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Kholis A Audah
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Bioinformatics and Biomedical Research Journal
Published by Future Science
ISSN : -     EISSN : 26203324     DOI : 10.11594/bbrj
Core Subject : Science, Education, Engineering,
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Education Engineering
Bioinformatics and Biomedical Research Journal (BBR) serve the interests of the research-oriented and professional section in the fields of Bioinformatics and Biomedical Research. The current emphasis of the BBR Journal includes (but is not limited to) the following areas: Drugs Discovery Genomics study Proteomics study, structural bioinformatics Pharmacogenomics Epigentics Gene Mutation Polimorfism Biomarker Pharmaceutical Biotechnology Pharmaceutical biosciences and other field related to bioimedical research
Arjuna Subject : Biokimia, Genetika dan Biologi Molekuler - Bioteknologi
Articles 4 Documents
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Search results for , issue "Vol. 3 No. 2 (2020): Volume 3 issue 2" : 4 Documents clear
The Art of Mesenchymal Stem Cells in Liver Fibrosis Management Anurogo, Dito; Amansyah, Farid
Bioinformatics and Biomedical Research Journal Vol. 3 No. 2 (2020): Volume 3 issue 2
Publisher : Future Science

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Abstract

Liver fibrogenesis is chronic tissue damage characterized by an extracellular accumulation of fibrillar matrix associated with continuous degradation and remodelling. This scientific review describes current concepts on the pathophysiology of liver fibrosis, inflammation as a fundamental concept of liver fibrosis, mechanistic concepts of liver fibrosis, the role of mesenchymal stem cells (MSC) in liver injury, the functional effects of MSC secretome, the advantages of secretome therapy, and the latest research developments on MSC. The role of MSCs has been proven in many liver fibrosis studies involving experimental animals. However, it still requires further research for safety and efficacy aspects
The Correlation Between Education Level, Knowledge and Motivation in Volunteer Performance: a Post-Study on Action Research in Developing Educator Volunteers for COVID-19 Based on community empowerment using a tiered and online platform in Indonesia Khuzaiyah, Siti; Priyogo, Nur Izzah; Setianto, Gigih
Bioinformatics and Biomedical Research Journal Vol. 3 No. 2 (2020): Volume 3 issue 2
Publisher : Future Science

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Abstract

Coronavirus infection is spreading globally, including in Indonesia. The coronavirus transmits so quickly; there is panic in the community to avoid transmission. Stakeholders could develop educator volunteers based on community empowerment to increase public knowledge so that they can prevent transmission of the Corona Virus. Aims. This study aimed to determine the correlation between education level, knowledge and motivation in volunteer performance after training on developing educator volunteers of covid-19. Methods. This study was action research with a post-test evaluation approach The population in this study were 250 educator volunteers of covid-19. The sampling technique used a Slovin formula with the total sample was 50 people. The data were analyzed using the Pearson chi-square test and Spearman rank. Results. There was a significant correlation between motivation for volunteer performance (p-value 0.014). Meanwhile, the education and knowledge variables did not have a substantial correlation on volunteer performance with p-value 0.917 and 0.243, respectively. Conclusions. Full support is needed for volunteers so that they are motivated to carry out their roles as educator volunteers of covid-19.
The Role of Phytochelatin Synthase in Phytoremediation Agent: Structural Conservation of Phytochelatin (PC) Synthase to Maintain Its Activity as Heavy Metal Detoxification in Plant Vidayanti, Viky; Permatasari, Galuh Wening
Bioinformatics and Biomedical Research Journal Vol. 3 No. 2 (2020): Volume 3 issue 2
Publisher : Future Science

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Abstract

Phytochelatin (PC) Enzyme has crucial role in heavy metal detoxification and homeostasis in plants. This study aimed to evaluate the genetic variation of PC synthase related to its activity based on structural comparison. We evaluated PC genes and protein sequences from 6 plants namely, Brassica sp., Amaranthus sp., Noccaea sp., Arabidopsis sp., Nicotiana sp., and Pteris sp. All sequences were aligned based on CLUSTALW matrix for DN sequences and MUSCLE algorithm for protein sequences. Data were clustered using MEGA Software for similarity clustering. Selected data were further modeled using SWISSMODEL to evaluate the 3D-structural analysis based on homology modeling. Thus, all protein models were superimposed and evaluated the structure comparison based on RMSD data. The result showed that genetic variation of PC gene is high among species. But it is clustered for the same species has similar sequence. In addition, protein sequences also showed the high diversity among species and it is still clustered based on their genus. RMSD data showed that PC synthase from 6 plant has similar structure and tend to conserved even there is genetic variation or amino acid modification. We concluded that structural of PC gene is more conserved than its sequences. It is important to keep its function among species.
Identification of Significant Proteins in Coronavirus Disease 2019 Protein-Protein Interaction Using Principal Component Analysis and ClusterONE Ananta Kusuma, Wisnu; Farhan Ramadhani , Hilmi; Annisa
Bioinformatics and Biomedical Research Journal Vol. 3 No. 2 (2020): Volume 3 issue 2
Publisher : Future Science

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Abstract

Coronavirus Disease 2019 (COVID-19) will cause disease complications and organ damage due to excessive inflammatory reactions if left untreated. Computational analysis of protein-protein interactions can be carried out in various ways, including topological analysis and clustering of protein-protein interaction networks. Topological analysis can identify significant proteins by measuring the most important nodes with centrality measurements. By using Principal Component Analysis (PCA), the types of centrality measures were extracted into the overall centrality value. The study aimed to found significant proteins in COVID-19 protein-protein interactions using PCA and ClusterONE. This study used 57 proteins associated with COVID-19 to obtain protein networks. All of these proteins are homo sapiens organism. The number of proteins and the number of interactions from 57 proteins were 357 proteins and 1686 interactions. The results of this study consisted of two clusters; the best cluster was the first cluster with a lower p-value but had an average overall centrality value that closed to the second cluster. There are twenty important proteins in that cluster, and all of these proteins are related to COVID-19. These proteins are expected to be used in the process of discovering medicinal compounds in COVID-19.

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