Dataset Information

Electronic control of H+ current in a bioprotonic device with Gramicidin A and Alamethicin.

ABSTRACT: In biological systems, intercellular communication is mediated by membrane proteins and ion channels that regulate traffic of ions and small molecules across cell membranes. A bioelectronic device with ion channels that control ionic flow across a supported lipid bilayer (SLB) should therefore be ideal for interfacing with biological systems. Here, we demonstrate a biotic-abiotic bioprotonic device with Pd contacts that regulates proton (H+) flow across an SLB incorporating the ion channels Gramicidin A (gA) and Alamethicin (ALM). We model the device characteristics using the Goldman-Hodgkin-Katz (GHK) solution to the Nernst-Planck equation for transport across the membrane. We derive the permeability for an SLB integrating gA and ALM and demonstrate pH control as a function of applied voltage and membrane permeability. This work opens the door to integrating more complex H+ channels at the Pd contact interface to produce responsive biotic-abiotic devices with increased functionality.

SUBMITTER: Hemmatian Z

PROVIDER: S-EPMC5059763 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Publications

Electronic control of H<sup>+</sup> current in a bioprotonic device with Gramicidin A and Alamethicin.

Hemmatian Zahra Z Keene Scott S Josberger Erik E Miyake Takeo T Arboleda Carina C Soto-Rodríguez Jessica J Baneyx François F Rolandi Marco M

Nature communications 20161007

In biological systems, intercellular communication is mediated by membrane proteins and ion channels that regulate traffic of ions and small molecules across cell membranes. A bioelectronic device with ion channels that control ionic flow across a supported lipid bilayer (SLB) should therefore be ideal for interfacing with biological systems. Here, we demonstrate a biotic-abiotic bioprotonic device with Pd contacts that regulates proton (H<sup>+</sup>) flow across an SLB incorporating the ion ch ...[more]

PMID: 27713411

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Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

Dataset Information

Electronic control of H+ current in a bioprotonic device with Gramicidin A and Alamethicin.

Publications

Electronic control of H<sup>+</sup> current in a bioprotonic device with Gramicidin A and Alamethicin.

Similar Datasets

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