scholarly journals Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap

2020 ◽  
Vol 11 (6) ◽  
pp. 2976
Author(s):  
Sangyeon Youn ◽  
Kyungsu Lee ◽  
Jeehoon Son ◽  
In-Hwan Yang ◽  
Jae Youn Hwang
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Fully Automatic

scholarly journals Forecast Breast Cancer Cells from Microscopic Biopsy Images using Big Transfer (BiT): A Deep Learning Approach

2021 ◽  
Vol 12 (10) ◽  
Author(s):  
Md. Ashiqul Islam ◽  
Dhonita Tripura ◽  
Mithun Dutta ◽  
Md. Nymur Rahman Shuvo ◽  
Wasik Ahmmed Fahim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Learning Approach

Microarray determination of Bcl-2 family protein inhibition sensitivity in breast cancer cells

2013 ◽  
Vol 238 (2) ◽  
pp. 248-256 ◽  
Author(s):  
Sandra G Hudson ◽  
Devin R Halleran ◽  
Barbara Nevaldine ◽  
Anna Shapiro ◽  
Robert E Hutchison ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Protein Inhibition ◽  
Family Protein

Determination of HER2 amplification status in breast cancer cells using Raman spectroscopy

2010 ◽  
Author(s):  
Xiaohong Bi ◽  
Brent Rexer ◽  
Carlos L. Arteaga ◽  
Mingsheng Guo ◽  
Ming Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Raman Spectroscopy ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Her2 Amplification

scholarly journals 3P Determination of therapeutic effects of multifunctional micelle-based nanocarriers on breast cancer cells

2021 ◽  
Vol 32 ◽  
pp. S1
Author(s):  
G.T. Ulu ◽  
N.N. Bayram ◽  
N. Abdulhadi ◽  
S. Gürdap ◽  
A. İşoğlu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Therapeutic Effects

Synthesis of prodigiosene–estrogen conjugates: optimization of protecting group strategies and anticancer properties

2015 ◽  
Vol 93 (5) ◽  
pp. 526-535 ◽  
Author(s):  
Estelle Marchal ◽  
Md. Imam Uddin ◽  
Cassandra L.A. Hawco ◽  
Alison Thompson
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cell Lines ◽  
Breast Cancer Cell ◽  
Breast Cancer Cells ◽  
Breast Cancer Cell Lines ◽  
Protecting Group ◽  
Anticancer Properties ◽  
Hydroxy Groups

The tripyrrolic prodigiosene skeleton was conjugated to several estrogen ligands. The conjugation was achieved via an ester linker that proved to be unusually sensitive to hydrolysis during synthesis. This work describes the determination of an appropriate protecting group for the hydroxy groups of the estrogen linker. The anticancer properties of the target prodigiosene–estrogen conjugates were evaluated against breast cancer cells and some show selectivity for ER+ breast cancer cell lines.

scholarly journals Detection of live breast cancer cells in brightfield microscopy images containing white blood cells by image analysis and deep learning

2021 ◽  
Author(s):  
Golnaz Moallem ◽  
Adity A. Pore ◽  
Anirudh Gangadhar ◽  
Hamed Sari-Sarraf ◽  
Siva A Vanapalli
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Cells ◽  
Blood Cells ◽  
Breast Cancer Cells ◽  
White Blood Cells ◽  
Size Difference ◽  
Microscopy Images

Significance: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization. Aim: The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright field microscopy images that contain white blood cells (WBCs). Approach: We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate cancer cells from WBCs. The second approach is based on Faster Region-based Convolutional Neural Network (Faster R-CNN). Results: Both approaches detected cancer cells with high sensitivity and specificity with the Faster R-CNN being more efficient and suitable for deployment. The distinctive feature used by the CNN used to discriminate is cell size, however, in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations. Conclusions: CNN-based deep learning approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.

Synthesis of a theranostic agent: radioiodinated PEGylated PLGA-indocyanine capsules and in vitro determination of their bioaffinity on ovarian, cervical and breast cancer cells

2015 ◽  
Vol 308 (2) ◽  
pp. 659-670 ◽  
Author(s):  
Levent Akman ◽  
Fazilet Zumrut Biber Muftuler ◽  
Ahmet Bilgi ◽  
Ayfer Yurt Kilcar ◽  
Sevki Goksun Gokulu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Theranostic Agent
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