RESUMEN
BACKGROUND: Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals. METHODS: An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values. RESULTS: Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones. CONCLUSIONS: These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.
RESUMEN
Simultaneous calculation of entropies, enthalpies and free energies has been a long-standing challenge in computational chemistry, partly because of the difficulty in obtaining estimates of all three properties from a single consistent simulation methodology. This has been particularly true for methods from the Integral Equation Theory of Molecular Liquids such as the Reference Interaction Site Model which have traditionally given large errors in solvation thermodynamics. Recently, we presented pyRISM-CNN, a combination of the 1 Dimensional Reference Interaction Site Model (1D-RISM) solver, pyRISM, with a deep learning based free energy functional, as a method of predicting solvation free energy (SFE). With this approach, a 40-fold improvement in prediction accuracy was delivered for a multi-solvent, multi-temperature dataset when compared to the standard 1D-RISM theory [Fowles et al., Digital Discovery, 2023, 2, 177-188]. Here, we report three further developments to the pyRISM-CNN methodology. Firstly, solvation free energies have been introduced for organic molecular ions in methanol or water solvent systems at 298 K, with errors below 4 kcal mol-1 obtained without the need for corrections or additional descriptors. Secondly, the number of solvents in the training data has been expanded from carbon tetrachloride, water and chloroform to now also include methanol. For neutral solutes, prediction errors nearing or below 1 kcal mol-1 are obtained for each organic solvent system at 298 K and water solvent systems at 273-373 K. Lastly, pyRISM-CNN was successfully applied to the simultaneous prediction of solvation enthalpy, entropy and free energy through a multi-task learning approach, with errors of 1.04, 0.98 and 0.47 kcal mol-1, respectively, for water solvent systems at 298 K.
RESUMEN
We demonstrate that physics-based calculations of intrinsic aqueous solubility can rival cheminformatics-based machine learning predictions. A proof-of-concept was developed for a physics-based approach via a sublimation thermodynamic cycle, building upon previous work that relied upon several thermodynamic approximations, notably the 2RT approximation, and limited conformational sampling. Here, we apply improvements to our sublimation free-energy model with the use of crystal phonon mode calculations to capture the contributions of the vibrational modes of the crystal. Including these improvements with lattice energies computed using the model-potential-based Ψmol method leads to accurate estimates of sublimation free energy. Combining these with hydration free energies obtained from either molecular dynamics free-energy perturbation simulations or density functional theory calculations, solubilities comparable to both experiment and informatics predictions are obtained. The application to coronene, succinic acid, and the pharmaceutical desloratadine shows how the methods must be adapted for the adoption of different conformations in different phases. The approach has the flexibility to extend to applications that cannot be covered by informatics methods.
