scholarly journals New 99mTc-Labeled Digitoxigenin Derivative for Cancer Cell Identification

ACS Omega ◽  
2019 ◽  
Vol 4 (26) ◽  
pp. 22048-22056
Author(s):  
Jennifer Munkert ◽  
Eliza R. Gomes ◽  
Lucas L. Marostica ◽  
Betânia B. Cota ◽  
Cristina L. M. Lopes ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Cell Identification

A DNA-functionalized biomass nanoprobe for the targeted photodynamic therapy of tumor and ratiometric fluorescence imaging-based visual cancer cell identification/antitumor drug screening

The Analyst ◽  
2021 ◽  
Author(s):  
Pengxiang Lin ◽  
Liangliang Zhang ◽  
Dongxia Chen ◽  
Jiayao Xu ◽  
Yulong Bai ◽  
...  
Keyword(s):  
Photodynamic Therapy ◽  
Fluorescence Imaging ◽  
Cancer Cell ◽  
Drug Screening ◽  
Antitumor Drug ◽  
Ratiometric Fluorescence ◽  
Cell Identification ◽  
Targeted Photodynamic Therapy

A DNA-functionalized biomass nanoprobe was developed for the targeted photodynamic therapy of tumor and ratiometric fluorescence imaging-based visual cancer cell identification/antitumor drug screening.

High-precision microscopic phase imaging without phase unwrapping for cancer cell identification

2013 ◽  
Vol 38 (8) ◽  
pp. 1319 ◽  
Author(s):  
Eriko Watanabe ◽  
Takashi Hoshiba ◽  
Bahram Javidi
Keyword(s):  
Cancer Cell ◽  
High Precision ◽  
Phase Unwrapping ◽  
Phase Imaging ◽  
Cell Identification

MNAzyme probes mediated DNA logic platform for microRNAs logic detection and cancer cell identification

2021 ◽  
Vol 1149 ◽  
pp. 338213
Author(s):  
Rui Wang ◽  
Shuai Wang ◽  
Xiaowen Xu ◽  
Wei Jiang ◽  
Nan Zhang
Keyword(s):  
Cancer Cell ◽  
Cell Identification

scholarly journals End-to-end learning via a convolutional neural network for cancer cell line classification

2019 ◽  
Vol 1 (1) ◽  
pp. 17-23
Author(s):  
Darlington A. Akogo ◽  
Xavier-Lewis Palmer
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cell Line ◽  
Cancer Cell ◽  
Practical Implication ◽  
Cancer Cell Line ◽  
Quantitative Measure ◽  
Content Type ◽  
Cell Identification ◽  
End To End

Purpose Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification. Design/methodology/approach The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and tested their 6-layer CNN on 1,241 images of MDA-MB-468 and MCF7 breast cancer cell line in an end-to-end fashion, allowing the system to distinguish between the two different cancer cell types. Findings They obtained a 99% accuracy, providing a foundation for more comprehensive systems. Originality/value Value can be found in that systems based on this design can be used to assist cell identification in a variety of contexts, whereas a practical implication can be found that these systems can be deployed to assist biomedical workflows quickly and at low cost. In conclusion, this system demonstrates the potentials of end-to-end learning systems for faster and more accurate automated cell analysis.

Cancer cell identification by facile imaging of intracellular reductive substances with fluorescent nanosensor

Talanta ◽  
2021 ◽  
pp. 122650
Author(s):  
Chunrong Li ◽  
Yuzhi Xu ◽  
Si-Yang Liu ◽  
Yanfei Zhang ◽  
Wen Yin ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Cell Identification

Feature evaluation for automated cancer cell identification

1975 ◽  
Vol 5 (3) ◽  
pp. 245-255 ◽  
Author(s):  
Y. Imasato ◽  
T. Yoneyama ◽  
S. Watanabe ◽  
H. Genchi
Keyword(s):  
Cancer Cell ◽  
Cell Identification ◽  
Feature Evaluation

Progress on Aggregation-induced Emission Probes for Mitochondria Target and Cancer Cell Identification

2021 ◽  
Vol 42 (03) ◽  
pp. 348-360
Author(s):  
Jia-qi PENG ◽  
◽  
Ming CHEN ◽  
An-jun QIN ◽  
Ben-zhong TANG ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Aggregation Induced Emission ◽  
Cell Identification
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