As reported by World Health Organization (WHO), most than half of the word population is potentially at risk to be infected with Dengue virus. This virus is a single strained RNA virus of the flaviviridae family that presents four different serotypes. Secondary infections with different serotypes can induce very dangerous complications, like Dengue haemorrhagic fever and shock syndrome. The tropical and sub-tropical areas are the most affected by this infection. Therefore, there is a strong need of a cheap, portable, fast and sensitive diagnostic test also for economic reasons. In this paper, the development of a Point-of-Care multiparametric system using an organic light emitting diode (OLED)-based biochip for the simultaneous detection of the four different Dengue serotypes, is presented. The system exploits the fluorescence detection of a low density matrix of protein antigens and secondary fluorophore tagged antibodies, excited by an OLED source. A low cost CMOS camera, allowing low light detection, to perform multiparametric quantitative analysis has been used in combination with a specifically developed image processing software. The software pipeline adopted is described as well. Fourty Dengue patient sera, previously characterized by standard diagnostic techniques, have been tested, using 3 μl of sera and an incubation time of 30 minutes. The results of these analysis demonstrate the capability of this diagnostic system to perform early Dengue diagnosis and to recognize the Dengue serotype with very high precision, in particular in convalescent patients.

POINT-OF CARE OLED-BASED MULTIPARAMETRIC BIOCHIP EXPLOITING ADVANCED IMAGE PROCESSING FOR THE DENGUE SEROTYPE RECOGNITION / Patrizia Melpignano; Serena Morigi ; Enrico Daniso ; Elena Loli Piccolomini; Luca Benini. - ELETTRONICO. - (2018), pp. 1-14. (Intervento presentato al convegno CMBBE 2018 tenutosi a Lisbona nel Marzo 2018).

POINT-OF CARE OLED-BASED MULTIPARAMETRIC BIOCHIP EXPLOITING ADVANCED IMAGE PROCESSING FOR THE DENGUE SEROTYPE RECOGNITION

Serena Morigi;Elena Loli Piccolomini;Luca Benini
2018

Abstract

As reported by World Health Organization (WHO), most than half of the word population is potentially at risk to be infected with Dengue virus. This virus is a single strained RNA virus of the flaviviridae family that presents four different serotypes. Secondary infections with different serotypes can induce very dangerous complications, like Dengue haemorrhagic fever and shock syndrome. The tropical and sub-tropical areas are the most affected by this infection. Therefore, there is a strong need of a cheap, portable, fast and sensitive diagnostic test also for economic reasons. In this paper, the development of a Point-of-Care multiparametric system using an organic light emitting diode (OLED)-based biochip for the simultaneous detection of the four different Dengue serotypes, is presented. The system exploits the fluorescence detection of a low density matrix of protein antigens and secondary fluorophore tagged antibodies, excited by an OLED source. A low cost CMOS camera, allowing low light detection, to perform multiparametric quantitative analysis has been used in combination with a specifically developed image processing software. The software pipeline adopted is described as well. Fourty Dengue patient sera, previously characterized by standard diagnostic techniques, have been tested, using 3 μl of sera and an incubation time of 30 minutes. The results of these analysis demonstrate the capability of this diagnostic system to perform early Dengue diagnosis and to recognize the Dengue serotype with very high precision, in particular in convalescent patients.
2018
15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization CMBBE 2018
1
14
POINT-OF CARE OLED-BASED MULTIPARAMETRIC BIOCHIP EXPLOITING ADVANCED IMAGE PROCESSING FOR THE DENGUE SEROTYPE RECOGNITION / Patrizia Melpignano; Serena Morigi ; Enrico Daniso ; Elena Loli Piccolomini; Luca Benini. - ELETTRONICO. - (2018), pp. 1-14. (Intervento presentato al convegno CMBBE 2018 tenutosi a Lisbona nel Marzo 2018).
Patrizia Melpignano; Serena Morigi ; Enrico Daniso ; Elena Loli Piccolomini; Luca Benini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/642357
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