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Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy

Open Access
|Dec 2019

References

  1. Rajabzadeh N, Fathi E, Farahzadi R. Stem cell-based regenerative medicine. Stem Cell Investig. 2019 Jul;6(0):19–9. https://doi.org/10.21037/sci.2019.06.04
  2. Nguyen PK, Neofytou E, Rhee J-W, Wu JC. Potential Strategies to Address the Major Clinical Barriers Facing Stem Cell Regenerative Therapy for Cardiovascular Disease. JAMA Cardiol. 2016 Nov 1;1(8):953–16. https://doi.org/10.1001/jamacardio.2016.2750
  3. Ronaghi M, Erceg S, Moreno-Manzano V, Stojkovic M. Challenges of Stem Cell Therapy for Spinal Cord Injury: Human Embryonic Stem Cells, Endogenous Neural Stem Cells or Induced Pluripotent Stem Cells? Stem Cells. 2009;28(1):93–99. https://doi.org/10.1002/stem.253
  4. Gamal W, Wu H, Underwood I, Jia J, Smith S, Bagnaninchi PO. Impedance-based cellular assays for regenerative medicine. Phil Trans R Soc B. 2018 May 21;373(1750):20170226. https://doi.org/10.1098/rstb.2017.0226
  5. Lee AS, Tang C, Cao F, Xie X, van der Bogt K, Hwang A, et al. Effects of cell number on teratoma formation by human embryonic stem cells. Cell Cycle. 2014 Oct 28;8(16):2608–12. https://doi.org/10.4161/cc.8.16.9353
  6. Hussain R, Zubair H, Pursell S, Shahab M. Neurodegenerative Diseases: Regenerative Mechanisms and Novel Therapeutic Approaches. Brain Sciences. 2018 Sep;8(9):177–37. https://doi.org/10.3390/brainsci8090177
  7. Gage FH, Temple S. Neural Stem Cells: Generating and Regenerating the Brain. Neuron. Elsevier Inc; 2013 Oct 30;80(3):588–601. https://doi.org/10.1016/j.neuron.2013.10.037
  8. Xiao C, Luong JHT. On-Line Monitoring of Cell Growth and Cytotoxicity Using Electric Cell-Substrate Impedance Sensing (ECIS). Biotechnol Prog. American Chemical Society (ACS); 2003 Jun 6;19(3):1000–5. https://doi.org/10.1021/bp025733x
  9. Krinke D, Jahnke H-G, Mack TGA, Hirche A, Striggow F, Robitzki AA. A novel organotypic tauopathy model on a new microcavity chip for bioelectronic label-free and real time monitoring. Biosensors and Bioelectronics. Elsevier B.V; 2010 Sep 15;26(1):162–8. https://doi.org/10.1016/j.bios.2010.06.002
  10. Hug TS. Biophysical Methods for Monitoring Cell-Substrate Interactions in Drug Discovery. ASSAY and Drug Development Technologies. 2003 Jun;1(3):479–88. https://doi.org/10.1089/154065803322163795
  11. Jahnke H-G, Braesigk A, Mack TGA, Pönick S, Striggow F, Robitzki AA. Impedance spectroscopy based measurement system for quantitative and label-free real-time monitoring of tauopathy in hippocampal slice cultures. Biosensors and Bioelectronics. Elsevier B.V; 2012 Feb 15;32(1):250–8. https://doi.org/10.1016/j.bios.2011.12.026
  12. Haas S, Jahnke H-G, Glass M, Azendorf R, Schmidt S, Robitzki AA. Real-time monitoring of relaxation and contractility of smooth muscle cells on a novel biohybrid chip. Lab Chip. 2010;10(21):2965–7. https://doi.org/10.1039/c0lc00008f
  13. Seidel D, Obendorf J, Englich B, Jahnke H-G, Semkova V, Haupt S, et al. Impedimetric real-time monitoring of neural pluripotent stem cell differentiation process on microelectrode arrays. Biosensors and Bioelectronics. Elsevier; 2016 Dec 15;86(C):277–86. https://doi.org/10.1016/j.bios.2016.06.056
  14. Xu Y, Xie X, Duan Y, Wang L, Cheng Z, Cheng J. A review of impedance measurements of whole cells. Biosensors and Bioelectronics. 2016 Mar;77:824–36. https://doi.org/10.1016/j.bios.2015.10.027
  15. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. WIREs Data Mining Knowl Discov. 2019 Feb 22;9(4):2672–13. https://doi.org/10.1002/widm.1312
  16. Tronstad C, Strand-Amundsen R. Possibilities in the application of machine learning on bioimpedance time-series. Journal of Electrical Bioimpedance. 2019 Jun 26;10(1):24–33. https://doi.org/10.2478/joeb-2019-0004
  17. Strand-Amundsen RJ, Tronstad C, Reims HM, Reinholt FP, Høgetveit JO, Tønnessen TI. Machine learning for intraoperative prediction of viability in ischemic small intestine. Physiol Meas. 2018 Oct 1;39(10):105011–24. https://doi.org/10.1088/1361-6579/aae0ea
  18. Ludwig TE, Bergendahl V, Levenstein ME, Yu J, Probasco MD, Thomson JA. Feeder-independent culture of human embryonic stem cells. Nat Methods. Nature Publishing Group; 2006 Jul 21;3(8):637–46. https://doi.org/10.1038/nmeth902
  19. Li W, Sun W, Zhang Y, the WWPO, 2011. Rapid induction and long-term self-renewal of primitive neural precursors from human embryonic stem cells by small molecule inhibitors. National Acad. Sciences. https://doi.org/10.1073/pnas.1014041108
  20. Graves A, Fernández S, Gomez F, Schmidhuber J. Connectionist temporal classification. New York, USA: ACM Press; 2006. pp. 369–76.
  21. Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks. 2005 Jul;18(5-6):602–10. https://doi.org/10.1016/j.neunet.2005.06.042
  22. Burger M, Neubauer A. Analysis of Tikhonov regularization for function approximation by neural networks. Neural Networks. 2003 Jan;16(1):79–90. https://doi.org/10.1016/s0893-6080(02)00167-3
  23. Gal Y, Ghahramani Z. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. NIPS, Barcelona, Spain. 2016.
Language: English
Page range: 124 - 132
Submitted on: Dec 1, 2019
Published on: Dec 31, 2019
Published by: University of Oslo
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2019 André B. Cunha, Jie Hou, Christin Schuelke, published by University of Oslo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.