Single-Molecule Bioelectronic Sensor: Improving Reliability with Machine Learning Approaches

L. Sarcina; C. Scandurra; M. Caputo; M. Catacchio; C. Di Franco; P. Bollella; M. Chironna; F. Torricelli; I. Esposito; R. Osterbacka; G. Scamarcio; E. Macchia; L. Torsi

doi:10.1109/FLEPS57599.2023.10220418

Single-Molecule Bioelectronic Sensor: Improving Reliability with Machine Learning Approaches

L. Sarcina, C. Scandurra, M. Caputo, M. Catacchio, C. Di Franco, P. Bollella, M. Chironna, F. Torricelli, I. Esposito, R. Osterbacka, G. Scamarcio, E. Macchia, L. Torsi^*

^*Corresponding author for this work

Physics

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

Abstract

Digitizing biomarkers analysis by quantifying them at the single-molecule level is the new frontier for advancing the science of precision health. The enhancement of the technical capabilities of bioelectronics systems, by giving clinicians the possibility to rely on biomarkers quantifications down to the single-molecule, holds the potential to revolutionize the way healthcare is provided. Such an analytical tool will indeed enable clinicians to associate a biomarker tiniest increase to the progression of a disease, particularly at its early stage.1 Eventually, physicians will be able to identify the very moment in which the illness state begins. Such an occurrence will enormously enhance their ability of curing diseases by supporting better prognosis and permitting the application of precise treatment methods. The single molecule bio-electronic smart system array for clinical testing - SiMBiT - technology has been developed within the blooming field of precision medicine, leveraging on the single molecule with large transistor (SiMoT)2 lab-based technology that can perform single-molecule detection of both proteins and DNA bio-markers.3,4 Specifically, the SiMBiT technology has lately developed the SiMoT lab-based device into a cost-effective portable prototype multiplexing array that integrates, with a modular approach, standard components and interfaces with novel materials and exhibits enhanced sensing capabilities. The SiMBiT prototype has proven its potency in early detection of pancreatic cancer, being capable to discriminate among low-grade and high-grade mucinous cyst's lesions in peripheral biofluids, such as plasma samples. In this perspective, machine learning approaches play a pivotal role in developing classifiers for a fast, reliable multiparametric biosensors output. Supervised model based on multivariate data processing has been undertaken to enable multiplexing, i.e. the simultaneous quantification of three biomarkers, namely MUC1 and CD55 proteins and KRAS DNA mutated sequence, in plasma and cysts' fluid samples. The main technological aspect of the SiMBiT device, with particular emphasis on the potency of machine learning approaches, will be discussed.

Original language	English
Title of host publication	FLEPS 2023 - IEEE International Conference on Flexible and Printable Sensors and Systems, Proceedings
Publisher	the Institute of Electrical and Electronics Engineers, Inc.
Number of pages	7
ISBN (Electronic)	978-1-6654-5733-0
ISBN (Print)	978-1-6654-5733-0
DOIs	https://doi.org/10.1109/FLEPS57599.2023.10220418
Publication status	Published - 2023
MoE publication type	A4 Article in a conference publication
Event	5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023 - Boston, United States Duration: 9 Jul 2023 → 12 Jul 2023

Publication series

Name	IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), proceedings
Publisher	IEEE
ISSN (Electronic)	2832-8256

Conference

Conference	5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023
Country/Territory	United States
City	Boston
Period	09/07/23 → 12/07/23

Keywords

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10.1109/FLEPS57599.2023.10220418Licence: All rights reserved

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Sarcina, L., Scandurra, C., Caputo, M., Catacchio, M., Di Franco, C., Bollella, P., Chironna, M., Torricelli, F., Esposito, I., Osterbacka, R., Scamarcio, G., Macchia, E., & Torsi, L. (2023). Single-Molecule Bioelectronic Sensor: Improving Reliability with Machine Learning Approaches. In FLEPS 2023 - IEEE International Conference on Flexible and Printable Sensors and Systems, Proceedings (IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), proceedings). the Institute of Electrical and Electronics Engineers, Inc.. https://doi.org/10.1109/FLEPS57599.2023.10220418

Sarcina, L. ; Scandurra, C. ; Caputo, M. et al. / Single-Molecule Bioelectronic Sensor : Improving Reliability with Machine Learning Approaches. FLEPS 2023 - IEEE International Conference on Flexible and Printable Sensors and Systems, Proceedings. the Institute of Electrical and Electronics Engineers, Inc., 2023. (IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), proceedings).

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title = "Single-Molecule Bioelectronic Sensor: Improving Reliability with Machine Learning Approaches",

abstract = "Digitizing biomarkers analysis by quantifying them at the single-molecule level is the new frontier for advancing the science of precision health. The enhancement of the technical capabilities of bioelectronics systems, by giving clinicians the possibility to rely on biomarkers quantifications down to the single-molecule, holds the potential to revolutionize the way healthcare is provided. Such an analytical tool will indeed enable clinicians to associate a biomarker tiniest increase to the progression of a disease, particularly at its early stage.1 Eventually, physicians will be able to identify the very moment in which the illness state begins. Such an occurrence will enormously enhance their ability of curing diseases by supporting better prognosis and permitting the application of precise treatment methods. The single molecule bio-electronic smart system array for clinical testing - SiMBiT - technology has been developed within the blooming field of precision medicine, leveraging on the single molecule with large transistor (SiMoT)2 lab-based technology that can perform single-molecule detection of both proteins and DNA bio-markers.3,4 Specifically, the SiMBiT technology has lately developed the SiMoT lab-based device into a cost-effective portable prototype multiplexing array that integrates, with a modular approach, standard components and interfaces with novel materials and exhibits enhanced sensing capabilities. The SiMBiT prototype has proven its potency in early detection of pancreatic cancer, being capable to discriminate among low-grade and high-grade mucinous cyst's lesions in peripheral biofluids, such as plasma samples. In this perspective, machine learning approaches play a pivotal role in developing classifiers for a fast, reliable multiparametric biosensors output. Supervised model based on multivariate data processing has been undertaken to enable multiplexing, i.e. the simultaneous quantification of three biomarkers, namely MUC1 and CD55 proteins and KRAS DNA mutated sequence, in plasma and cysts' fluid samples. The main technological aspect of the SiMBiT device, with particular emphasis on the potency of machine learning approaches, will be discussed.",

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author = "L. Sarcina and C. Scandurra and M. Caputo and M. Catacchio and {Di Franco}, C. and P. Bollella and M. Chironna and F. Torricelli and I. Esposito and R. Osterbacka and G. Scamarcio and E. Macchia and L. Torsi",

note = "BOF 17/10/23 EK Funding Information: H2020 - Electronic Smart Systems – SiMBiT: Single-molecule bio-electronic smart system array for clinical testing (Grant agreement ID: 824946), NoOne-A binary sensor with single-molecule digit to discriminate biofluids enclosing zero or at least one biomarker, ERC Stg2021, GA:101040383, Biosensori analitici usa-e getta a base di transistori organici auto-alimentati per la rivelazione di biomarcatori proteomici alla singola molecola per la diagnostica decentrata dell'HIV (6CDD3786); Research for Innovation REFIN—Regione Puglia POR PUGLIA FESR-FSE 2014/2020, PMGB—Sviluppo di piattaforme meccatroniche, genomiche e bioinformatiche per l'oncologia di precisione—ARS01_01195-PON {"}RICERCA E INNOVAZIONE{"} 2014–2020, {\AA}bo Akademi University CoE {"}Bioelectronic activation of cell functions{"}, Svenska Tekniska Vetenskapsakademien i Finland, and CSGI are acknowledged for partial financial support. Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023 ; Conference date: 09-07-2023 Through 12-07-2023",

year = "2023",

doi = "10.1109/FLEPS57599.2023.10220418",

language = "English",

isbn = "978-1-6654-5733-0",

series = "IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), proceedings",

publisher = "the Institute of Electrical and Electronics Engineers, Inc.",

booktitle = "FLEPS 2023 - IEEE International Conference on Flexible and Printable Sensors and Systems, Proceedings",

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Sarcina, L, Scandurra, C, Caputo, M, Catacchio, M, Di Franco, C, Bollella, P, Chironna, M, Torricelli, F, Esposito, I, Osterbacka, R, Scamarcio, G, Macchia, E & Torsi, L 2023, Single-Molecule Bioelectronic Sensor: Improving Reliability with Machine Learning Approaches. in FLEPS 2023 - IEEE International Conference on Flexible and Printable Sensors and Systems, Proceedings. IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), proceedings, the Institute of Electrical and Electronics Engineers, Inc., 5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023, Boston, United States, 09/07/23. https://doi.org/10.1109/FLEPS57599.2023.10220418

Single-Molecule Bioelectronic Sensor: Improving Reliability with Machine Learning Approaches. / Sarcina, L.; Scandurra, C.; Caputo, M. et al.
FLEPS 2023 - IEEE International Conference on Flexible and Printable Sensors and Systems, Proceedings. the Institute of Electrical and Electronics Engineers, Inc., 2023. (IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), proceedings).

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

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AU - Catacchio, M.

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AU - Bollella, P.

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AU - Torricelli, F.

