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A multistage volumetric bar chart chip for visualized quantification of DNA.

ABSTRACT: Nucleic acid detection is critical in disease diagnosis as well as in the environmental assays of harmful bacteria or viruses and forensic applications. Current methods for visualized quantification of DNA require costly and sophisticated instruments. Here, we report a multistage propelled volumetric bar chart chip (MV-Chip) for multiplexing and quantitative detection of DNA. Because of its "rocket-like" propelling reaction, the predeposited platinum films could perform cascade amplification and detect as low as 20 pM DNA targets after three stages of platinum-catalyzed propulsion. The resulting ink bar charts can be directly read out by the naked eye, and the signal shows little interference from serum. Single-nucleotide polymorphism and multiplex DNA detection were carried out to demonstrate this powerful application.

SUBMITTER: Song Y

PROVIDER: S-EPMC3875332 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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A multistage volumetric bar chart chip for visualized quantification of DNA.

Song Yujun Y Wang Yuanchen Y Qin Lidong L

Journal of the American Chemical Society 20131029 45

Nucleic acid detection is critical in disease diagnosis as well as in the environmental assays of harmful bacteria or viruses and forensic applications. Current methods for visualized quantification of DNA require costly and sophisticated instruments. Here, we report a multistage propelled volumetric bar chart chip (MV-Chip) for multiplexing and quantitative detection of DNA. Because of its "rocket-like" propelling reaction, the predeposited platinum films could perform cascade amplification and ...[more]

PMID: 24160770

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Project description:Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC?=?0.85±0.15, p?=?0.0009) compared to the features extracted from the manual segmentations (ICC?=?0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p?=?3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p?=?0.007, higher: p?=?5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.

Dataset Information

A multistage volumetric bar chart chip for visualized quantification of DNA.

Publications

A multistage volumetric bar chart chip for visualized quantification of DNA.

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