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Subject: biophotonics ×

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    Deep learning a boon for biophotonics
    (Weinheim : Wiley-VCH-Verl., 2020) Pradhan, Pranita; Guo, Shuxia; Ryabchykov, Oleg; Popp, Juergen; Bocklitz, Thomas W.
    This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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    Printed Degradable Optical Waveguides for Guiding Light into Tissue
    (Weinheim : Wiley-VCH, 2020) Feng, Jun; Zheng, Yijun; Bhusari, Shardul; Villiou, Maria; Pearson, Samuel; del Campo, Aránzazu
    Optogenetics and photonic technologies are changing the future of medicine. To implement light‐based therapies in the clinic, patient‐friendly devices that can deliver light inside the body while offering tunable properties and compatibility with soft tissues are needed. Here extrusion printing of degradable, hydrogel‐based optical waveguides with optical losses as low as 0.1 dB cm−1 at visible wavelengths is described. Core‐only and core‐cladding fibers are printed at room temperature from polyethylene glycol (PEG)‐based and PEG/Pluronic precursors, and cured by in situ photopolymerization. The obtained waveguides are flexible, with mechanical properties tunable within a tissue‐compatible range. Degradation times are also tunable by adjusting the molar mass of the diacrylate gel precursors, which are synthesized by linking PEG diacrylate (PEGDA) with varying proportions of DL‐dithiothreitol (DTT). The printed waveguides are used to activate photochemical and optogenetic processes in close‐to‐physiological environments. Light‐triggered migration of cells in a photoresponsive 3D hydrogel and drug release from an optogenetically‐engineered living material by delivering light across >5 cm of muscle tissue are demonstrated. These results quantify the in vitro performance, and reflect the potential of the printed degradable fibers for in vivo and clinical applications.