Abstract
The optical immersion clearing technique has been successfully applied through the last 30 years in the visible to near infrared spectral range, and has proven to be a promising method to promote the application of optical technologies in clinical practice. To investigate its potential in the ultraviolet range, collimated transmittance spectra from 200 to 1000 nm were measured from colorectal muscle samples under treatment with glycerol-water solutions. The treatments created two new optical windows with transmittance efficiency peaks at 230 and 300 nm, with magnitude increasing with glycerol concentration in the treating solution. Such discovery opens the opportunity to develop clinical procedures to perform diagnosis or treatments in the ultraviolet.

Abstract
A robust method is presented for evaluating the diffusion properties of chemicals in ex vivo biological tissues. Using this method that relies only on thickness and collimated transmittance measurements, the diffusion properties of glycerol, fructose, polypropylene glycol and water in muscle tissues were evaluated. Amongst other results, the diffusion coefficient of glycerol in colorectal muscle was estimated with a value of 3.3 x 10(-7) cm(2)/s. Due to the robustness and simplicity of the method, it can be used in other fields of biomedical engineering, namely in organ cryoprotection and food industry.

Abstract
In this paper, we describe a simple and indirect method to evaluate the kinetics of the optical properties for biological tissues under optical clearing treatments. We use the theoretical formalism in this method to process experimental data obtained from colorectal muscle samples to evaluate and characterize the dehydration and refractive index matching mechanisms.

Abstract
This study analyzed the accuracy of a BGL predictive model (BGL-PM) for type 1 diabetes mellitus patients (T1DM) in a real-world environment. The study population consisted of 10 individuals with T1DM, half of them were female, age 33 (SD:11.2), BMI of 26.1 (4.2) and 60% were under carbohydrate-count treatment. After consent, patients underwent a medical evaluation and registered their daily activities using a smartphone application (GlucoTrends) for 28 days, with BGL and heart rate continuously monitored. BGL-PM was developed using a Deep Learning architecture, based on Recurrent Neural Networks. Models were trained for each patient using different training sets sizes (7, 14, 21 days). Prediction accuracy was evaluated by Mean Absolute Percentage Error (MAPE) on the last 5 days for different Prediction Horizons (PH): 30, 60, 120, 180 and 360 minutes, comparing full day and nocturnal period. The model predicted BGL with relevant accuracy for the dataset with 21 training days up to 60 minutes in both periods: full day (median MAPE 22.5%) and nocturnal (14.3%) (Figure). The BGL-PM was able to provide useful BGL predictions, especially during the night period, which can be improved by increasing the training period. Consequently, this BGL-PM poses as a complementary tool for the prevention of acute complications such as hypoglycemia and hyperglycemia in the management of DM. Disclosure M. Foss-Freitas: None. G.S. Moreira: Stock/Shareholder; Self; GlucoGear Tecnologia. V.P. Antloga: Stock/Shareholder; Self; GlucoGear Tecnologia. C.R. Neto: Research Support; Self; University of Sao Paulo. E.M. Rodrigues: Consultant; Self; GlucoGear Tecnologia. M.F. da Costa: Research Support; Self; GlucoGear. A.P. dos Santos: None. Y.K. Matsumoto: Board Member; Self; GlucoGear. Stock/Shareholder; Self; GlucoGear. Other Relationship; Self; GlucoGear.

Abstract
Smart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the network edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart healthcare systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make autonomous decisions for smart health applications. In particular, we present and implement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low computational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient's conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%.

Abstract
Improving the efficiency of healthcare systems is a top national interest worldwide. However, the need to deliver scalable healthcare services to patients while reducing costs is a challenging issue. Among the most promising approaches for enabling smart healthcare (s-health) are edge-computing capabilities and next-generation wireless networking technologies that can provide real-time and cost-effective patient remote monitoring. In this article, we present our vision of exploiting MEC for s-health applications. We envision a MEC-based architecture and discuss the benefits that it can bring to realize in-network and context-aware processing so that the s-health requirements are met. We then present two main functionalities that can be implemented leveraging such an architecture to provide efficient data delivery, namely, multimodal data compression and edge-based feature extraction for event detection. The former allows efficient and low distortion compression, while the latter ensures high-reliability and fast response in case of emergency applications. Finally, we discuss the main challenges and opportunities that edge computing could provide and possible directions for future research.