Dengue fever is a significant public health concern throughout the world, causing an estimated 500,000 hospitalizations and 20,000 deaths each year, despite the lack of effective therapies. The DENV-2 RdRp has been identified as a potential target for the development of new and effective dengue therapies. This research’s primary objective was to discover an anti-DENV inhibitor using in silico ligand- and structure-based approaches. To begin, a ligand-based pharmacophore model was developed, and 130 distinct natural products (NPs) were screened. Docking of the pharmacophore-matched compounds were performed to the active site of DENV-2 RdRp protease . Eleven compounds were identified as potential DENV-2 RdRp inhibitors based on docking energy and binding interactions. ADMET and drug-likeness were done to predict their pharmacologic, pharmacokinetic, and drug-likeproperties . Compounds ranked highest in terms of pharmacokinetics and drug-like appearances were then subjected to additional toxicity testing to determine the leading compound. Additionally, MD simulation of the lead compound was performed to confirm the docked complex’s stability and the binding site determined by docking. As a result, the lead compound (compound-108) demonstrated an excellent match to the pharmacophore, a strong binding contact and affinity for the RdRp enzyme, favourable pharmacokinetics, and drug-like characteristics. In summary, the lead compound identified in this study could be a possible DENV-2 RdRp inhibitor that may be further studied on in vitro and in vivo models to develop as a drug candidate. Communicated by Ramaswamy H. Sarma

The quality grading of agarwood oil is vital issue among producers. This paper presents the implementation of Radial Basis Function (RBF) tuned parameter in Support Vector Machine (SVM) for agarwood oil quality classification. The work involved of GC-MS based data of agarwood oil, were fed into SVM programming as input and the quality of oil as output. The high and low qualities of agarwood oil were pre-processed using MATLAB software version 2015a which involves of normalization, randomization and data division into training datasets (80%) and testing datasets (20%). By using `svmclassify' script function in MATLAB version R2015a, the data is trained and tested as well as their performances were measured. Several criteria were chosen; specification, precision, accuracy, sensitivity, error rates, error test and mean square error in grading the agarwood oil. It can be concluded that the SVM model with RBF tuning was a success and passed all the criteria in classifying the agarwood oil qualities. The significant in this research is the reliable of the SVM handle with RBF as kernel parameter and its finding that contributed to the agarwood oil research area especially in grading system.

Medicinal and aromatic plants (MAPs) are widely valued for their aromas, tastes, and treatments for various human illnesses. The chemical constituents and toxicity content of three aromatic plant species, Syzygium polyanthum Wight (Walp.), Monocarpia marginalis (Scheff.) J. Sinclair, and Chromolaena odorata (L.) R.M. King & H. Rob, were determined, via gas chromatography (GC) with mass spectrometry (MS), and flame ionization detector (FID). Altogether, 116 compounds were identified in the essential oils and hexane and methanol extracts. Toxicity evaluations were carried out on human peripheral blood mononuclear cells (PBMCs). Three plant samples were found toxic to human PBMCs. The essential oils of M. marginalis and C. odorata, and the hexane extract of C. odorata yielded IC50 and LD50 values of 76 mg/mL and 6,913 mg/kg, 14 mg/mL and 3,684 mg/kg, and 2.45 mg/mL and 1,927 mg/kg, respectively. Based on the LD50 values, M. marginalis and C. odorata can be classified as slightly and moderately hazardous, respectively. A detailed toxicity evaluation via comet assay showed that M. marginalis and C. odorata induced significant DNA damage (p < 0.05). As for S. polyanthum, the species did not give any cytotoxic or genotoxic evidences.

This paper gives precise summary on the application of stepwise regression model based upon the pre-process analysis of boxplot for four chemical compounds into four different qualities of agarwood oil. In the global market, agarwood oil is acknowledged as a pricey and valuable nature product owing to its benefits. Unfortunately, there is no standard grading method for agarwood oil grade classification. Intelligent model in grading the quality of agarwood oil is crucial as one of the efforts to classify the agarwood quality. The main model chosen in this study is stepwise regression by concerned specific parameter which is the value of correlation coefficient, R2. To achieve this goal, four out of eleven significant compounds of agarwood oil that consist of 660 data samples from low, medium low, medium high and high quality are representing the input. The independent variables are X1, X2, X3 and X4 which refer to the ɤ-Eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin compounds, respectively. MATLAB software version r2015a has been chosen as the simulation platform for this research work. The result showed that the stepwise regression model has a correlation coefficient of 0.756 and p-value less than 0.05 significance level which successfully passed the performance criteria toward regression value.

The paper examines the characterization of Aquilaria essential oils from different species, namely Aquilaria malaccensis, Aquilaria beccariana, Aquilaria crassna, and Aquilaria subintegra, renowned for agarwood production in Malaysia. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID) were employed for extracting essential oil data, facilitating compound identification. Subsequently, a preliminary analysis focused on classifying significant chemical compounds in the samples. The study then utilized boxplot pre-processing for visualizing and interpreting data distribution. The statistical analyses were performed using MATLAB software version R2021b, considering two key parameters which are the peak area (%) of significant chemical compounds and the classification of Aquilaria species (A. beccariana, A. malaccensis, A. crassna, and A. subintegra) based on their chemical composition. The results, presented through boxplot analyses, demonstrated a clear representation of the parameters and their distribution in the data. This method not only confirmed the potential of boxplot analysis in statistical evaluation of significant compounds in Aquilaria essential oil but also suggested its applicability for further classification work.