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.

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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.

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ObjectiveThe classification of Acute Coronary Syndrome (ACS), using artificial intelligence (AI), has recently drawn the attention of the medical researchers. Using this approach, patients with myocardial infarction can be differentiated from those with unstable angina. The present study aims to develop an integrated model, based on the feature selection and classification, for the automatic classification of ACS.MethodsA dataset containing medical records of 809 patients suspected to suffer from ACS was used. For each subject, 266 clinical factors were collected. At first, a feature selection was performed based on interviews with 20 cardiologists; thereby 40 seminal features for classifying ACS were selected. Next, a feature selection algorithm was also applied to detect a subset of the features with the best classification accuracy. As a result, the feature numbers considerably reduced to only seven. Lastly, based on the seven selected features, eight various common pattern recognition tools for classification of ACS were used.ResultsThe performance of the aforementioned classifiers was compared based on their accuracy computed from their confusion matrices. Among these methods, the multi-layer perceptron showed the best performance with the 83.2% accuracy.ConclusionThe results reveal that an integrated AI-based feature selection and classification approach is an effective method for the early and accurate classification of ACS and ultimately a timely diagnosis and treatment of this disease.

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Plants play vital roles in many health care systems, be it rural or an urban community. Plants became familiar as medicine due to the primordial ideologies and believed. Several plant parts served as medicines to so many ailments including gastrointestinal ailments, due to the fact that their active ingredients are powerful against the microbes as well as healing so many physiological abnormalities. The principal antimicrobial components were used to inhibit the growth of microbes (S. aureus, E. coli, Salmonella spp, B. cereus, and B. subtili,), as well as most of the recognized compounds in most plants were aromatic or saturated organic compounds which enabled the plants to be active against the gastrointestinal microbes. The commonly used diluents were; water, methanol and Di methyl sulphate oxides to ascertain the level of activity of the plants. As such, plant materials in one way or the other are very active when dealing with microbes due to their active ingredients or the phytoconstituents. Most of the microbes identified in many reviewed researches were enteric bacterial species, by which divided into both gram negative and gram positive bacterial isolates, they differ in their cell components, which are the main targets of bioactive constituents to deal with any bacteria. However, certain parasites contributed towards the production of ailments for their survival and causing havoc to the hosts and sometimes be mutualistic.

Hyperlipidemia is defined as the presence of either hypertriglyceridemia or hypercholesterolemia, which could cause atherosclerosis. Although hyperlipidemia can be treated by hypolipidemic drugs, they are limited due to lack of effectiveness and safety. Previous studies demonstrated that xanthorrhizol (XNT) isolated from Curcuma xanthorrhizza Roxb. reduced the levels of free fatty acid and triglyceride in vivo. However, its ability to inhibit cholesterol uptake in HT29 colon cells and adipogenesis in 3T3-L1 cells are yet to be reported. In this study, XNT purified from centrifugal TLC demonstrated 98.3% purity, indicating it could be an alternative purification method. The IC50 values of XNT were 30.81 ± 0.78 μg/mL in HT29 cells and 35.07 ± 0.24 μg/mL in 3T3-L1 adipocytes, respectively. Cholesterol uptake inhibition study using HT29 colon cells showed that XNT (15 μg/mL) significantly inhibited the fluorescent cholesterol analogue NBD uptake by up to 27 ± 3.1% relative to control. On the other hand, higher concentration of XNT (50 μg/mL) significantly suppressed the growth of 3T3-L1 adipocytes (5.9 ± 0.58%) compared to 3T3-L1 preadipocytes (81.31 ± 0.55%). XNT was found to impede adipogenesis of 3T3-L1 adipocytes in a dose-dependent manner from 3.125 to 12.5 μg/mL, where 12.5 μg/mL significantly suppressed 36.13 ± 2.1% of lipid accumulation. We postulate that inhibition of cholesterol uptake, adipogenesis, preadipocyte and adipocyte number may be utilized as treatment modalities to reduce the prevalence of lipidemia. To conclude, XNT could be a potential hypolipidemic agent to improve cardiovascular health in the future.