It is well known that water inside hydrophobic nano-channels diffuses faster than bulk water. Recent theoretical studies have shown that this enhancement depends on the size of the hydrophobic nanochannels. However, experimental evidence of this dependence is lacking. Here, by combining two-dimensional nuclear magnetic resonance diffusion–relaxation (D–T2eff) spectroscopy in the stray field of a superconducting magnet and molecular dynamics simulations, we analyze the size dependence of water dynamics inside Carbon Nanotubes (CNTs) of different diameters (1.1–6.0 nm), in the temperature range of 265–305 K. Depending on the CNT diameter, the nanotube water is shown to resolve in two or more tubular components acquiring different self-diffusion coefficients. Most notably, a favorable CNT diameter range (3.0–4.5 nm) is experimentally verified for the first time, in which water molecule dynamics at the center of the CNTs exhibits distinctly non-Arrhenius behavior, characterized by ultrafast diffusion and extraordinary fragility, a result of significant importance in the efforts to understand water behavior in hydrophobic nanochannels.

Cross-linked polyethylene (XLPE) and silicone rubber (SiR) samples were subjected to a high-voltage AC stress plane-plane configuration and inclined plane test, respectively. The voltage was applied such that discharge was observed across the surface of the XLPE test sample for several hours and for visible damage to occur on SiR samples also after several hours. Selected stressed samples together with virgin samples from the same manufactured batch were tested using nuclear magnetic resonance (NMR) spectroscopy. Specifically, 1 H NMR spin-lattice (T 1) and spin-spin (T 2) relaxation time measurements were employed to examine potential changes in the chemical bonding of undamaged and damaged XLPE and SiR samples. Preliminary results show that there may be a moderate increase in the T 1 and T 2 values of the damaged samples in comparison with the undamaged ones. This raises the possibility that NMR can be a useful additional experimental tool in characterising material degradation.

IntroductionElectronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research.ObjectivesThis study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism.MethodsThe MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k.ResultsThe average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected.ConclusionThe experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.

Artificial Intelligence (AI) has shown the ability to enhance the accuracy and efficiency of physicians. ChatGPT is an AI chatbot that can interact with humans through text, over the internet. It is trained with machine learning algorithms, using large datasets. In this study, we compare the performance of using a ChatGPT API 3.5 Turbo model to a general model, in assisting urologists in obtaining accurate, valid medical information. The API was accessed through a Python script that was applied specifically for this study based on 2023 EAU guidelines in PDF format. This custom-trained model leads to providing doctors with more precise, prompt answers about specific urologic subjects, thus helping them, ultimately, providing better patient care.

Association rule mining is a very popular unsupervised machine learning technique for discovering patterns in large datasets. Patients with stone disease commonly suffer from urinary tract infections (UTI), complicated by the emergence of antimicrobial resistance (AMR), due to the excessive use of antibiotics. In this study, we explore the use of association rule mining in the AMR profile of patients suffering from stone disease.