Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net.

The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are of great benefit for treating pulmonary edema, they should not be used to treat COVID-19 as they carry the risk of several adverse consequences, including worsening the matching of ventilation and perfusion, impaired carbon dioxide transport, systemic hypotension, and increased work of breathing. This study proposes a machine learning-based method (EDECOVID-net) that automatically differentiates the COVID-19 symptoms from pulmonary edema in lung CT scans using radiomic features. To the best of our knowledge, EDECOVID-net is the first method to differentiate COVID-19 from pulmonary edema and a helpful tool for diagnosing COVID-19 at early stages. The EDECOVID-net has been proposed as a new machine learning-based method with some advantages, such as having simple structure and few mathematical calculations. In total, 13 717 imaging patches, including 5759 COVID-19 and 7958 edema images, were extracted using a CT incision by a specialist radiologist. The EDECOVID-net can distinguish the patients with COVID-19 from those with pulmonary edema with an accuracy of 0.98. In addition, the accuracy of the EDECOVID-net algorithm is compared with other machine learning methods, such as VGG-16 (Acc = 0.94), VGG-19 (Acc = 0.96), Xception (Acc = 0.95), ResNet101 (Acc = 0.97), and DenseNet20l (Acc = 0.97).

Texture appearance model, a new model-based segmentation paradigm, application on the segmentation of lung nodule in the CT scan of the chest.

Lung cancer causes more than one million deaths worldwide each year. Averages of 5-year survival rate of patients with Non-small cell lung cancer (NSCLC), which is the most common type of lung cancer, is 15%. Computer-Aided Detection (CAD) is a very important tool for identifying lung lesions in medical imaging. In general, the process line of a CAD system can be divided into four main stages: preprocessing, localization, feature extraction, and classification. As segmentation is required for localization in computer vision and medical image analysis, this step has become a major and challenging problem, and much research has been done on new segmentation techniques. In recent years, interest in model-based segmentation methods has increased, and the reason for this is even if some object information is lost, such gaps can be filled by using the previous information in the model. This paper proposed Texture Appearance Model (TAM), which is a new model-based method and segments all types of nodule areas accurately and efficiently, including juxta-pleural nodules, without separating the lung from the surrounding area in a CT scan of the lung. In this method, Texture Representation of Image (TRI) is obtained using tissue texture feature extraction and feature selection algorithms. The proposed method has been evaluated in 85 nodules of the dataset, received from the Iranian hospital, in which the ground-truth annotation by physicians and CT imaging data were provided. The results show that the proposed algorithm has an encouraging performance for distinguishing different types of nodules, including pleural, cavity and non-solid nodules, achieving an average dice similarity coefficient (DSC) of 84.75%.

Aggregation Properties of Albumin in Interacting with Magnetic Fluids.

In this study, interactions of Fe3O4 magnetic nanoparticles with serum albumin biomolecules in aqueous solutions were considered. The studies were conducted with the laser correlation spectroscopy and optical analysis of dehydrated films. It was shown that the addition of magnetite to an albumin solution at low concentrations of up to 10-6 g/L led to the formation of aggregates with sizes of up to 300 nm in the liquid phase and an increase in the number of spiral structures in the dehydrated films, which indicated an increase in their stability. With a further increase in the magnetite concentration in the solution (from 10-4 g/L), the magnetic particles stuck together and to albumin, thus forming aggregates with sizes larger than 1000 nm. At the same time, the formation of morphological structures in molecular films was disturbed, and a characteristic decrease in their stability occurred. Most stable films were formed at low concentrations of magnetic nanoparticles (less than 10-4 g/L) when small albumin-magnetic nanoparticle aggregates were formed. These results are important for characterizing the interaction processes of biomolecules with magnetic nanoparticles and can be useful for predicting the stability of biomolecular films with the inclusion of magnetite particles.

Localized surface plasmon modes of core-shell bimetal nanowires do not hybridize.

We investigate localized plasmon-mode resonances in the scattering and absorption of transversely polarized light by bimetal core-shell nanowires made of silver and gold, in two possible combinations, using an analytical solution and experimental data for metal permittivities. In particular, the influence of the metal shell thickness and material on these resonances is studied. We show that, unlike metal nanotubes with dielectric filling, bimetal wires do not display plasmon-mode hybridization.

Design of Liver Functional Reserve Estimation Technique Based on Optical Densitometry.

This work is aimed at creating a modified invasive technique for assessing the liver's functional reserves. A study of the degree of hepatodepression is carried out by measuring the plasma elimination of indocyanine green using the method of optical densitometry. This paper presents test results for an aqueous solution and an albumin solution, as well as the results of measurements of plasma elimination of indocyanine green for patients with liver disease. Perfecting the proposed method will make an important scientific contribution to modern diagnostic medicine. Diagnosing the stages in the progression of the disease and its developing complications can make it possible to rapidly correct the patient's treatment algorithm, achieving positive outcomes in medical practice.

Molecular Aggregation in Immune System Activation Studied by Dynamic Light Scattering.

Determination of the concentration and size of the circulating immune complexes in the blood is an essential part of diagnostics of immune diseases. In this work, we suggest using the dynamic light scattering method to determine the sizes of circulating immune complexes in blood serum. By the dynamic light scattering spectrometer, we found that for healthy and sick donors, the size and concentration of circulating immune complexes differed significantly. The dynamics of formation of these complexes were also examined in this work. It was shown that the formation of immune complexes in the blood of healthy donors is faster than the same reactions in the blood serum of donors with diseases. The results can be used in the diagnostics of the immune status and detection of chronic inflammation. We can recommend the dynamic light scattering method for implementation in biomedical diagnostics.

Refractive-index sensitivities of hybrid surface-plasmon resonances for a core-shell circular silver nanotube sensor.

We study the scattering and absorption of an H-polarized plane electromagnetic wave by a circular silver nanotube in the visible range of wavelengths using the separation of variables. The computed spectra of the extinction cross section display several hybrid localized surface-plasmon resonances of the dipole and multipole type. Analytical equations are derived for their resonance wavelengths. Bulk refractive-index sensitivities of nanotube-based sensors are determined, showing higher values for multipole resonances.