Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Physical confinement and distance of migration cooperatively enhance chemotherapeutic resistance in migratory GBM cells.

Physical biology·2025
Same author

Corrigendum to 'Changes in the tumor immune microenvironment during disease progression in clear cell ovarian cancer' [International Journal of Gynecological Cancer, Volume 34 Issue 11 (2024) 1780-1786].

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society·2025
Same author

Changes in the tumor immune microenvironment during disease progression in clear cell ovarian cancer.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society·2025
Same author

Changes in the tumor immune microenvironment during disease progression in clear cell ovarian cancer.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society·2024
Same author

Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis.

Journal of biomedical science·2024
Same author

Efficacy and safety of BVAC-C in HPV type 16- or 18-positive cervical carcinoma who failed 1st platinum-based chemotherapy: a phase I/IIa study.

Frontiers in immunology·2024

Related Experiment Video

Updated: May 7, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

6.4K

Quantitative classification of tumor cell morphological changes on selectively functionalized biochips.

Mohammed A I Mahmood, Chaudhry M A Arafat, Young-tae Kim

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary

    This study presents a new method using aptamer-functionalized chips to detect cancer cells by measuring their behavior. Nanoscale surface textures improved tumor cell isolation and differentiation from normal cells for early cancer detection.

    More Related Videos

    Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
    09:48

    Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

    Published on: June 30, 2017

    9.2K
    Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
    08:59

    Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

    Published on: October 28, 2018

    8.7K

    Related Experiment Videos

    Last Updated: May 7, 2026

    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
    09:53

    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

    Published on: August 16, 2020

    6.4K
    Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
    09:48

    Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

    Published on: June 30, 2017

    9.2K
    Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
    08:59

    Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

    Published on: October 28, 2018

    8.7K

    Area of Science:

    • Biotechnology
    • Cancer Research
    • Nanotechnology

    Background:

    • Cancer progression is linked to molecular abnormalities, including growth factor receptor dysregulation.
    • Epidermal growth factor receptor (EGFR) is a frequently overexpressed cancer biomarker.
    • Current methods for cancer cell detection can be limited in sensitivity and specificity.

    Purpose of the Study:

    • To develop a quantifiable method for measuring tumor cell behavior on functionalized chips.
    • To utilize aptamers targeting EGFR for selective cancer cell isolation.
    • To investigate the impact of nanoscale surface textures on tumor cell capture and behavior analysis.

    Main Methods:

    • Functionalizing chips with aptamers specific to epidermal growth factor receptor (EGFR).
    • Isolating tumor cells from mixed cell samples using aptamer-bound chips.
    • Chemically treating chip surfaces to create nanoscale textures.
    • Quantifying distinct behavioral differences between captured cancer and normal cells.

    Main Results:

    • Aptamer-functionalized chips selectively isolated tumor cells.
    • Nanoscale surface texturing enhanced the selectivity and sensitivity of tumor cell isolation.
    • Distinct behavioral patterns were observed for cancer cells compared to normal cells on the chip surface.
    • The developed method offers a directly quantifiable approach to tumor cell behavior.

    Conclusions:

    • This chip-based approach provides a novel modality for cancer cell detection.
    • The method demonstrates potential for analyzing cancer cells in simple biological samples like blood, saliva, or urine.
    • Quantifying cell behavior on functionalized, nanostructured surfaces offers a promising avenue for early cancer diagnostics.