Data mining for the identification of metabolic syndrome status

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/ understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS

Determining a new formula for calculating low-density lipoprotein cholesterol: data mining approach

Low-density lipoprotein cholesterol (LDL-C) is a risk factor of coronary heart diseases. The estimation of LDL-C (LDL-Cal) level was performed using Friedewald’s equation for triglyceride (TG) level less than 400 mg/dL. Therefore, the aim of this study is to generate a new formula for LDL-Cal and validate the correlation coefficient between LDL-Cal and LDL-C directly measured (LDL-Direct). A data set of 1786 individuals receiving annual medical check-ups from the Faculty of Medical Technology, Mahidol University, Thailand in 2008 was used in this study. Lipid profiles including total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C) and LDL-C were determined using Roche/Hitachi modular system analyzer. The estimated LDL-C was obtained using Friedewald’s equation and the homogenous enzymatic method. The level of TG was divided into 6 groups (TG<200, <300, <400, <500, <600 and < 1000 mg/dL) for constructing the LDL-Cal formula. The pace regression model was used to construct the candidate formula for the LDL-Cal and determine the correlation coefficient (r) with the LDL-Direct. The candidate LDL-Cal formula was generated for 6 groups of TG levels that displayed well correlation between LDL-Cal and LDL-Direct. Interestingly, The TG level was less than 1000 mg/dL, the regression model was able to generate the equation as shown as strong r of 0.9769 with LDL-Direct. Furthermore, external data set (n = 666) with TG measurement (36-1480 mg/dL) was used to validate new formula which displayed high r of 0.971 between LDL-Cal and LDL-direct. This study explored a new formula for LDL-Cal which exhibited higher r of 0.9769 and far beyond the limitation of TG more than 1000 mg/dL and potential used for estimating LDL-C in routine clinical laboratories

Bioactive 4-hydroxycinnamide and bioactivities of Polyalthia cerasoides

Constituents from Polyalthia cerasoides, stem bark methanol extract, were previously documented. This study reports the first isolation of bioactive N-(4-hydroxy-β-phenethyl)-4-hydroxycinnamide (1) from ethyl acetate extract of the plant species including stigmasterol and a mixture of triterpenes from hexane and dichloromethane extracts. Trace essential elements were found in the hexane extract in ppm level. The plant extracts were evaluated for their antimicrobial and antioxidative activities. The dichloromethane extract displayed the highest activity against Corynebacterium diphtheriae NCTC 10356 with MIC of 32 μg/mL, as well as, the highest SOD activity with an IC50 of 4.51 μg/mL

Aromatase inhibitory activity of 1,4-naphthoquinone derivatives and QSAR study

A series of 2-amino(chloro)-3-chloro-1,4-naphthoquinone derivatives (1-11) were investigated for their aromatase inhibitory activities. 1,4-Naphthoquinones 1 and 4 were found to be the most potent compounds affording IC50 values 5.2 times lower than the reference drug, ketoconazole. A quantitative structure-activity relationship (QSAR) model provided good predictive performance (R2 CV = 0.9783 and RMSECV = 0.0748) and indicated mass (Mor04m and H8m), electronegativity (Mor08e), van der Waals volume (G1v) and structural information content index (SIC2) descriptors as key descriptors governing the activity. To investigate the effects of structural modifications on aromatase inhibitory activity, the model was employed to predict the activities of an additional set of 39 structurally modified compounds constructed in silico. The prediction suggested that the 2,3-disubstitution of 1,4-naphthoquinone ring with halogen atoms (i.e., Br, I and F) is the most effective modification for potent activity (1a, 1b and 1c). Importantly, compound 1b was predicted to be more potent than its parent compound 1 (11.90-fold) and the reference drug, letrozole (1.03-fold). The study suggests the 1,4-naphthoquinone derivatives as promising compounds to be further developed as a novel class of aromatase inhibitors

Identification of the ubiquitin–proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network

The major mechanism of proteolysis in the cytosol and nucleus is the ubiquitin–proteasome pathway (UPP). The highly controlled UPP has an effect on a wide range of cellular processes and substrates, and flaws in the system can lead to the pathogenesis of a number of serious human diseases. Knowledge about UPPs provide useful hints to understand the cellular process and drug discovery. The exponential growth in next-generation sequencing wet lab approaches have accelerated the accumulation of unannotated data in online databases, making the UPP characterization/analysis task more challenging. Thus, computational methods are used as an alternative for fast and accurate identification of UPPs. Aiming this, we develop a novel deep learning-based predictor named “2DCNN-UPP” for identifying UPPs with low error rate. In the proposed method, we used proposed algorithm with a two-dimensional convolutional neural network with dipeptide deviation features. To avoid the over fitting problem, genetic algorithm is employed to select the optimal features. Finally, the optimized attribute set are fed as input to the 2D-CNN learning engine for building the model. Empirical evidence or outcomes demonstrates that the proposed predictor achieved an overall accuracy and AUC (ROC) value using 10-fold cross validation test. Superior performance compared to other state-of-the art methods for discrimination the relations UPPs classification. Both on and independent test respectively was trained on 10-fold cross validation method and then evaluated through independent test. In the case where experimentally validated ubiquitination sites emerged, we must devise a proteomics-based predictor of ubiquitination. Meanwhile, we also evaluated the generalization power of our trained modal via independent test, and obtained remarkable performance in term of 0.862 accuracy, 0.921 sensitivity, 0.803 specificity 0.803, and 0.730 Matthews correlation coefficient (MCC) respectively. Four approaches were used in the sequences, and the physical properties were calculated combined. When used a 10-fold cross-validation, 2D-CNN-UPP obtained an AUC (ROC) value of 0.862 predicted score. We analyzed the relationship between UPP protein and non-UPP protein predicted score. Last but not least, this research could effectively analyze the large scale relationship between UPP proteins and non-UPP proteins in particular and other protein problems in general and our research work might improve computational biological research. Therefore, we could utilize the latest features in our model framework and Dipeptide Deviation from Expected Mean (DDE) -based protein structure features for the prediction of protein structure, functions, and different molecules, such as DNA and RNA

