Study of the collagen tissue nanostructure by analyzing the echo decay obtained using the MRI technique.

The multicomponent relaxation observed in nuclear magnetic resonance experiments in biological tissues makes it difficult to establish a correlation between specific relaxation times and tissue structural parameters. The analysis of a nanostructure (the characteristic size of 10-1000 nm) is usually based on formulas for relaxation times which depend on structural parameters at the atomic or molecular levels in the size range of 0.1-5 nm. We have recently developed an analysis method in which relaxation times' anisotropy in a sample is explicitly related to its structure of nanocavities containing a liquid or gas. However, the method is based on the analysis of experimental data on the anisotropy of relaxation times obtained by using the standard NMR technique and rotating the sample relative to a magnetic field and requires a series of experiments. In the present study, to address this challenge, we develop a new method of analysis of a multi-exponential magnetic resonance signal that does not require determining relaxation times and eliminates the sample rotation and the necessity of a series of experiments. Using the magnetic resonance imaging (MRI) technique, the total signal from the whole sample was obtained as a sum of the signals (echo decays) from all voxels. In contrast to previous research, the volumes of nanocavities and their angular distribution can be obtained by analyzing a single total signal for the entire cartilage. In addition, within the framework of this approach, it is possible to identify the reason for the multicomponent nature of relaxation. The proposed method for analyzing a single multi-exponential signal (transverse relaxation) was implemented on cartilage. Using the signal, three anatomical zones of cartilage were studied, revealing significant structural differences between them. The proposed method not only avoids the need for sample rotation but also enables repeated application of layer-by-layer magnetic resonance imaging with micron resolution. The study results allow us to suggest that water molecules contributing to the echo decay are more likely located in nanocavities formed by the fibrillar structure rather than inside the fibrils.

Application of deep learning for bronchial asthma diagnostics using respiratory sound recordings.

Methods of computer-assisted diagnostics that utilize deep learning techniques on recordings of respiratory sounds have been developed to diagnose bronchial asthma. In the course of the study an anonymous database containing audio files of respiratory sound recordings of patients suffering from different respiratory diseases and healthy volunteers has been accumulated and used to train the software and control its operation. The database consists of 1,238 records of respiratory sounds of patients and 133 records of volunteers. The age of tested persons was from 18 months to 47 years. The sound recordings were captured during calm breathing at four points: in the oral cavity, above the trachea, at the chest, the second intercostal space on the right side, and at the point on the back. The developed software provides binary classifications (diagnostics) of the type: "sick/healthy" and "asthmatic patient/non-asthmatic patient and healthy". For small test samples of 50 (control group) to 50 records (comparison group), the diagnostic sensitivity metric of the first classifier was 88%, its specificity metric -86% and accuracy metric -87%. The metrics for the classifier "asthmatic patient/non-asthmatic patient and healthy" were 92%, 82%, and 87%, respectively. The last model applied to analyze 941 records in asthmatic patients indicated the correct asthma diagnosis in 93% of cases. The proposed method is distinguished by the fact that the trained model enables diagnostics of bronchial asthma (including differential diagnostics) with high accuracy irrespective of the patient gender and age, stage of the disease, as well as the point of sound recording. The proposed method can be used as an additional screening method for preclinical bronchial asthma diagnostics and serve as a basis for developing methods of computer assisted patient condition monitoring including remote monitoring and real-time estimation of treatment effectiveness.

Multicomponents of spin-spin relaxation, anisotropy of the echo decay, and nanoporous sample structure.

We have experimentally and theoretically investigated multicomponent 1H nuclear magnetic resonance (NMR) echo decays in a-Si:H films containing anisotropic nanopores, in which randomly moving hydrogen molecules are entrapped. The experimental results are interpreted within the framework of the previously developed theory, in which a nanoporous material is represented as a set of nanopores containing liquid or gas, and the relaxation rate is determined by the dipole-dipole spin interaction, considering the restricted motion of molecules inside the pores. Previously, such characteristics of a nanostructure as the average volume of pores and their orientation distribution were determined from the angular dependences of the spin-spin and spin-lattice relaxation times. We propose a new approach to the analysis of the NMR signal, the main advantage of which is the possibility of obtaining nanostructure parameters from a single decay of the echo signal. In this case, there is no need to analyze the anisotropy of the relaxation time T2, the determination of which is a rather complicated problem in multicomponent decays. Despite multicomponent signals, the fitting parameter associated with the size and shape of nanopores is determined quite accurately. This made it possible to determine the size and shape of nanopores in a-Si:H films, herewith our estimates are in good agreement with the results obtained by other methods. The fitting of the decays also provides information about the nanostructure of the sample, such as the standard deviations of the angular distribution of pores and the polar and azimuthal angles of the average direction of the pore axes relative to the sample axis, with reasonable accuracy. The approach makes it possible to quantitatively determine the parameters of the non-spherical nanoporous structure from NMR data in a non-destructive manner.