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- ItemPhoto-Cross-Linked Dual-Responsive Hollow Capsules Mimicking Cell Membrane for Controllable Cargo Post-Encapsulation and Release(Weinheim : Wiley-VCH, 2016) Liu, Xiaoling; Appelhans, Dietmar; Wei, Qiang; Voit, BrigitteMultifunctional and responsive hollow capsules are ideal candidates to establish highly sophisticated compartments mimicking cell membranes for controllable bio-inspired functions. For this purpose pH and temperature dual-responsive and photo-cross-linked hollow capsules, based on silica-templated layer-by-layer approach by using poly(N-isopropyl acrylamide)-blockpolymethacrylate) and polyallylamine, have been prepared to use them for the subsequent and easily available post-encapsulation process of proteinlike macromolecules at room temperature and pH 7.4 and their controllable release triggered by stimuli. The uptake and release properties of the hollow capsules for cargos are highly affected by changes in the external stimuli temperature (25, 37, or 45 °C) and internal stimuli pH of the phosphate-containing buffer solution (5.5 or 7.4), by the degree of photo-cross-linking, and the size of cargo. The photo-cross-linked and dual stimuli-responsive hollow capsules with different membrane permeability can be considered as attractive material for mimicking cell functions triggered by controllable uptake and release of different up to 11 nm sized biomolecules.
- ItemNeuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zarrin, Pouya Soltani; Zahari, Finn; Mahadevaiah, Mamathamba K.; Perez, Eduardo; Kohlstedt, Hermann; Wenger, ChristianChronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. © 2020, The Author(s).