Water Dynamics in Dextran-Based Hydrogel Micro/Nanoparticles Studied by NMR Diffusometry and Relaxometry.

Water dynamics in mesoporous dextran hydrogel micro/nanoparticles was investigated by means of nuclear magnetic resonance (NMR) techniques. High-resolution 1H NMR spectra and pulsed field gradient (PFG) NMR diffusometry measurements obtained on swollen state dextran micro/nanogel revealed the existence of different fractions of water molecules based on their interaction with the gel matrix. In addition to the translational diffusion of bulk water, two more diffusion processes characterized with self-diffusion coefficients 1 and 2 orders of magnitude smaller than that of bulk water were identified. 1H spin-lattice relaxation dispersion profiles obtained for a broad range of Larmor frequencies using fast field cycling (FFC) and conventional NMR relaxometry techniques allowed us to further clarify the mechanisms of molecular motion. According to the water proton pool fractions and associated self-diffusion coefficients, it is shown that the relaxation contribution associated with reorientation-mediated translational motions (RMTDs) dominates the relaxation dispersion observed at intermediate frequencies. At very low frequencies, the spin-lattice relaxation rate is dominated by the slow solid-gel dynamics probed by the water molecules interacting with the pores' surface hydroxyl groups due to the rapid chemical exchange between surface hydroxyl groups and free water. The correlation time for the thumbling-like motion of the dextran gel was found to be in the submillisecond range. The values of the self-diffusion and coherence lengths associated with motion of water molecules interacting with the solid-gel particles are consistent with the particle size and pore size distributions obtained for the studied dextran gels.

A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics.

Compartmental epidemiological models are, by far, the most popular in the study of dynamics related with infectious diseases. It is, therefore, not surprising that they are frequently used to study the current COVID-19 pandemic. Taking advantage of the real-time availability of COVID-19 related data, we perform a compartmental model fitting analysis of the portuguese case, using an online open-access platform with the integrated capability of solving systems of differential equations. This analysis enabled the data-driven validation of the used model and was the basis for robust projections of different future scenarios, namely, increasing the detected infected population, reopening schools at different moments, allowing Easter celebrations to take place and population vaccination. The method presented in this work can easily be used to perform the non-trivial task of simultaneously fitting differential equation solutions to different epidemiological data sets, regardless of the model or country that might be considered in the analysis.