Isolation of rare cancer cells from blood cells using dielectrophoresis

2012 ◽  
Author(s):  
A. Salmanzadeh ◽  
M. B. Sano ◽  
H. Shafiee ◽  
M. A. Stremler ◽  
R. V. Davalos
Keyword(s):  
Cancer Cells ◽  
Blood Cells ◽  
Rare Cancer

Effective reduction of non-specific binding of blood cells in a microfluidic chip for isolation of rare cancer cells

2018 ◽  
Vol 6 (11) ◽  
pp. 2871-2880 ◽  
Author(s):  
Dan Yu ◽  
Ling Tang ◽  
Ziye Dong ◽  
Kevin A. Loftis ◽  
Zhenya Ding ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Microfluidic Chip ◽  
Blood Cells ◽  
Specific Binding ◽  
Rare Cancer ◽  
Effective Reduction

Effective reducing non-specific binding of blood cells in microchips by sheathing the surface with a biodegradable multilayer nanofilm.

scholarly journals Development of a bispecific immune engager using a recombinant malaria protein

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Mie A. Nordmaj ◽  
Morgan E. Roberts ◽  
Emilie S. Sachse ◽  
Robert Dagil ◽  
Anne Poder Andersen ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Immune Evasion ◽  
Blood Cells ◽  
Xenograft Model ◽  
Cancer Targeting ◽  
Single Chain Variable Fragment ◽  
Single Chain ◽  
Conjugation System ◽  
Immune Mediated

AbstractAs an immune evasion and survival strategy, the Plasmodium falciparum malaria parasite has evolved a protein named VAR2CSA. This protein mediates sequestration of infected red blood cells in the placenta through the interaction with a unique carbohydrate abundantly and exclusively present in the placenta. Cancer cells were found to share the same expression of this distinct carbohydrate, termed oncofetal chondroitin sulfate on their surface. In this study we have used a protein conjugation system to produce a bispecific immune engager, V-aCD3, based on recombinant VAR2CSA as the cancer targeting moiety and an anti-CD3 single-chain variable fragment linked to a single-chain Fc as the immune engager. Conjugation of these two proteins resulted in a single functional moiety that induced immune mediated killing of a broad range of cancer cells in vitro and facilitated tumor arrest in an orthotopic bladder cancer xenograft model.

Abstract 188: Deep learning enables label-free profiling of the tumor microenvironment and enrichment of rare cancer cells

2021 ◽  
Author(s):  
Mahyar Salek ◽  
Hou-pu Chou ◽  
Prashast Khandelwal ◽  
Krishna P. Pant ◽  
Thomas J. Musci ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tumor Microenvironment ◽  
Cancer Cells ◽  
Rare Cancer ◽  
Label Free

A Microfluidic Platform for On-Chip Analysis of Circulating Tumor Cells

2021 ◽  
Author(s):  
Jeff Darabi ◽  
Joseph Schober
Keyword(s):  
Cancer Cells ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Whole Blood ◽  
Peripheral Blood ◽  
Automated Image Analysis ◽  
Rare Cancer ◽  
On Chip ◽  
Chip Analysis ◽  
Selection Of

Abstract Studies have shown that primary tumor sites begin shedding cancerous cells into peripheral blood at early stages of cancer, and the presence and frequency of circulating tumor cells (CTCs) in blood is directly proportional to disease progression. The challenge is that the concentration of the CTCs in peripheral blood may be extremely low. In the past few years, several microfluidic-based concepts have been investigated to isolate CTCs from whole blood. However, these devices are generally hampered by complex fabrication processes and very low volumetric throughputs, which may not be practical for rapid clinical applications. This paper presents a high-performance yet simple magnetophoretic microfluidic chip for the enrichment and on-chip analysis of rare CTCs from blood. Microscopic and flow cytometric assays developed for selection of cancer cell lines, selection of monoclonal antibodies, and optimization of bead coupling are discussed. Additionally, on-chip characterization of rare cancer cells using high resolution immunofluorescence microscopy and modeling results for prediction of CTC capture length are presented. The device has the ability to interface directly with on-chip pre and post processing modules such as mixing, incubation, and automated image analysis systems. These features will enable us to isolate rare cancer cells from whole blood and detect them on the chip with subcellular resolution.

