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High-Throughput Analytics in the Function of Personalized Medicine

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Personalized Medicine in Healthcare Systems

Part of the book series: Europeanization and Globalization ((EAG,volume 5))

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Abstract

Rapid and highly reliable analysis of patient sample is the first premise and simultaneously the first step in the direction of right diagnosis and personalized treatment. However, this fact is frequently neglected. Despite this situation, almost unnoticed activities in this field started 55 years ago with the concept of a biosensor for fast monitoring of blood glucose and diabetes control, and blood glucose determination is still the driving force for optimization of these kinds of high-throughput analytical devices. The development of devices for high-throughput analytical methods for fast and accurate analysis started with the separation of molecules based on different size, charge and hydrophobicity and with the introduction of chromatographic and electrophoretic methods into clinical laboratories. The next step was their optimization towards the strategy for a fast analysis and miniaturization. One of the main tools for such kinds of analyses are different optimized supports and instruments for signal amplification that are used in such devices. The discovery and use of miniaturized chromatographic and electrophoretic systems based on monolithic supports was briefly discussed here. Their development in the direction of further miniaturization towards biosensors and nanobiosensors was also presented.

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Notes

  1. 1.

    ESF Forward Look (2012).

  2. 2.

    Bošnjak et al. (2008).

  3. 3.

    Pavelić et al. (2016).

  4. 4.

    The Royal Swedish Academy of Sciences (2002).

  5. 5.

    Josić and Andjelković (2016).

  6. 6.

    Nobel Lectures Chemistry (1964).

  7. 7.

    Axén and Porath (1966).

  8. 8.

    Johansson et al. (1975).

  9. 9.

    Unger (1979) and Chang et al. (1976).

  10. 10.

    Josić et al. (1989).

  11. 11.

    Tennikova et al. (1990).

  12. 12.

    Hjertén et al. (1989).

  13. 13.

    Josić et al. (1992).

  14. 14.

    Josić et al. (1992) and Svec and Huber (2006).

  15. 15.

    Svec and Huber (2006) and Josić and Buchacher (2001).

  16. 16.

    Štrancar et al. (1996).

  17. 17.

    Podgornik et al. (2000).

  18. 18.

    Wu et al. (2001) and Švec and Lv (2015).

  19. 19.

    Ručević et al. (2006) and Opal et al. (2007).

  20. 20.

    Josić and Buchacher (2001).

  21. 21.

    Opal et al. (2007) and Chaaban et al. (2015).

  22. 22.

    Breen et al. (2012).

  23. 23.

    Pučić et al. (2011).

  24. 24.

    Brgles et al. (2011).

  25. 25.

    Burke et al. (1988).

  26. 26.

    Veeraragavan et al. (1991) and Manseth et al. (2004).

  27. 27.

    Husband et al. (2000).

  28. 28.

    Veeraragavan et al. (1991).

  29. 29.

    Manseth et al. (2004).

  30. 30.

    Brgles et al. (2011) and Šrajer Gajdošik et al. (2012).

  31. 31.

    Ahrends et al. (2010), Kotasinska et al. (2012) and Trusch et al. (2012).

  32. 32.

    Shu et al. (2012).

  33. 33.

    Šrajer Gajdošik et al. (2014).

  34. 34.

    Burnouf (2007).

  35. 35.

    Burnouf (2007) and Josić et al. (2000).

  36. 36.

    Cho (2015).

  37. 37.

    Schiel et al. (2011).

  38. 38.

    Černigoj et al. (2015).

  39. 39.

    Brown et al. (2010).

  40. 40.

    Brgles et al. (2011).

  41. 41.

    Brgles et al. (2011), Šrajer Gajdošik et al. (2012), Ahrends et al. (2010), Kotasinska et al. (2012), Trusch et al. (2012) and Josić and Clifton (2007).

  42. 42.

    Josić et al. (1992) and Abou-Rebyeh et al. (1991).

  43. 43.

    Štrancar et al. (1996).

  44. 44.

    Svec and Fréchet (1996).

  45. 45.

    Brgles et al. (2011), Ahrends et al. (2010) and Šrajer Gajdošik et al. (2014).

  46. 46.

    Ahrends et al. (2010).

  47. 47.

    Josić and Clifton (2007).

  48. 48.

    Trbojević-Akmačić et al. (2016).

  49. 49.

    Naldi et al. (2017).

  50. 50.

    Breen et al. (2012), Pučić et al. (2011), Naldi et al. (2017) and Breen et al. (2016).

