Dataset Information

Plasmon based biosensor for distinguishing different peptides mutation states.

ABSTRACT: Periodic and reproducible gold nanocuboids with various matrix dimensions and with different inter-particle gaps were fabricated by means of top-down technique. Rhodamine 6G was used as a probe molecule to optimize the design and the fabrication of the cuboid nanostructures. The electric field distribution for the nanocuboids with varying matrix dimensions/inter-particle gap was also investigated. These SERS devices were employed as biosensors through the investigation of both myoglobin and wild/mutated peptides. The results demonstrate the probing and the screening of wild/mutated BRCA1 peptides, thus opening a path for the fabrication of simple and cheap SERS device capable of early detection of several diseases.

SUBMITTER: Das G

PROVIDER: S-EPMC3647166 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Plasmon based biosensor for distinguishing different peptides mutation states.

Das Gobind G Chirumamilla Manohar M Toma Andrea A Gopalakrishnan Anisha A Zaccaria Remo Proietti RP Alabastri Alessandro A Leoncini Marco M Di Fabrizio Enzo E

Scientific reports 20130101

Periodic and reproducible gold nanocuboids with various matrix dimensions and with different inter-particle gaps were fabricated by means of top-down technique. Rhodamine 6G was used as a probe molecule to optimize the design and the fabrication of the cuboid nanostructures. The electric field distribution for the nanocuboids with varying matrix dimensions/inter-particle gap was also investigated. These SERS devices were employed as biosensors through the investigation of both myoglobin and wild ...[more]

PMID: 23652645

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Project description:BackgroundEstimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment.MethodsA novel method of distinguishing different anaesthesia states was developed based on four HRV-derived features in the time and frequency domain combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which utilized the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.ResultsThe accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.ConclusionsThe incorporation of four HRV-derived features in the time and frequency domain and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot feasibility study. The proposed method-with other evaluation methods, such as EEG-is expected to assist anaesthesiologists in the accurate evaluation of the DoA.

Dataset Information

Plasmon based biosensor for distinguishing different peptides mutation states.

Publications

Plasmon based biosensor for distinguishing different peptides mutation states.

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