Microfluidically fabricated pH-responsive anionic amphiphilic microgels for drug release

© 2016 The Royal Society of Chemistry. Amphiphilic microgels of different composition based on the hydrophilic, pH-responsive acrylic acid (AA) and the hydrophobic, non-ionic n-butyl acrylate (BuA) were synthesised using a lab-on-a-chip device. Hydrophobic droplets were generated via a microfluidic platform that contained a protected form of AA, BuA, the hydrophobic crosslinker, ethylene glycol dimethacrylate (EGDMA), and a free radical initiator in an organic solvent. These hydrophobic droplets were photopolymerised within the microfluidic channels and subsequently hydrolysed, enabling an integrated platform for the rapid, automated, and in situ production of anionic amphiphilic microgels. The amphiphilic microgels did not feature the conventional core-shell structure but were instead based on random amphiphilic copolymers of AA and BuA and hydrophobic crosslinks. Due to their amphiphilic nature they were able to encapsulate and deliver both hydrophobic and hydrophilic moieties. The model drug delivery and the swelling ability of the microgels were influenced by the pH of the surrounding aqueous solution and the hydrophobic content of the microgels

Tailoring pH-responsive acrylic acid microgels with hydrophobic crosslinks for drug release

Amphiphilic microgels based on the hydrophilic acrylic acid (AA) and hydrophobic crosslinks of different compositions were synthesised using a lab-on-a-chip device. The microgels were formed by polymerising hydrophobic droplets. The droplets were generated via a microfluidic platform and contained a protected form of AA, a hydrophobic crosslinker (ethylene glycol dimethacrylate, EGDMA) and a free radical initiator in an organic solvent. Following photopolymerisation and subsequent hydrolysis, AA based microgels of amphiphilic nature were produced and it was demonstrated that they can successfully deliver both hydrophilic as well as hydrophobic moieties. The model drug delivery and the swelling ability of the microgels were influenced by the pH of the aqueous solution as well as the crosslinking density and hydrophobic content of the microgels

Urban encounters: juxtapositions of difference and the communicative interface of global cities

This article explores the communicative interface of global cities, especially as it is shaped in the juxtapositions of difference in culturally diverse urban neighbourhoods. These urban zones present powerful examples, where different groups live cheek by jowl, in close proximity and in intimate interaction — desired or unavoidable. In these urban locations, the need to manage difference is synonymous to making them liveable and one's own. In seeking (and sometimes finding) a location in the city and a location in the world, urban dwellers shape their communication practices as forms of everyday, mundane and bottom-up tactics for the management of diversity. The article looks at three particular areas where cultural diversity and urban communication practices come together into meaningful political and cultural relations for a sustainable cosmopolitan life: citizenship, imagination and identity

Single-layer-coated surfaces with linearized reflectance versus angle of incidence: application to passive and active silicon rotation sensors

A transparent or absorbing substrate can be coated with a transparent thin film to produce a linear reflectanceversus- angle-of-incidence response over a certain range of angles. Linearization at and near normal incidence is a special case that leads to a maximally flat response for p-polarized, s-polarized, or unpolarized light. For midrange and high-range linearization with moderate and high slopes, respectively, the best results are obtained when the incident light is s polarized. Application to a Si substrate that is coated with a SiO2 film leads to novel passive and active reflection rotation sensors. Experimental results and an error analysis of this rotation sensor are presented

Single-layer-coated surfaces with linearized reflectance versus angle of incidence: application to passive and active silicon rotation sensors

A transparent or absorbing substrate can be coated with a transparent thin film to produce a linear reflectanceversus- angle-of-incidence response over a certain range of angles. Linearization at and near normal incidence is a special case that leads to a maximally flat response for p-polarized, s-polarized, or unpolarized light. For midrange and high-range linearization with moderate and high slopes, respectively, the best results are obtained when the incident light is s polarized. Application to a Si substrate that is coated with a SiO2 film leads to novel passive and active reflection rotation sensors. Experimental results and an error analysis of this rotation sensor are presented

Edge-Based Temporal Fusion Transformer for Multi-Horizon Blood Glucose Prediction

Deep learning models have achieved the state of the art in blood glucose (BG) prediction, which has been shown to improve type 1 diabetes (T1D) management. However, most existing models can only provide single-horizon prediction and face a variety of real-world challenges, such as lacking hardware implementation and interpretability. In this work, we introduce a new deep learning framework, the edge-based temporal fusion Transformer (E-TFT), for multi-horizon BG prediction, and implement the trained model on a customized wristband with a system on a chip (Nordic nRF52832) for edge computing. E-TFT employs a self-attention mechanism to extract long-term temporal dependencies and enables post-hoc explanation for feature selection. On a clinical dataset with 12 T1D subjects, it achieved a mean root mean square error of 19.09 ± 2.47 mg/dL and 32.31 ± 3.79 mg/dL for 30 and 60-minute prediction horizons, respectively, and outperformed all the considered baseline methods, such as N-BEATS and N-HiTS. The proposed model is effective for multi-horizon BG prediction and can be deployed on wearable devices to enhance T1D management in clinical settings

