Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells
Detecting circulating tumor cells (CTCs) is a challenge in cancer research. Their dissemination into the blood stream represents a crucial event in the formation of the metastases from the primary tumor. For this reason, targeting CTCs in human liquid biopsies is a warning event for cancer invasiven...
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MDPI AG
2024-03-01
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| author | Joanna Filippi Francesca Corsi Paola Casti Gianni Antonelli Michele D’Orazio Francesco Capradossi Rosamaria Capuano Giorgia Curci Lina Ghibelli Arianna Mencattini Eugenio Martinelli |
| author_facet | Joanna Filippi Francesca Corsi Paola Casti Gianni Antonelli Michele D’Orazio Francesco Capradossi Rosamaria Capuano Giorgia Curci Lina Ghibelli Arianna Mencattini Eugenio Martinelli |
| author_sort | Joanna Filippi |
| collection | DOAJ |
| description | Detecting circulating tumor cells (CTCs) is a challenge in cancer research. Their dissemination into the blood stream represents a crucial event in the formation of the metastases from the primary tumor. For this reason, targeting CTCs in human liquid biopsies is a warning event for cancer invasiveness, progression, and prognosis. In this regard, by means of the optically induced dielectrophoresis (ODEP) technique, we investigated the response to the electric field, at different frequencies, of human prostatic carcinoma PC3 cells, which mimic CTCs derived from prostate cancer, and human leukemia monocytic THP-1 cells, which simulate circulating monocytes. The obtained spectra of the cell motion descriptors represent the unique identification signature of each cell type. |
| format | Article |
| id | doaj-art-37d7c922af4c4e04bf24b925d48ef66f |
| institution | Kabale University |
| issn | 2504-3900 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj-art-37d7c922af4c4e04bf24b925d48ef66f2024-12-27T14:48:43ZengMDPI AGProceedings2504-39002024-03-019717110.3390/proceedings2024097071Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor CellsJoanna Filippi0Francesca Corsi1Paola Casti2Gianni Antonelli3Michele D’Orazio4Francesco Capradossi5Rosamaria Capuano6Giorgia Curci7Lina Ghibelli8Arianna Mencattini9Eugenio Martinelli10Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Biology, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Biology, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Biology, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyDetecting circulating tumor cells (CTCs) is a challenge in cancer research. Their dissemination into the blood stream represents a crucial event in the formation of the metastases from the primary tumor. For this reason, targeting CTCs in human liquid biopsies is a warning event for cancer invasiveness, progression, and prognosis. In this regard, by means of the optically induced dielectrophoresis (ODEP) technique, we investigated the response to the electric field, at different frequencies, of human prostatic carcinoma PC3 cells, which mimic CTCs derived from prostate cancer, and human leukemia monocytic THP-1 cells, which simulate circulating monocytes. The obtained spectra of the cell motion descriptors represent the unique identification signature of each cell type.https://www.mdpi.com/2504-3900/97/1/71optically induced dielectrophoresismachine learningLab-on-ChipCTCs |
| spellingShingle | Joanna Filippi Francesca Corsi Paola Casti Gianni Antonelli Michele D’Orazio Francesco Capradossi Rosamaria Capuano Giorgia Curci Lina Ghibelli Arianna Mencattini Eugenio Martinelli Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells Proceedings optically induced dielectrophoresis machine learning Lab-on-Chip CTCs |
| title | Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells |
| title_full | Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells |
| title_fullStr | Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells |
| title_full_unstemmed | Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells |
| title_short | Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells |
| title_sort | optically induced dielectrophoresis and machine learning algorithms for the identification of the circulating tumor cells |
| topic | optically induced dielectrophoresis machine learning Lab-on-Chip CTCs |
| url | https://www.mdpi.com/2504-3900/97/1/71 |
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