scholarly journals Automatic Detection Method for Cancer Cell Nucleus Image Based on Deep-Learning Analysis and Color Layer Signature Analysis Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4409
Author(s):  
Hsing-Hao Su ◽  
Hung-Wei Pan ◽  
Chuan-Pin Lu ◽  
Jyun-Jie Chuang ◽  
Tsan Yang

Exploring strategies to treat cancer has always been an aim of medical researchers. One of the available strategies is to use targeted therapy drugs to make the chromosomes in cancer cells unstable such that cell death can be induced, and the elimination of highly proliferative cancer cells can be achieved. Studies have reported that the mitotic defects and micronuclei in cancer cells can be used as biomarkers to evaluate the instability of the chromosomes. Researchers use these two biomarkers to assess the effects of drugs on eliminating cancer cells. However, manual work is required to count the number of cells exhibiting mitotic defects and micronuclei either directly from the viewing window of a microscope or from an image, which is tedious and creates errors. Therefore, this study aims to detect cells with mitotic defects and micronuclei by applying an approach that can automatically count the targets. This approach integrates the application of a convolutional neural network for normal cell identification and the proposed color layer signature analysis (CLSA) to spot cells with mitotic defects and micronuclei. This approach provides a method for researchers to detect colon cancer cells in an accurate and time-efficient manner, thereby decreasing errors and the processing time. The following sections will illustrate the methodology and workflow design of this study, as well as explain the practicality of the experimental comparisons and the results that were used to validate the practicality of this algorithm.

2001 ◽  
Vol 120 (5) ◽  
pp. A493-A493
Author(s):  
J HARDWICK ◽  
G VANDENBRINK ◽  
S VANDEVENTER ◽  
M PEPPELENBOSCH

Endoscopy ◽  
2005 ◽  
Vol 37 (05) ◽  
Author(s):  
GA Doherty ◽  
SM Byrne ◽  
SC Austin ◽  
GM Scully ◽  
EW Kay ◽  
...  

Author(s):  
Mayson H. Alkhatib ◽  
Dalal Al-Saedi ◽  
Wadiah S. Backer

The combination of anticancer drugs in nanoparticles has great potential as a promising strategy to maximize efficacies by eradicating resistant, reduce the dosage of the drug and minimize toxicities on the normal cells. Gemcitabine (GEM), a nucleoside analogue, and atorvastatin (ATV), a cholesterol lowering agent, have shown anticancer effect with some limitations. The objective of this in vitro study was to evaluate the antitumor activity of the combination therapy of GEM and ATVencapsulated in a microemulsion (ME) formulation in the HCT116 colon cancer cells. The cytotoxicity and efficacy of the formulation were assessed by the 3- (4,5dimethylthiazole-2-yl)-2,5-diphyneltetrazolium bromide (MTT) assay. The mechanism of cell death was examined by observing the morphological changes of treated cells under light microscope, identifying apoptosis by using the ApopNexin apoptosis detection kit, and viewing the morphological changes in the chromatin structure stained with 4′,6-diamidino-2-phenylindole (DAPI) under the inverted fluorescence microscope. It has been found that reducing the concentration of GEM loaded on ME (GEM-ME) from 5μM to 1.67μM by combining it with 3.33μM of ATV in a ME formulation (GEM/2ATV-ME) has preserved the strong cytotoxicity of GEM-ME against HCT116 cells. The current study proved that formulating GEM with ATV in ME has improved the therapeutic potential of GEM and ATV as anticancer drugs.


Sign in / Sign up

Export Citation Format

Share Document