scholarly journals Correction: Substrate-free and label-free electrocatalysis-assisted biosensor for sensitive detection of microRNA in lung cancer cells

2020 ◽  
Vol 56 (13) ◽  
pp. 2055-2055
Author(s):  
Lin Cui ◽  
Meng Wang ◽  
Bing Sun ◽  
Shiyun Ai ◽  
Shaocong Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Cells ◽  
Sensitive Detection ◽  
Lung Cancer Cells ◽  
Label Free

Correction for ‘Substrate-free and label-free electrocatalysis-assisted biosensor for sensitive detection of microRNA in lung cancer cells’ by Lin Cui et al., Chem. Commun., 2019, 55, 1172–1175.

Substrate-free and label-free electrocatalysis-assisted biosensor for sensitive detection of microRNA in lung cancer cells

2019 ◽  
Vol 55 (8) ◽  
pp. 1172-1175 ◽  
Author(s):  
Lin Cui ◽  
Meng Wang ◽  
Bing Sun ◽  
Shiyun Ai ◽  
Shaocong Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Cells ◽  
Sensitive Detection ◽  
Lung Cancer Cells ◽  
Label Free

We develop a substrate-free and label-free electrocatalysis-assisted biosensor for sensitive detection of microRNA with FeCN as the catalytic element.

scholarly journals Simultaneous sensitive detection of multiple DNA glycosylases from lung cancer cells at the single-molecule level

2018 ◽  
Vol 9 (3) ◽  
pp. 712-720 ◽  
Author(s):  
Juan Hu ◽  
Ming-hao Liu ◽  
Ying Li ◽  
Bo Tang ◽  
Chun-yang Zhang
Keyword(s):  
Lung Cancer ◽  
Cancer Cells ◽  
Single Molecule ◽  
Simultaneous Detection ◽  
Sensitive Detection ◽  
Lung Cancer Cells ◽  
Dna Glycosylase ◽  
Dna Glycosylases ◽  
Single Molecule Level ◽  
Alkyladenine Dna Glycosylase

We demonstrate the simultaneous detection of human 8-oxoguanine DNA glycosylase 1 and human alkyladenine DNA glycosylase at the single-molecule level.

A recognition-before-labeling strategy for sensitive detection of lung cancer cells with a quantum dot–aptamer complex

The Analyst ◽  
2015 ◽  
Vol 140 (17) ◽  
pp. 6100-6107 ◽  
Author(s):  
Chunlei Wu ◽  
Jianbo Liu ◽  
Pengfei Zhang ◽  
Jing Li ◽  
Haining Ji ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Quantum Dots ◽  
Quantum Dot ◽  
Cancer Cells ◽  
Steric Hindrance ◽  
Sensitive Detection ◽  
Lung Cancer Cells ◽  
Recognition Elements

A recognition-before-labeling strategy is developed for sensitive detection of A549 cancer cells, by using fluorescent quantum dots as signal units and aptamers as recognition elements, which avoided the possible impact on the aptamer configuration from steric hindrance.

scholarly journals Highly sensitive detection of invasive lung cancer cells by novel antibody against amino‐terminal domain of laminin γ2 chain

2016 ◽  
Vol 107 (12) ◽  
pp. 1909-1918 ◽  
Author(s):  
Kaoru Miyazaki ◽  
Jun Oyanagi ◽  
Atsuko Sugino ◽  
Hiroki Sato ◽  
Tomoyuki Yokose ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Cells ◽  
Sensitive Detection ◽  
Lung Cancer Cells ◽  
Amino Terminal ◽  
Terminal Domain ◽  
Highly Sensitive ◽  
Highly Sensitive Detection ◽  
Amino Terminal Domain

scholarly journals Label-Free Quantitative Proteomics and N-terminal Analysis of Human Metastatic Lung Cancer Cells

2014 ◽  
Vol 37 (6) ◽  
pp. 457-466 ◽  
Author(s):  
Hophil Min ◽  
Dohyun Han ◽  
Yikwon Kim ◽  
Jee Yeon Cho ◽  
Jonghwa Jin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Cells ◽  
Quantitative Proteomics ◽  
Lung Cancer Cells ◽  
Metastatic Lung Cancer ◽  
Label Free ◽  
Metastatic Lung

Label-free discrimination of lung cancer cells through mueller matrix decomposition of diffuse reflectance imaging

2018 ◽  
Vol 40 ◽  
pp. 505-518 ◽  
Author(s):  
Suman Shrestha ◽  
Aditi Deshpande ◽  
Tannaz Farrahi ◽  
Thomas Cambria ◽  
Tri Quang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Cells ◽  
Diffuse Reflectance ◽  
Matrix Decomposition ◽  
Mueller Matrix ◽  
Lung Cancer Cells ◽  
Label Free ◽  
Diffuse Reflectance Imaging

scholarly journals Terahertz toroidal metasurface biosensor for sensitive distinction of lung cancer cells

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Chiben Zhang ◽  
Tingjia Xue ◽  
Jin Zhang ◽  
Longhai Liu ◽  
Jianhua Xie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Resonance Frequency ◽  
Cancer Cells ◽  
Incident Angle ◽  
Lung Cancer Cells ◽  
Ring Resonators ◽  
Transmission Spectra ◽  
Label Free ◽  
Human Beings ◽  
Terahertz Transmission

Abstract Lung cancer is the most frequently life-threatening disease and the prominent cause of cancer-related mortality among human beings worldwide, where poor early diagnosis and expensive detection costs are considered as significant reasons. Here, we try to tackle this issue by proposing a novel label-free and low-cost strategy for rapid detection and distinction of lung cancer cells relying on plasmonic toroidal metasurfaces at terahertz frequencies. Three disjoint regions are displayed in identifiable intensity-frequency diagram, which could directly help doctors determine the type of lung cancer cells for clinical treatment. The metasurface is generated by two mirrored gold split ring resonators with subwavelength sizes. When placing analytes on the metasurface, apparent shifts of both the resonance frequency and the resonance depth can be observed in the terahertz transmission spectra. The theoretical sensitivity of the biosensor over the reflective index reaches as high as 485.3 GHz/RIU. Moreover, the proposed metasurface shows high angular stability for oblique incident angle from 0 to 30°, where the maximum resonance frequency shift is less than 0.66% and the maximum transmittance variation keeps below 1.33%. To experimentally verify the sensing strategy, three types of non-small cell lung cancer cells (Calu-1, A427, and 95D) are cultured with different concentrations and their terahertz transmission spectra are measured with the proposed metasurface biosensor. The two-dimensional fingerprint diagram considering both the frequency and transmittance variations of the toroidal resonance dip is obtained, where the curves for different cells are completely separated with each other. This implies that we can directly distinguish the type of the analyte cells and its concentration by only single spectral measurement. We envisage that the proposed strategy has potential for clinical diagnosis and significantly expands the capabilities of plasmonic metamaterials in biological detection.

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