Dilated Recurrent Neural Networks for Glucose Forecasting in Type 1 Diabetes

Diabetes is a chronic disease affecting 415 million people worldwide. People with type 1 diabetes mellitus (T1DM) need to self-administer insulin to maintain blood glucose (BG) levels in a normal range, which is usually a very challenging task. Developing a reliable glucose forecasting model would have a profound impact on diabetes management, since it could provide predictive glucose alarms or insulin suspension at low-glucose for hypoglycemia minimisation. Recently, deep learning has shown great potential in healthcare and medical research for diagnosis, forecasting and decision-making. In this work, we introduce a deep learning model based on a dilated recurrent neural network (DRNN) to provide 30-min forecasts of future glucose levels. Using dilation, the DRNN model gains a much larger receptive field in terms of neurons aiming at capturing long-term dependencies. A transfer learning technique is also applied to make use of the data from multiple subjects. The proposed approach outperforms existing glucose forecasting algorithms, including autoregressive models (ARX), support vector regression (SVR) and conventional neural networks for predicting glucose (NNPG) (e.g. RMSE = NNPG, 22.9 mg/dL; SVR, 21.7 mg/dL; ARX, 20.1 mg/dl; DRNN, 18.9 mg/dL on the OhioT1DM dataset). The results suggest that dilated connections can improve glucose forecasting performance efficiently

Blood glucose prediction for type 1 diabetes using generative adversarial networks

Maintaining blood glucose in a target range is essential for people living with Type 1 diabetes in order to avoid excessive periods in hypoglycemia and hyperglycemia which can result in severe complications. Accurate blood glucose prediction can reduce this risk and enhance early interventions to improve diabetes management. However, due to the complex nature of glucose metabolism and the various lifestyle related factors which can disrupt this, diabetes management still remains challenging. In this work we propose a novel deep learning model to predict future BG levels based on the historical continuous glucose monitoring measurements, meal ingestion, and insulin delivery. We adopt a modified architecture of the generative adversarial network that comprises of a generator and a discriminator. The generator computes the BG predictions by a recurrent neural network with gated recurrent units, and the auxiliary discriminator employs a one-dimensional convolutional neural network to distinguish between the predictive and real BG values. Two modules are trained in an adversarial process with a combination of loss. The experiments were conducted using the OhioT1DM dataset that contains the data of six T1D contributors over 40 days. The proposed algorithm achieves an average root mean square error (RMSE) of 18.34 ± 0.17 mg/dL with a mean absolute error (MAE) of 13.37 ± 0.18 mg/dL for the 30-minute prediction horizon (PH) and an average RMSE of 32.31 ± 0.46 mg/dL with a MAE of 24.20 ± 0.42 for the 60-minute PH. The results are compared for clinical relevance using the Clarke error grid which confirms the promising performance of the proposed model

Blood Glucose Prediction in Type 1 Diabetes Using Deep Learning on the Edge

Real-time blood glucose (BG) prediction can enhance decision support systems for insulin dosing such as bolus calculators and closed-loop systems for insulin delivery. Deep learning has been proven to achieve state-of-the-art performance in BG prediction. However, it is usually seen as a very computationally expensive approach, hence difficult to implement in wearable medical devices such as transmitters in continuous glucose monitoring (CGM) systems. In this work, we introduce a novel deep learning framework to predict BG levels with the edge inference on a microcontroller unit embedded in a low- power system. By using glucose measurements from a CGM sensor and a recurrent neural network that builds on long-short term memory, the personalized models achieves state-of-the-art performance on a clinical data set obtained from 12 subjects with T1D. In particular, the proposed method achieves an average root mean square error of 19.10 ± 2.04 for a 30-minute prediction horizon (PH) and 32.61 ± 3.45 for a 60-minute PH with high clinical accuracy. Notably, the framework has been optimized to achieve a minimum use of hardware resources (34KB FLASH and 1KB SRAM) as well as an execution time of 22 ms for low power operations (8 μW). The presented system has the potential to be implemented in wearable medical devices for diabetes care (CGM and insulin pumps) and to be integrated within an Internet of Things platform

IoMT-Enabled Real-time Blood Glucose Prediction with Deep Learning and Edge Computing

Blood glucose (BG) prediction is essential to the success of glycemic control in type 1 diabetes (T1D) management. Empowered by the recent development of the Internet of Medical Things (IoMT), continuous glucose monitoring (CGM) and deep learning technologies have been demonstrated to achieve the state of the art in BG prediction. However, it is challenging to implement such algorithms in actual clinical settings to provide persistent decision support due to the high demand for computational resources, while smartphone-based implementations are limited by short battery life and require users to carry the device. In this work, we propose a new deep learning model using an attention-based evidential recurrent neural network and design an IoMT-enabled wearable device to implement the embedded model, which comprises a low-cost and low-power system on a chip to perform Bluetooth connectivity and edge computing for real-time BG prediction and predictive hypoglycemia detection. In addition, we developed a smartphone app to visualize BG trajectories and predictions, and desktop and cloud platforms to backup data and fine-tune models. The embedded model was evaluated on three clinical datasets including 47 T1D subjects. The proposed model achieved superior performance of root mean square error (RMSE), mean absolute error, and glucose-specific RMSE, and obtained the best accuracy for hypoglycemia detection when compared with a group of machine learning baseline methods. Moreover, we performed hardware-in-the-loop in silico trials with 10 virtual T1D adults to test the whole IoMT system with predictive low-glucose management, which significantly reduced hypoglycemia and improved BG control