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Effects of Terminal Sterilization on PEG-Based Bioresorbable Polymers Used in Biomedical Applications.

ABSTRACT: The effects of ethylene oxide (EO), vaporized hydrogen peroxide (VHP), gamma (?) radiation, and electron-beam (E-beam) on the physiochemical and morphological properties of medical device polymers are investigated. Polymers with ether, carbonate, carboxylic acid, amide and ester functionalities are selected from a family of poly(ethylene glycol) (PEG) containing tyrosine-derived polycarbonates (TyrPCs) to include slow, medium, fast, and ultrafast degrading polymers. Poly(lactic acid) (PLA) is used for comparison. Molecular weight (Mw) of all tested polymers decreases upon gamma and E-beam, and this effect becomes more pronounced at higher PEG content. Gamma sterilization increases the glass transition temperature of polymers with high PEG content. EO esterifies the carboxylic acid groups in desaminotyrosol-tyrosine (DT) and causes significant degradation. VHP causes hydroxylation of the phenyl ring, and hydrolytic degradation. This study signifies the importance of the chemical composition when selecting a sterilization method, and provides suggested conditions for each of the sterilization methods.

SUBMITTER: Bhatnagar D

PROVIDER: S-EPMC5340269 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Effects of Terminal Sterilization on PEG-Based Bioresorbable Polymers Used in Biomedical Applications.

Bhatnagar Divya D Dube Koustubh K Damodaran Vinod B VB Subramanian Ganesan G Aston Kenneth K Halperin Frederick F Mao Meiyu M Pricer Kurt K Murthy N Sanjeeva NS Kohn Joachim J

Macromolecular materials and engineering 20160711 10

The effects of ethylene oxide (EO), vaporized hydrogen peroxide (VHP), gamma (γ) radiation, and electron-beam (E-beam) on the physiochemical and morphological properties of medical device polymers are investigated. Polymers with ether, carbonate, carboxylic acid, amide and ester functionalities are selected from a family of poly(ethylene glycol) (PEG) containing tyrosine-derived polycarbonates (TyrPCs) to include slow, medium, fast, and ultrafast degrading polymers. Poly(lactic acid) (PLA) is us ...[more]

PMID: 28280451

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Project description:Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.

Dataset Information

Effects of Terminal Sterilization on PEG-Based Bioresorbable Polymers Used in Biomedical Applications.

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Effects of Terminal Sterilization on PEG-Based Bioresorbable Polymers Used in Biomedical Applications.

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