AU - Esposito, I.

AU - Osterbacka, R.

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PY - 2023

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N2 - Digitizing biomarkers analysis by quantifying them at the single-molecule level is the new frontier for advancing the science of precision health. The enhancement of the technical capabilities of bioelectronics systems, by giving clinicians the possibility to rely on biomarkers quantifications down to the single-molecule, holds the potential to revolutionize the way healthcare is provided. Such an analytical tool will indeed enable clinicians to associate a biomarker tiniest increase to the progression of a disease, particularly at its early stage.1 Eventually, physicians will be able to identify the very moment in which the illness state begins. Such an occurrence will enormously enhance their ability of curing diseases by supporting better prognosis and permitting the application of precise treatment methods. The single molecule bio-electronic smart system array for clinical testing - SiMBiT - technology has been developed within the blooming field of precision medicine, leveraging on the single molecule with large transistor (SiMoT)2 lab-based technology that can perform single-molecule detection of both proteins and DNA bio-markers.3,4 Specifically, the SiMBiT technology has lately developed the SiMoT lab-based device into a cost-effective portable prototype multiplexing array that integrates, with a modular approach, standard components and interfaces with novel materials and exhibits enhanced sensing capabilities. The SiMBiT prototype has proven its potency in early detection of pancreatic cancer, being capable to discriminate among low-grade and high-grade mucinous cyst's lesions in peripheral biofluids, such as plasma samples. In this perspective, machine learning approaches play a pivotal role in developing classifiers for a fast, reliable multiparametric biosensors output. Supervised model based on multivariate data processing has been undertaken to enable multiplexing, i.e. the simultaneous quantification of three biomarkers, namely MUC1 and CD55 proteins and KRAS DNA mutated sequence, in plasma and cysts' fluid samples. The main technological aspect of the SiMBiT device, with particular emphasis on the potency of machine learning approaches, will be discussed.

AB - Digitizing biomarkers analysis by quantifying them at the single-molecule level is the new frontier for advancing the science of precision health. The enhancement of the technical capabilities of bioelectronics systems, by giving clinicians the possibility to rely on biomarkers quantifications down to the single-molecule, holds the potential to revolutionize the way healthcare is provided. Such an analytical tool will indeed enable clinicians to associate a biomarker tiniest increase to the progression of a disease, particularly at its early stage.1 Eventually, physicians will be able to identify the very moment in which the illness state begins. Such an occurrence will enormously enhance their ability of curing diseases by supporting better prognosis and permitting the application of precise treatment methods. The single molecule bio-electronic smart system array for clinical testing - SiMBiT - technology has been developed within the blooming field of precision medicine, leveraging on the single molecule with large transistor (SiMoT)2 lab-based technology that can perform single-molecule detection of both proteins and DNA bio-markers.3,4 Specifically, the SiMBiT technology has lately developed the SiMoT lab-based device into a cost-effective portable prototype multiplexing array that integrates, with a modular approach, standard components and interfaces with novel materials and exhibits enhanced sensing capabilities. The SiMBiT prototype has proven its potency in early detection of pancreatic cancer, being capable to discriminate among low-grade and high-grade mucinous cyst's lesions in peripheral biofluids, such as plasma samples. In this perspective, machine learning approaches play a pivotal role in developing classifiers for a fast, reliable multiparametric biosensors output. Supervised model based on multivariate data processing has been undertaken to enable multiplexing, i.e. the simultaneous quantification of three biomarkers, namely MUC1 and CD55 proteins and KRAS DNA mutated sequence, in plasma and cysts' fluid samples. The main technological aspect of the SiMBiT device, with particular emphasis on the potency of machine learning approaches, will be discussed.

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Sarcina L, Scandurra C, Caputo M, Catacchio M, Di Franco C, Bollella P et al. Single-Molecule Bioelectronic Sensor: Improving Reliability with Machine Learning Approaches. In FLEPS 2023 - IEEE International Conference on Flexible and Printable Sensors and Systems, Proceedings. the Institute of Electrical and Electronics Engineers, Inc. 2023. (IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), proceedings). doi: 10.1109/FLEPS57599.2023.10220418

Single-Molecule Bioelectronic Sensor: Improving Reliability with Machine Learning Approaches

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

BACE: Center of Excellence in Bioelectronic Activation of Cell Functions

SIMBIT: Single molecule bio-electronic smart system array for clinical testing

Cite this

Single-Molecule Bioelectronic Sensor: Improving Reliability with Machine Learning Approaches

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

BACE: Center of Excellence in Bioelectronic Activation of Cell Functions

SIMBIT: Single molecule bio-electronic smart system array for clinical testing

Cite this