilitate the understanding of XNT’s pharmacological role in drug discovery, thus suggesting areas where further research is required.

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models.

Type 2 Diabetes Mellitus accounts for 90% of most diabetes cases. Many commercial drugs used to treat this disease come with adverse side effects and eventually fail to restore glucose homeostasis. Therefore, an effective, economical and safe antidiabetic remedy from dietary source is considered. Taraxacum officinale (L.) Weber ex F.H.Wigg and Momordica charantia L. were chosen since both are used for centuries as traditional medicine to treat various ailments and diseases. In this study, the antidiabetic properties of a polyherbal combination of T. officinale and M. charantia ethanol extracts are evaluated. The bioactive solvent extracts of the samples selected from in vitro antidiabetic assays; α-amylase, α-glucosidase, and dipeptidyl peptidase-4 (DPP-4) inhibition, and glucose-uptake in L6 muscle cells were combined (1:1) to form the polyherbal combination. The antidiabetic efficacy of polyherbal combination was evaluated employing the above stated in vitro antidiabetic assays and in vivo oral glucose tolerance test and streptozotocin-nicotinamide (STZ-NA) induced diabetic rat model. A quadrupole time-of-flight liquid chromatography-mass spectrometry (Q-TOF LCMS) analysis was done to identify active compounds. The polyherbal combination exerted improved antidiabetic properties; increased DPP-4, α-amylase, and α-glucosidase inhibition. The polyherbal combination tested in vivo on diabetic rats showed optimum blood glucose-lowering activity comparable to that of Glibenclamide and Metformin. This study confirms the polyherbal combination of T. officinale and M. charantia to be rich in various bioactive compounds, which exhibited antidiabetic properties. Therefore, this polyherbal combination has the potential to be further developed as complex phytotherapeutic remedy for the treatment of Type 2 Diabetes Mellitus.

Background: despite the increase in malaria control and elimination efforts, weather patterns and ecological factors continue to serve as important drivers of malaria transmission dynamics. This study examined the statistical relationship between weather variables and malaria incidence in Abuja, Nigeria. Methodology/Principal Findings: monthly data on malaria incidence and weather variables were collected in Abuja from the year 2000 to 2013. The analysis of count outcomes was based on generalized linear models, while Pearson correlation analysis was undertaken at the bivariate level. The results showed more malaria incidence in the months with the highest rainfall recorded (June–August). Based on the negative binomial model, every unit increase in humidity corresponds to about 1.010 (95% confidence interval (CI), 1.005–1.015) times increase in malaria cases while the odds of having malaria decreases by 5.8% for every extra unit increase in temperature: 0.942 (95% CI, 0.928–0.956). At lag 1 month, there was a significant positive effect of rainfall on malaria incidence while at lag 4, temperature and humidity had significant influences. Conclusions: malaria remains a widespread infectious disease among the local subjects in the study area. Relative humidity was identified as one of the factors that influence a malaria epidemic at lag 0 while the biggest significant influence of temperature was observed at lag 4. Therefore, emphasis should be given to vector control activities and to create public health awareness on the proper usage of intervention measures such as indoor residual sprays to reduce the epidemic especially during peak periods with suitable weather conditions.