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Interindividual variation is important in the response to metformin as the first-line therapy for type-2 diabetes mellitus (T2DM). Considering that OCT1 and MATE1 transporters determine the metformin pharmacokinetics, this study aimed to investigate the influence of SLC22A1 and SLC47A1 variants on the steady-state pharmacokinetics of metformin and the glycemic response. This research used the prospective-cohort study design for 81 patients with T2DM who received 500 mg metformin twice a day from six primary healthcare centers. SLC22A1 rs628031 A>G (Met408Val) and Met420del genetic variants in OCT1 as well as SLC47A1 rs2289669 G>A genetic variant in MATE1 were examined through the PCR-RFLP method. The bioanalysis of plasma metformin was performed in the validated reversed-phase HPLC-UV detector. The metformin steady-state concentration was measured for the trough concentration (Cssmin) and peak concentration (Cssmax). The pharmacodynamic parameters of metformin use were the fasting blood glucose (FBG) and glycated albumin (GA). Only SLC22A1 Met420del alongside estimated-glomerular filtration rate (eGFR) affected both Cssmax and Cssmin with an extremely weak correlation. Meanwhile, SLC47A1 rs2289669 and FBG were correlated. This study also found that there was no correlation between the three SNPs studied and GA, so only eGFR and Cssmax influenced GA. The average Cssmax in patients with the G allele of SLC22A1 Met408Val, reaching 1.35-fold higher than those with the A allele, requires further studies with regard to metformin safe dose in order to avoid exceeding the recommended therapeutic range.

COVID-19 pandemic defined a worldwide health crisis into a humanitarian crisis. Amid this global emergency, human civilization is under enormous strain since no proper therapeutic method is discovered yet. A wave of research effort has been put towards the invention of therapeutics and vaccines against COVID-19. Contrarily, the spread of this fatal virus has already infected millions of people and claimed many lives all over the world. Computational biology can attempt to understand the protein-protein interactions between the viral protein and host protein. Therefore potential viral-host protein interactions can be identified which is known as crucial information towards the discovery of drugs. In this paper, we have presented an approach for predicting novel interactions from maximal biclusters. Additionally, the predicted interactions are verified from biological perspectives. For this, we conduct a study on the gene ontology and KEGG pathway in relation to the newly predicted interactions.

Bioinformatics is a branch in Statistics which is still unpopular among statistics students in Indonesia. Bioinformatics research used microarray technology, because data is available through to microarray experiment on tissue sample at hand. Microarray technology has been widely used to provide data for bioinformatics research, since it was first introduced in late 1990, particularly in life sciences and biotechnology research. The emergence and development of the Covid-19 disease further reinforces the need to understand bioinformatics and its technology. There are two of the most advance platforms in microarray technology, namely, are the Affymetrix GeneChip and Illumina BeadArray.  This paper aims to give an overview about microarray technology on the two platforms and the advantage of using them on bioinformatics research.

The RMA, since its introduction in [15][16][17], has gained popularity among bioinformaticians. It has evolved from the exponential-normal convolution to the gamma-normal convolution, from single to two channels and from the Affymetrix to the Illumina platform.The Illumina design has provided two probe types: the regular and the control probes. This design is very suitable for studying the probability distribution of both and one can apply the convolution model to compute the true intensity estimator. The availability of benchmarking data set at Illumina platform, the Illumina spike-in, helps researchers to evaluate their proposed method for Illumina BeadArrays.In this paper, we study the existing convolution models for background correction of Illumina BeadArrays in the literature and give a new estimator for the true intensity, where the intensity value is exponentially or gamma distributed and the noise has lognormal distribution. We compare the performance of the models on the Illumina spike-in data set, based on various criteria, for example, root and mean square error, L 1 error, Kullback-Leibler coefficient, and some adapted criteria from Affycomp [5]. We then provide a simulation study to measure the consistency of the error of background correction and the parametrization. We also study the performance of all models on the FFPE data set.Our study shows that our GLN model (with the method of moments for parameter estimation) is the optimal one for the benchmarking data set with benchmarking criteria, while the gamma-normal model has the best performance for the benchmarking data set with simulation criteria. At the public data set of FFPE, the gamma-normal and the exponential-gamma models with MLE cannot be used and our proposed models ELN and GLN have the best performance, showing a moderate error in background correction and in the parametrization.ii