{"title":"神经接口中新型印刷传感器的有机衬底:细胞相容性分析的测量方法","authors":"Sarah Tonello, G. Giorgi, S. Pisu, A. Cester","doi":"10.1109/MeMeA49120.2020.9137118","DOIUrl":null,"url":null,"abstract":"Advances in bioelectronics as interdisciplinary field combining electronics with novel materials for targeting biological monitoring have brought a strong acceleration in the development of sensors for neural interfaces. However, the success of any implantable device comes along with properties such as long-term stability and optimal cytocompatibility. Thus, the need to improve the reliability and reproducibility of quantitative methods for analyzing and comparing cells-substrate interaction of novel heterogeneous materials has become more and more compelling. To this aim, a systematization of the approach to analyze cytocompatibility assays appears as important as the standardization of the acquisition conditions themselves.. In this picture, the paper proposes a user-friendly toolbox able to improve the reliability of the analysis of fluorescence images from cultured organic materials. The first section, allowing a proper customization of several parameters, performs an optimal automatized segmentation of the image with an adaptive threshold strategy, evaluating cell parameters distribution on the substrate. The second one estimates the uncertainty in the evaluation of cell number and provides cell density maps at different scales. The comparison among different organic semiconductors demonstrated the possibility to use the software to compare the effect of different materials on cell parameters. Future works will address the correlation of those results from cell imaging with correspondent maps of the most peculiar properties of each cultured substrate.","PeriodicalId":152478,"journal":{"name":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Organic substrates for novel printed sensors in neural interfacing: a measurement method for cytocompatibility analysis\",\"authors\":\"Sarah Tonello, G. Giorgi, S. Pisu, A. Cester\",\"doi\":\"10.1109/MeMeA49120.2020.9137118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Advances in bioelectronics as interdisciplinary field combining electronics with novel materials for targeting biological monitoring have brought a strong acceleration in the development of sensors for neural interfaces. However, the success of any implantable device comes along with properties such as long-term stability and optimal cytocompatibility. Thus, the need to improve the reliability and reproducibility of quantitative methods for analyzing and comparing cells-substrate interaction of novel heterogeneous materials has become more and more compelling. To this aim, a systematization of the approach to analyze cytocompatibility assays appears as important as the standardization of the acquisition conditions themselves.. In this picture, the paper proposes a user-friendly toolbox able to improve the reliability of the analysis of fluorescence images from cultured organic materials. The first section, allowing a proper customization of several parameters, performs an optimal automatized segmentation of the image with an adaptive threshold strategy, evaluating cell parameters distribution on the substrate. The second one estimates the uncertainty in the evaluation of cell number and provides cell density maps at different scales. The comparison among different organic semiconductors demonstrated the possibility to use the software to compare the effect of different materials on cell parameters. Future works will address the correlation of those results from cell imaging with correspondent maps of the most peculiar properties of each cultured substrate.\",\"PeriodicalId\":152478,\"journal\":{\"name\":\"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MeMeA49120.2020.9137118\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MeMeA49120.2020.9137118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Organic substrates for novel printed sensors in neural interfacing: a measurement method for cytocompatibility analysis
Advances in bioelectronics as interdisciplinary field combining electronics with novel materials for targeting biological monitoring have brought a strong acceleration in the development of sensors for neural interfaces. However, the success of any implantable device comes along with properties such as long-term stability and optimal cytocompatibility. Thus, the need to improve the reliability and reproducibility of quantitative methods for analyzing and comparing cells-substrate interaction of novel heterogeneous materials has become more and more compelling. To this aim, a systematization of the approach to analyze cytocompatibility assays appears as important as the standardization of the acquisition conditions themselves.. In this picture, the paper proposes a user-friendly toolbox able to improve the reliability of the analysis of fluorescence images from cultured organic materials. The first section, allowing a proper customization of several parameters, performs an optimal automatized segmentation of the image with an adaptive threshold strategy, evaluating cell parameters distribution on the substrate. The second one estimates the uncertainty in the evaluation of cell number and provides cell density maps at different scales. The comparison among different organic semiconductors demonstrated the possibility to use the software to compare the effect of different materials on cell parameters. Future works will address the correlation of those results from cell imaging with correspondent maps of the most peculiar properties of each cultured substrate.