  51. 51.

    Peterson et al. (2002).

  52. 52.

    Makaram et al. (2014).

  53. 53.

    Nayak et al. (2017).

  54. 54.

    Wang (2008).

  55. 55.

    Nayak et al. (2017), Wang (2008) and Witkowska Nery et al. (2016).

  56. 56.

    Zang et al. (2004).

  57. 57.

    Chen et al. (2013).

  58. 58.

    Sheng et al. (2012).

  59. 59.

    Odom et al. (2013).

  60. 60.

    Lusczek et al. (2017).

  61. 61.

    Champagne et al. (2016).

  62. 62.

    Cavally et al. (2017).

  63. 63.

    Vander Heiden et al. (2009).

  64. 64.

    Nayak et al. (2017), Du and Dong (2017) and Liu et al. (2010).

  65. 65.

    Wang (2008) and Witkowska Nery et al. (2016).

  66. 66.

    Clark et al. (2016).

  67. 67.

    Alves et al. (2016).

  68. 68.

    Zamperi et al. (2017) and Švec and Lv (2015).

  69. 69.

    Fu et al. (2017), Du and Dong (2017) and Ashley et al. (2017).

  70. 70.

    Wen et al. (2017).

  71. 71.

    Yang et al. (2017).

  72. 72.

    Du and Dong (2017).

  73. 73.

    Ashley et al. (2017).

  74. 74.

    Ručević et al. (2006).

  75. 75.

    Švec and Lv (2015) and Malik et al. (2013).

  76. 76.

    Huang et al. (2016a).

  77. 77.

    Fu et al. (2017).

  78. 78.

    Nayak et al. (2017), Wang (2008) and Witkowska Nery et al. (2016).

  79. 79.

    Wang (2008).

  80. 80.

    Witkowska Nery et al. (2016).

  81. 81.

    Lupinek et al. (2014).

  82. 82.

    Lupinek et al. (2014).

  83. 83.

    Huang et al. (2016a).

  84. 84.

    Huang et al. (2016b).

  85. 85.

    Zhong et al. (2016).

  86. 86.

    Fu et al. (2017).

  87. 87.

    Du and Dong (2017).

  88. 88.

    Wen et al. (2017).

  89. 89.

    Jianrong et al. (2004).

  90. 90.

    MacKenzie et al. (2009) and Zeng et al. (2011).

  91. 91.

    Malik et al. (2013) and Jianrong et al. (2004).

  92. 92.

    MacKenzie et al. (2009) and Zeng et al. (2011).

  93. 93.

    Tiwari and Turner (2014).

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Acknowledgement

This research was supported by the European Union (Marie Currie Programme, HTP-Glycomet “Methods for high-throughput glycoproteomic analysis”) and University of Rijeka (Project No. 13.11.1.3.03).

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Authors and Affiliations

  1. Department of Biotechnology, Centre for High-Throughput Technologies, University of Rijeka, Rijeka, Croatia

    Djuro Josić, Tamara Martinović & Krešimir Pavelić

  2. Department of Medicine, Warren Alpert Medical School, Brown University, Providence, RI, USA

    Djuro Josić

  3. BIA Separations d.o.o., Ajdovščina, Slovenia

    Urh Černigoj & Jana Vidič

  4. Juraj Dobrila University of Pula, Faculty of Medicine, Pula, Croatia

    Djuro Josić & Krešimir Pavelić

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  1. Djuro Josić
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  2. Tamara Martinović
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Correspondence to Djuro Josić .

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Editors and Affiliations

  1. Faculty of Law, University of Rijeka, Rijeka, Croatia

    Nada Bodiroga-Vukobrat

  2. Croatian Academy of Sciences and Arts, Rijeka, Croatia

    Daniel Rukavina

  3. Juraj Dobrila University of Pula, Faculty of Medicine, Pula, Croatia

    Krešimir Pavelić

  4. Department of Biotechnology, Centre for High-Throughput Technologies, University of Rijeka, Rijeka, Croatia

    Krešimir Pavelić

  5. University of Applied Sciences, Ludwigsburg, Germany

    Gerald G. Sander

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Josić, D., Martinović, T., Černigoj, U., Vidič, J., Pavelić, K. (2019). High-Throughput Analytics in the Function of Personalized Medicine. In: Bodiroga-Vukobrat, N., Rukavina, D., Pavelić, K., Sander, G.G. (eds) Personalized Medicine in Healthcare Systems. Europeanization and Globalization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-16465-2_6

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