An Open-Source BioMEMS Platform for Generalized Early Cancer Detection

Cancer remains one of the most pressing challenges for humanity, with early detection significant for improving survival rates. Open-source initiatives have revolutionized various fields by fostering collaboration, broadening impact, and democratizing access to resources. We propose to expand this ethos to cancer research, introducing a versatile biosensing framework developed using open-source tools only. This biosensor is capable of detecting diverse types of cancer simultaneously, or detecting a single type with enhanced sensitivity. To validate the platform, a proof-of-concept design is presented, and limitations of the current open-source ecosystem are discussed.

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Publication Details

Published
2024-09-02
DOI
https://doi.org/10.1109/inscit62583.2024.10693413
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.12
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article

An Open-Source BioMEMS Platform for Generalized Early Cancer Detection

J. P. Nunes, João Frischenbruder Sulzbach, Lucca Reinehr Silva, Thales Vieira e Silva Lobo de Almeida
1 citations
Machine Learning in Materials Science
0.12
article

An Open-Source BioMEMS Platform for Generalized Early Cancer Detection

J. P. Nunes, João Frischenbruder Sulzbach, Lucca Reinehr Silva, Thales Vieira e Silva Lobo de Almeida
article en
1 citations

Abstract

Cancer remains one of the most pressing challenges for humanity, with early detection significant for improving survival rates. Open-source initiatives have revolutionized various fields by fostering collaboration, broadening impact, and democratizing access to resources. We propose to expand this ethos to cancer research, introducing a versatile biosensing framework developed using open-source tools only. This biosensor is capable of detecting diverse types of cancer simultaneously, or detecting a single type with enhanced sensitivity. To validate the platform, a proof-of-concept design is presented, and limitations of the current open-source ecosystem are discussed.

Universidade Federal do Rio de Janeiro (BR), Universidade Federal do Rio Grande do Sul (BR), Universidade Estadual Paulista (Unesp) (BR)
Openalex Percentile: Top 59%
Machine Learning in Materials Science
0.12
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