210 |
Nurhayati R.W., Cahyo R.D., Pratama G., Anggraini D., Mubarok W., Kobayashi M., Antarianto R.D. |
55748436600;57212460506;57195959221;57221606578;57208440063;20835016200;57190862806; |
Alginate-chitosan microencapsulated cells for improving cd34+ progenitor maintenance and expansion |
2021 |
Applied Sciences (Switzerland) |
11 |
17 |
7887 |
|
|
|
|
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114106674&doi=10.3390%2fapp11177887&partnerID=40&md5=8aa18a5d52c69e6eb72dcb38252f4489 |
Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Jl. Prof. Soemantri Brojonegoro, Kampus UI, Depok, 16424, Indonesia; Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia; Department of Biotechnology, Faculty of Engineering, 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan; Department of Obstetric and Gynecology, Faculty of Medicine, Universitas Indonesia—Dr. Cipto Mangunkusumo General Hospital, Jl. Diponegoro No. 71, Central Jakarta, 10430, Indonesia; Integrated Service Unit of Stem Cell Medical Technology (IPT TK Sel Punca), Dr. Cipto Mangunkusumo General Hospital (RSCM), Jl. Diponegoro No. 71, Salemba, Central Jakarta, 10430, Indonesia; Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, 630-0192, Japan; Division of Chemical Engineering, Department of Materials Engineering Science, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan; Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, 630-0192, Japan; Department of Histology, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia |
Nurhayati, R.W., Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Jl. Prof. Soemantri Brojonegoro, Kampus UI, Depok, 16424, Indonesia, Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia; Cahyo, R.D., Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia, Department of Biotechnology, Faculty of Engineering, 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan; Pratama, G., Department of Obstetric and Gynecology, Faculty of Medicine, Universitas Indonesia—Dr. Cipto Mangunkusumo General Hospital, Jl. Diponegoro No. 71, Central Jakarta, 10430, Indonesia, Integrated Service Unit of Stem Cell Medical Technology (IPT TK Sel Punca), Dr. Cipto Mangunkusumo General Hospital (RSCM), Jl. Diponegoro No. 71, Salemba, Central Jakarta, 10430, Indonesia; Anggraini, D., Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia, Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, 630-0192, Japan; Mubarok, W., Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia, Division of Chemical Engineering, Department of Materials Engineering Science, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan; Kobayashi, M., Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, 630-0192, Japan; Antarianto, R.D., Stem Cells & Tissue Engineering Research Cluster, Indonesian Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia, Department of Histology, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6, Central Jakarta, 10430, Indonesia |
Protocols for isolation, characterization, and transplantation of hematopoietic stem cells (HSCs) have been well established. However, difficulty in finding human leucocyte antigens (HLA)-matched donors and scarcity of HSCs are still the major obstacles of allogeneic transplanta-tion. In this study, we developed a double-layered microcapsule to deliver paracrine factors from non-matched or low-matched HSCs to other cells. The umbilical cord blood-derived hematopoietic progenitor cells, identified as CD34+ cells, were entrapped in alginate polymer and further protected by chitosan coating. The microcapsules showed no toxicity for surrounding CD34+ cells. When CD34+ cells-loaded microcapsules were co-cultured with bare CD34+ cells that have been collected from unrelated donors, the microcapsules affected surrounding cells and increased the percentage of CD34+ cell population. This study is the first to report the potency of alginate-chitosan microcap-sules containing non-HLA-matched cells for improving proliferation and progenitor maintenance of CD34+ cells. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
Alginate; CD34; Chitosan; Hematopoietic; Megakaryocyte; Microencapsulation; Progenitor; Proliferation; Stem cells |
|
MDPI |
20763417 |
|
|
Article |
Q2 |
435 |
11324 |
|
|
585 |
Silitonga P., Bustamam A., Muradi H., Mangunwardoyo W., Dewi B.E. |
57219406661;36815737800;57188977950;24544449900;24076058600; |
Comparison of dengue predictive models developed using artificial neural network and discriminant analysis with small dataset |
2021 |
Applied Sciences (Switzerland) |
11 |
3 |
943 |
1 |
16 |
|
3 |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099695109&doi=10.3390%2fapp11030943&partnerID=40&md5=3a9bba4cf9ba9dacfef04d7349c81f4b |
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Department of Mathematics, Faculty of Science and Information Technology, Institut Sains dan Teknologi Nasional, Jl.Moh Kahfi II Srengseng Sawah Jagakarsa, Jakarta Selatan, 12640, Indonesia; Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Department of Microbiology, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya no. 5, Kota Jakarta Pusat, Daerah Khusus Ibu Kota Jakarta, 10430, Indonesia |
Silitonga, P., Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Bustamam, A., Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Muradi, H., Department of Mathematics, Faculty of Science and Information Technology, Institut Sains dan Teknologi Nasional, Jl.Moh Kahfi II Srengseng Sawah Jagakarsa, Jakarta Selatan, 12640, Indonesia; Mangunwardoyo, W., Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Dewi, B.E., Department of Microbiology, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya no. 5, Kota Jakarta Pusat, Daerah Khusus Ibu Kota Jakarta, 10430, Indonesia |
In Indonesia, dengue has become one of the hyperendemic diseases. Dengue consists of three clinical phases—febrile phase, critical phase, and recovery phase. Many patients have died in the critical phase due to the lack of proper and timely treatment. Therefore, we developed models that can predict the severity level of dengue based on the laboratory test results of the corresponding patients using Artificial Neural Network (ANN) and Discriminant Analysis (DA). In developing the models, we used a very small dataset. It is shown that ANN models developed using logistic and hyperbolic tangent activation function with 70% training data yielded the highest accuracy (90.91%), sensitivity (91.11%), and specificity (95.51%). This is the proposed model in this research. The proposed model will be able to help physicians in predicting the severity level of dengue patients before entering the critical phase. Furthermore, it will ease physicians in treating dengue patients early, so fatal cases or deaths can be avoided. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
Artificial neural network; Dengue; Discriminant analysis |
|
MDPI AG |
20763417 |
|
|
Article |
Q2 |
435 |
11324 |
|
|