Multifunctional nanoparticles as a tissue adhesive and an injectable marker for image-guided procedures

Tissue adhesives have emerged as an alternative to sutures and staples for wound closure and reconnection of injured tissues after surgery or trauma. Owing to their convenience and effectiveness, these adhesives have received growing attention particularly in minimally invasive procedures. For safe and accurate applications, tissue adhesives should be detectable via clinical imaging modalities and be highly biocompatible for intracorporeal procedures. However, few adhesives meet all these requirements. Herein, we show that biocompatible tantalum oxide/silica core/shell nanoparticles (TSNs) exhibit not only high contrast effects for real-time imaging but also strong adhesive properties. Furthermore, the biocompatible TSNs cause much less cellular toxicity and less inflammation than a clinically used, imageable tissue adhesive (that is, a mixture of cyanoacrylate and Lipiodol). Because of their multifunctional imaging and adhesive property, the TSNs are successfully applied as a hemostatic adhesive for minimally invasive procedures and as an immobilized marker for image-guided procedures.

Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems

YesThe exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes.Supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904)

Surface design of magnetic nanoparticles for stimuli-responsive cancer imaging and therapy

Magnetic nanoparticles (MNPs) have been extensively studied for their potential applications to cancer diagnosis and treatment. However, various obstacles limit the use of nanoparticles for delivery in the tumor microenvironment. As a viable solution to such obstacles, advances in nanoparticle surface engineering augmented by a profound understanding of cancer physiology present new opportunities for MNP-based imaging and therapeutic agents. Stimuli-responsive ligands, rationally designed to interact with various physicochemical aspects, can improve the performance of MNPs in cancer-targeted imaging and therapy. In this review, we highlight recent progress in the design of MNP-based stimuli-responsive nanomaterials and their applications to cancer diagnosis and treatment. © 2017 Elsevier Ltd.343

Surface design of magnetic nanoparticles for stimuli-responsive cancer imaging and therapy

Magnetic nanoparticles (MNPs) have been extensively studied for their potential applications to cancer diagnosis and treatment. However, various obstacles limit the use of nanoparticles for delivery in the tumor microenvironment. As a viable solution to such obstacles, advances in nanoparticle surface engineering augmented by a profound understanding of cancer physiology present new opportunities for MNP-based imaging and therapeutic agents. Stimuli-responsive ligands, rationally designed to interact with various physicochemical aspects, can improve the performance of MNPs in cancer-targeted imaging and therapy. In this review, we highlight recent progress in the design of MNP-based stimuli-responsive nanomaterials and their applications to cancer diagnosis and treatment.

Enzyme-Based Glucose Sensor: From Invasive to Wearable Device

Blood glucose concentration is a key indicator of patients’ health, particularly for symptoms associated with diabetes mellitus. Because of the large number of diabetic patients, many approaches for glucose measurement have been studied to enable continuous and accurate glucose level monitoring. Among them, electrochemical analysis is prominent because it is simple and quantitative. This technology has been incorporated into commercialized and research-level devices from simple test strips to wearable devices and implantable systems. Although directly monitoring blood glucose assures accurate information, the invasive needle-pinching step to collect blood often results in patients (particularly young patients) being reluctant to adopt the process. An implantable glucose sensor may avoid the burden of repeated blood collections, but it is quite invasive and requires periodic replacement of the sensor owing to biofouling and its short lifetime. Therefore, noninvasive methods to estimate blood glucose levels from tears, saliva, interstitial fluid (ISF), and sweat are currently being studied. This review discusses the evolution of enzyme-based electrochemical glucose sensors, including materials, device structures, fabrication processes, and system engineering. Furthermore,invasive and noninvasive blood glucose monitoring methods using various biofluids or blood are described, highlighting the recent progress in the development of enzyme-based glucose sensors and their integrated systems. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Enzyme-based glucose sensor: from invasive to wearable device

Blood glucose concentration is a key indicator of patients' health, particularly for symptoms associated with diabetes mellitus. Because of the large number of diabetic patients, many approaches for glucose measurement have been studied to enable continuous and accurate glucose level monitoring. Among them, electrochemical analysis is prominent because it is simple and quantitative. This technology has been incorporated into commercialized and research-level devices from simple test strips to wearable devices and implantable systems. Although directly monitoring blood glucose assures accurate information, the invasive needle-pinching step to collect blood often results in patients (particularly young patients) being reluctant to adopt the process. An implantable glucose sensor may avoid the burden of repeated blood collections, but it is quite invasive and requires periodic replacement of the sensor owing to biofouling and its short lifetime. Therefore, noninvasive methods to estimate blood glucose levels from tears, saliva, interstitial fluid (ISF), and sweat are currently being studied. This review discusses the evolution of enzyme-based electrochemical glucose sensors, including materials, device structures, fabrication processes, and system engineering. Furthermore, invasive and noninvasive blood glucose monitoring methods using various biofluids or blood are described, highlighting the recent progress in the development of enzyme-based glucose sensors and their integrated systems.

Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework

Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters’ data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder to generate encoded sequences. The encoded sequences are decoded by another following LSTM-decoder to advance it to the final dense layer for energy prediction. The experimental results using different evaluation metrics show that the proposed hybrid model works well. Also, it records the smallest value for mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared to other state-of-the-art forecasting methods over the UCI residential building dataset. Furthermore, we conducted experiments on Korean commercial building data and the results indicate that our proposed hybrid model is a worthy contribution to energy forecasting

Device-assisted transdermal drug delivery

Transdermal drug delivery is a prospective drug delivery strategy to complement the limitations of conventional drug delivery systems including oral and injectable methods. This delivery route allows both convenient and painless drug delivery and a sustained release profile with reduced side effects. However, physiological barriers in the skin undermine the delivery efficiency of conventional patches, limiting drug candidates to small-molecules and lipophilic drugs. Recently, transdermal drug delivery technology has advanced from unsophisticated methods simply relying on natural diffusion to drug releasing systems that dynamically respond to external stimuli. Furthermore, physical barriers in the skin have been overcome using microneedles, and controlled delivery by wearable biosensors has been enabled ultimately. In this review, we classify the evolution of advanced drug delivery strategies based on generations and provide a comprehensive overview. Finally, the recent progress in advanced diagnosis and therapy through customized drug delivery systems based on real-time analysis of physiological cues is highlighted. © 2017 Elsevier B.