Capture and detection of rare cancer cells in blood by intrinsic fluorescence of a novel functionalized diatom

2020 ◽  
Vol 30 ◽  
pp. 101753 ◽  
Author(s):  
Javid Esfandyari ◽  
Behnaz Shojaedin-Givi ◽  
Hadi Hashemzadeh ◽  
Mohammad Mozafari-Nia ◽  
Zahra Vaezi ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Intrinsic Fluorescence ◽  
Rare Cancer

scholarly journals Differentiation potential to neural-like cells in human adrenocortical carcinoma SW-13 cells

2019 ◽  
Author(s):  
Eriko Shimada ◽  
Yusuke Tsuruwaka
Keyword(s):  
Early Diagnosis ◽  
Adrenal Gland ◽  
Cancer Cells ◽  
Adrenocortical Carcinoma ◽  
Neural Differentiation ◽  
Neural Induction ◽  
Carcinoma Cells ◽  
Rare Cancer ◽  
Differentiation Potential ◽  
Aggressive Tumor

Various cancer cells are known to show neural differentiation. Adrenocortical carcinoma (ACC) is a rare and frequently aggressive tumor originating in the cortex of the adrenal gland. Early diagnosis of ACC is challenging due to a lot of unknown aspects such as cell characteristics in a rare cancer. In the present study, morphological features were examined in the adrenal cortex carcinoma cells SW-13 as an initial candidate, which were exposed to neural differentiation condition. SW-13 cells treated with the neural induction supplement showed neural-like differentiation with elongated filaments. It was suggested that SW-13 cells had neural differentiation potential and could be a research tool to elucidate the cell characteristics in future ACC studies.

scholarly journals Detection of Blood Cancer Cells Using Microscopic Images

2021 ◽  
Vol 9 (8) ◽  
pp. 995-1003
Author(s):  
Vidyashree M S
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Blood Cancer ◽  
Acute Myeloid

Abstract: Blood Cancer cells forming a tissue is called lymphoma. Thus, disease decreases the cells to fight against the infection or cancer blood cells. Blood cancer is also categorized in too many types. The two main categories of blood cancer are Acute Lymphocytic Lymphoma and Acute Myeloid Lymphoma. In this project proposes a approach that robotic detects and segments the nucleolus from white blood cells in the microscopic Blood images. Here in this project, we have used the two Machine learning algorithms that are k-means algorithm, Support vector machine algorithm. K-mean algorithm is use for segmentation and clustering. Support vector machine algorithm is used for classification. Keywords: k-means, Support vector machine, Lymphoma, Acute Lymphocytic Lymphoma, Machine Learning

A New Nanorobots Movement Control Strategy for Treating Cancer

2021 ◽  
Vol 12 (4) ◽  
pp. 149-163
Author(s):  
Doaa Ezzat ◽  
Safaa El-Sayed Amin ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Fuzzy Logic ◽  
Cancer Cells ◽  
Control Strategy ◽  
Blood Cells ◽  
Movement Control ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
New Strategy ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle

Nanorobots were proposed to deliver drugs directly into cancer cells to destroy only these cells without harming the surrounding cells. During their journey, the nanorobots may encounter some obstacles such as blood cells which may be resistant to their movement. So, it is necessary to avoid collisions with these obstacles to achieve their goal. This study proposes a new strategy for controlling the nanorobots movement in human body to reach cancer cells. This proposed strategy uses an efficient algorithm based on fuzzy logic for dynamic obstacle avoidance. Also, this proposed strategy uses the directed particle swarm optimization (DPSO) algorithm for delivering nanorobots to cancer cells. Simulation experiments have proved that the proposed control strategy can efficiently deliver nanorobots to their target and also avoid collisions with dynamic obstacles which move in the same direction of the nanorobots or across their direction.

Sign in / Sign up

Export Citation Format

Share Document