Dataset Information

A Readily Scalable, Clinically Demonstrated, Antibiofouling Zwitterionic Surface Treatment for Implantable Medical Devices.

ABSTRACT: Unlike growth on tissue, microbes can grow freely on implantable devices with minimal immune system intervention and often form resilient biofilms that continuously pump out pathogenic cells. The efficacy of antibiotics used to treat infection is declining due to increased rates of pathogenic resistance. A simple, one-step zwitterionic surface modification is developed to significantly reduce protein and microbial adhesion to synthetic materials and demonstrate the successful modification of several clinically relevant materials, including recalcitrant materials such as elastomeric polydimethylsiloxane. The treated surfaces exhibit robust adhesion resistance against proteins and microorganisms in both static and flow conditions. Furthermore, the surface treatment prevents the adhesion of mammalian fibroblast cells while displaying no cytotoxicity. To demonstrate the clinical efficacy of the novel technology in the real-world, a surface-treated, commercial silicone foley catheter is developed that is cleared for use by the U.S. Food and Drug Administration (K192034). 16 long-term catheterized patients received surface-treated catheters and completed a Patient Global Impression of Improvement (PGI-I) questionnaire. 10 out of 16 patients described their urinary tract condition post implantation as "much better" or "very much better" and 72% (n = 13) of patients desire to continue using the surface-treated catheter over conventional latex or silicone catheters.

SUBMITTER: McVerry B

PROVIDER: S-EPMC9153982 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Readily Scalable, Clinically Demonstrated, Antibiofouling Zwitterionic Surface Treatment for Implantable Medical Devices.

McVerry Brian B Polasko Alexandra A Rao Ethan E Haghniaz Reihaneh R Chen Dayong D He Na N Ramos Pia P Hayashi Joel J Curson Paige P Wu Chueh-Yu CY Bandaru Praveen P Anderson Mackenzie M Bui Brandon B Sayegh Aref A Mahendra Shaily S Carlo Dino Di DD Kreydin Evgeniy E Khademhosseini Ali A Sheikhi Amir A Kaner Richard B RB

Advanced materials (Deerfield Beach, Fla.) 20220411 20

Unlike growth on tissue, microbes can grow freely on implantable devices with minimal immune system intervention and often form resilient biofilms that continuously pump out pathogenic cells. The efficacy of antibiotics used to treat infection is declining due to increased rates of pathogenic resistance. A simple, one-step zwitterionic surface modification is developed to significantly reduce protein and microbial adhesion to synthetic materials and demonstrate the successful modification of sev ...[more]

PMID: 35315553

Similar Datasets

Project description:ObjectiveClinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms.MethodsWe created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts.ResultsWe used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations.SignificanceiEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.

Dataset Information

A Readily Scalable, Clinically Demonstrated, Antibiofouling Zwitterionic Surface Treatment for Implantable Medical Devices.

Publications

A Readily Scalable, Clinically Demonstrated, Antibiofouling Zwitterionic Surface Treatment for Implantable Medical Devices.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets