PURPOSE:This study provides a detailed analysis of the bioinorganic chemical composition of lens substance in patients with senile cataract using classical and spatial statistics methods. MATERIAL AND METHODS:The study included 30 isolated human lenses. The light scattering ability (LSA) of the lens substance was evaluated using an original method. Additionally, distribution of chemical elements in the lens substance was analyzed using a scanning electron microscope with energy dispersive spectrometer (SEM/EDS). Measurements by all methods were carried out in a single coordinate space, which made it possible to compare the spatial correlation of different parameters. RESULTS:Small-angle light scattering of the lens substance has been quantitatively characterized for the first time. In contrast to the conventional norm, in senile cataract the accumulation fields of the majority of ion-forming elements (including Na, P, K, Cl) are distributed along the lines repeating the geometry of the lens capsule. At the same time, the light scattering ability of certain areas of the lens is significantly correlated with changes in the concentrations of Na, P, K, Ca in these areas. In particular, one ion-forming element can be distinguished - Na: spatial change of its concentration in senile cataract is strongly associated with a local change in LSA of the lens with opacities clustering of any degree. Thus, a change in the nature of the Na accumulation in the lens volume can be considered the main marker of senile cataract formation. CONCLUSION:The distribution pattern of ion-forming elements indicates that the loss of barrier properties in the capsule plays a significant role in the development of senile cataract.

Retinal diseases remain one of the leading causes of visual impairments in the world. The development of automated diagnostic methods can improve the efficiency and availability of the macular pathology mass screening programs. The objective of this work was to develop and validate deep learning algorithms detecting macular pathology (age-related macular degeneration, AMD) based on the analysis of color fundus photographs with and without data labeling. We used 1200 color fundus photographs from local databases, including 575 retinal images of AMD patients and 625 pictures of the retina of healthy people. The deep learning algorithm was deployed in the Faster RCNN neural network with ResNet50 for convolution. The process employed the transfer learning method. As a result, in the absence of labeling, the accuracy of the model was unsatisfactory (79%) because the neural network selected the areas of attention incorrectly. Data labeling improved the efficacy of the developed method: with the test dataset, the model determined the areas with informative features adequately, and the classification accuracy reached 96.6%. Thus, image data labeling significantly improves the accuracy of retinal color images recognition by a neural network and enables development and training of effective models with limited datasets.

Considering the demand for investigating spatial cell arrangement within a scaffold, yet unsatisfied, we aimed at developing a new method of sample preparation for SEM that would, besides other things, allow visualization of cellular ultrastructure and deliver from toxic chemicals and artifacts. A combination of BSE imaging in a low-vacuum mode with supravital lanthanoid staining has yielded highly informative images that contain many of the subsurface cell structures, particularly, intercellular contacts, cell membranes, nuclei with nucleoli, mitochondria, endoplasmic reticulum, and cytoskeleton. Moreover, the developed approach provides an idea of three-dimensional arrangement of cellular elements with a greater depth of focus than that of optical methods, which is drastically important for fulfilling the needs of tissue engineering. Being fast and simple, the method is able to change the whole concept of using SEM in biomedical studies.

The article studies application possibilities and potency of near infrared coagulative radiation of a diode laser for correction of postoperative iris defects followed by pupillary deformities and displacement. It is found that the use of near infrared diode laser radiation at 0.810 microm enables visual improvement through pupil enlargement or its shift to the optical centre. The use of the wavelength specified does not cause severe atrophic or cosmetic changes in the iris.