The use of large-scale cDNA analysis to profile differential gene expression in KYSE 410 human esophageal cancer cells after irradiation

2000 ◽  
Vol 130 (3) ◽  
pp. 882-885 ◽  
Author(s):  
J. L. Sebastian ◽  
D. A. Rigberg ◽  
E. Shrivatsan ◽  
E. Revasova ◽  
D. M. McFadden ◽  
...  
Keyword(s):  
Gene Expression ◽  
Esophageal Cancer ◽  
Cancer Cells ◽  
Differential Gene Expression ◽  
Large Scale ◽  
Cdna Analysis ◽  
Differential Gene ◽  
Human Esophageal Cancer ◽  
Esophageal Cancer Cells

scholarly journals Characteristics of P-Type and N-Type Photoelectrochemical Biosensors: A Case Study for Esophageal Cancer Detection

Nanomaterials ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1065
Author(s):  
Joseph-Hang Leung ◽  
Hong-Thai Nguyen ◽  
Shih-Wei Feng ◽  
Sofya B. Artemkina ◽  
Vladimir E. Fedorov ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Cancer Cells ◽  
Carrier Transport ◽  
X Ray Diffraction ◽  
Photoelectrochemical Response ◽  
Carrier Separation ◽  
Human Esophageal Cancer ◽  
P Type ◽  
Esophageal Cancer Cells

P-type and N-type photoelectrochemical (PEC) biosensors were established in the laboratory to discuss the correlation between characteristic substances and photoactive material properties through the photogenerated charge carrier transport mechanism. Four types of human esophageal cancer cells (ECCs) were analyzed without requiring additional bias voltage. Photoelectrical characteristics were examined by scanning electron microscopy (SEM), X-ray diffraction (XRD), UV–vis reflectance spectroscopy, and photocurrent response analyses. Results showed that smaller photocurrent was measured in cases with advanced cancer stages. Glutathione (L-glutathione reduced, GSH) and Glutathione disulfide (GSSG) in cancer cells carry out redox reactions during carrier separation, which changes the photocurrent. The sensor can identify ECC stages with a certain level of photoelectrochemical response. The detection error can be optimized by adjusting the number of cells, and the detection time of about 5 min allowed repeated measurement.

scholarly journals M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data

2019 ◽  
Vol 20 (S24) ◽  
Author(s):  
Yu Zhang ◽  
Changlin Wan ◽  
Pengcheng Wang ◽  
Wennan Chang ◽  
Yan Huo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Model Selection ◽  
Statistical Model ◽  
Single Cell ◽  
Differential Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Sequencing Data ◽  
Differential Gene ◽  
Selection Of

Abstract Background Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. Results We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. Conclusion A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.

Bioactivities of Sophorolipid with Different Structures Against Human Esophageal Cancer Cells

2012 ◽  
Vol 173 (2) ◽  
pp. 286-291 ◽  
Author(s):  
Lingjian Shao ◽  
Xin Song ◽  
Xiaojing Ma ◽  
Hui Li ◽  
Yinbo Qu
Keyword(s):  
Esophageal Cancer ◽  
Cancer Cells ◽  
Human Esophageal Cancer ◽  
Esophageal Cancer Cells

Differential Gene Expression in Human Cancer Cells Resistant to Cisplatin

1989 ◽  
Vol 7 (6) ◽  
pp. 581-587 ◽  
Author(s):  
Kevin J. Scanlon ◽  
Mohammed Kashani-Sabet ◽  
Hayato Miyachi
Keyword(s):  
Gene Expression ◽  
Cancer Cells ◽  
Differential Gene Expression ◽  
Human Cancer ◽  
Human Cancer Cells ◽  
Differential Gene
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