Elucidating the Structure-Activity Relationships of the Vasorelaxation and Antioxidation Properties of Thionicotinic Acid Derivatives

Nicotinic acid, known as vitamin B3, is an effective lipid lowering drug and intense cutaneous vasodilator. This study reports the effect of 2-(1-adamantylthio)nicotinic acid (6) and its amide 7 and nitrile analog 8 on phenylephrine-induced contraction of rat thoracic aorta as well as antioxidative activity. It was found that the tested thionicotinic acid analogs 6-8 exerted maximal vasorelaxation in a dose-dependent manner, but their effects were less than acetylcholine (ACh)-induced nitric oxide (NO) vasorelaxation. The vasorelaxations were reduced, apparently, in both NG-nitro-L-arginine methyl ester (L-NAME) and indomethacin (INDO). Synergistic effects were observed in the presence of L-NAME plus INDO, leading to loss of vasorelaxation of both the ACh and the tested nicotinic acids. Complete loss of the vasorelaxation was noted under removal of endothelial cells. This infers that the vasorelaxations are mediated partially by endothelium-induced NO and prostacyclin. The thionicotinic acid analogs all exhibited antioxidant properties in both 2,2-diphenyl-1-picrylhydrazyl (DPPH) and superoxide dismutase (SOD) assays. Significantly, the thionicotinic acid 6 is the most potent vasorelaxant with ED50 of 21.3 nM and is the most potent antioxidant (as discerned from DPPH assay). Molecular modeling was also used to provide mechanistic insights into the vasorelaxant and antioxidative activities. The findings reveal that the thionicotinic acid analogs are a novel class of vasorelaxant and antioxidant compounds which have potential to be further developed as promising therapeutics

ANTIMICROBIAL, ANTIOXIDANT AND ANTICANCER ACTIVITIES OF STRYCHNOS LUCIDA R. BR.

Backgroud: Strychnos lucida R. Br. (Loganiaceae), a well-known indigenous medicine in Timor Leste, has been used for the treatment of ailments such as malaria, diarrhoea, fever, hypertension, cancer, diabetes mellitus and skin infections. Its pharmacological activity has never been reported. The aim of this study was to determine the biological activities of S. lucida, including antimicrobial, antioxidant and anticancer activities. Materials and methods: The stem, stem bark, twig and leaves of S. lucida were extracted by non-polar (hexane) and polar (ethyl acetate and methanol) solvents. Antimicrobial activity of the plant extracts against 29 microorganisms was evaluated using the agar dilution method and antioxidant properties were determined using DPPH and SOD assays. Anticancer activity was investigated against HepG2, HuCCA-1, A549 and MOLT-3 cell lines using the MTT and XTT assays. Results: It was found that the hexane and ethyl acetate extracts of S. lucida selectively inhibited the growth of Gram positive bacteria (Bacillus subtilis ATCC 6633, Bacillus cereus and Streptococcus pyogenes) with MICs range of 32-128 g/mL. Antioxidant activities, radical and superoxide scavenging properties, were observed for ethyl acetate and methanol extracts of S. lucida. Particularly, the ethyl acetate extracts selectively showed inhibitory activity against MOLT-3 cells. Notably, the plant extracts showed the relationship between antimicrobial, antioxidant and anticancer activities. Conclusion: The findings support the medicinal usage of S. lucida

Biochemical and Cellular Investigation of Vitreoscilla Hemoglobin (VHb) Variants Possessing Efficient Peroxidase Activity

Peroxidase-like activity of Vitreoscilla hemoglobin (VHb) has been recently disclosed. To maximize such activity, two catalytically conserved residues (histidine and arginine) found in the distal pocket of peroxidases have successfully been introduced into that of the VHb. A 15-fold increase in catalytic constant (k(cat)) was obtained in P54R variant, which was presumably attributable to the lower rigidity and higher hydrophilicity of the distal cavity arising from substitution of proline to arginine. None of the modifications altered the affinity towards either H2O2 or ABTS substrate. Spectroscopic studies revealed that VHb variants harboring the T29H mutation apparently demonstrated a spectral shift in both ferric and ferrous forms (406-408 to 411 nm, and 432 to 424-425 nm, respectively). All VHb proteins in the ferrous state had a lambda(soret) peak at 419 nm following the carbon monoxide (CO) binding. Expression of the P54R mutant mediated the downregulation of iron superoxide dismutase (FeSOD) as identified by two-dimensional gel electrophoresis (2-DE) and peptide mass fingerprinting (PMF). According to the high peroxidase activity of P54R, it could effectively eliminate autoxidation-derived H2O2, which is a cause of heme degradation and iron release. This decreased the iron availability and consequently reduced the formation of the Fe2+-ferric uptake regulator protein (Fe2+-Fur), an inducer of FeSOD expression

Synthesis of N-Substituted 5-Iodouracils as Antimicrobial and Anticancer Agents

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Predicting Metabolic Syndrome Using the Random Forest Method

Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases