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Bringing clarity and confidence to physicians and patients through accurate, non-invasive cancer detection.
2021 | Atomic Force Microscopy Detects the Difference in Cancer Cells of Different Neoplastic Aggressiveness via Machine Learning
A novel method based on atomic force microscopy (AFM) working in Ringing mode (RM) to distinguish between two similar human colon epithelial cancer cell lines
Cellens to receive “Bit to Bytes” award from Baker-Polito Administration and the Massachusetts Life Sciences Center
Fourteen projects receiving funding to support R&D, innovation in addressing challenges in therapeutic delivery and unlocking potential of data science to answer pressing life sciences
2018 | Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: Detection of bladder cancer
We report an approach in diagnostic imaging based on nanoscale-resolution scanning of surfaces of cells collected from body fluids using a recent modality of atomic
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2009 | AFM Detects Differences in the Surface Brush of Normal and Cancerous Cervical Cells
The atomic force microscope is broadly used to study the
