Abstract
Light interaction with biological materials depends on the material’s optical properties. From those properties, the absorption and scattering coefficients are the most important, since they quantify how much of a light beam is attenuated when traveling inside a tissue. The scattering coefficient is known to be significantly higher than the absorption coefficient in biological materials, meaning that most of the light is scattered, turning optical methods in clinical practice limited. Such difference between the scattering and absorption coefficients is mainly due to a refractive index mismatch between tissue components and fluids. We explain this concept in the present chapter before introducing the technique that efficiently minimizes this effect in the following chapters. © 2019, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
With the growing research in the field of optical clearing and the various applications of this technique that have been recently developed, more than 1000 agents have been tested in various tissues in the past two decades to evaluate their clearing potential. To optimize the clearing treatments, knowledge on the dispersions and absorption spectra of the agents is necessary. We have gathered experimental and literature data to show that the absorption bands of typical clearing agents are located in the deep ultraviolet range, where the refractive index is significantly high. The desired characteristics for the clearing agents are presented, and their classification in three major groups is indicated. Solutions containing mixtures of optical clearing agents (OCAs) and diluted solutions are also important for certain applications, such as the enhancement of agent delivery or the evaluation of agent diffusion properties. Such applications are referred, and some examples are presented. A simple method to prepare diluted solutions of clearing agents is also described. © 2019, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In this chapter, we will describe methods based on simple measurements that allow one to acquire information of diverse nature. Regarding the characterization of optical clearing treatments and evaluation of their efficiency, we discuss a method to obtain the refractive index kinetics of the interstitial ground medium and another method to obtain the kinetics of the scattering properties of a tissue under study. The evaluation of the diffusion properties for the optical clearing agents and water involved in the fluxes between the tissue and the treating solution is also important. To obtain these properties, we describe in Sect. 6.4 a simple ex vivo method, which from collimated transmittance and thickness measurements allows one to estimate the diffusion time and the diffusion coefficient of these fluids. Such method can be used as a complementary diagnostic tool, since it allows also for discrimination between normal and pathological tissues. Also with the objective of obtaining physiological or pathological information from tissues, we describe in Sect. 6.5 the discovery of two new optical clearing windows in the ultraviolet range, which may turn possible the development of new diagnostic or treatment methodologies. © 2019, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In this chapter, other areas of application for optical clearing agents (OCAs) are presented. The osmotic properties of agents are highly important in dermatology, cosmetics, and pharmacology, if topical application to the skin is desired. After addressing this application in Sect. 8.2, tissue poisoning and discussing the osmotic properties of certain poisons or toxic compounds will be done in Sect. 8.3. The importance of evaluating the diffusion properties of those substances in the skin, eye, and other inner tissue is indicated as a tool for optimizing treatment or decontamination dosage and procedures. Section 8.4 is used to discuss the application of agents in food industry. The dehydration capabilities of certain agents, such as sodium chloride or glycerol, are presented, and the advantages of treating fruit, meat, or fish with sugars to improve their organoleptic properties during preservation are also presented. Finally, the application of OCAs for tissue or organ preservation is presented in Sect. 8.5, where some cases for preservation of eye tissues at room temperature made with glycerol will be discussed. The use of OCAs as cryoprotectants at low temperatures is also explained. In all these applications, we refer the applicability of the method described in Sect. 6.4 to evaluate the diffusion properties of water, poisons, or drugs for ex vivo tissue samples. © 2019, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
To overcome the high light-scattering problem that occurs in biological tissues, we present in this chapter the different clearing methods known today. Most of these methods have benefits and downsides, depending on the application for which they are used. The optical immersion method is introduced as a better, reliable, and reversible way to turn tissues clear. The major benefits and advantages of this method such as its reversibility, the lack of side effects, and application in large wavelength range will be presented. A description of the molecular diffusion of optical clearing agents is given to explain the reduction in the refractive index mismatch that natural tissues have. © 2019, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.