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The prevalence of rotator cuff ruptures among injuries of the musculoskeletal system according to the literature is 20 %. Massive injuries to the rotator cuff of the shoulder are tears with retraction of more than 5 cm or damage to two or more tendons. With massive injuries, the head of the humerus is decentrated, the biomechanics of the joint is impaired and, as a result, the progression of osteoarthritis occurs.The aim. To analyze the literature data of foreign and domestic authors and to provide an overview of modern views on the surgical treatment of massive ruptures of the rotator cuff tendons.Materials and methods. A search and analysis of the literature of foreign and domestic authors was carried out using the Internet resources PubMed, eLibrary in the period from 2010 to 2024.Results and discussion. To date, in treatment of massive ruptures, much attention is paid to the reconstruction of the upper capsule of the shoulder joint. The biomechanical principle underlying the reconstruction of the superior capsule involves the use of a graft to close a tendon defect. Today, the most commonly used grafts are allografts, autografts, xenografts, and synthetic materials. This review presents surgical techniques and results of surgical treatment using the broad fascia of the thigh, dermal allografts, peroneus longus tendon, biceps brachii longus tendon, and Achilles tendon, but there is no consensus on the choice of grafts and methods of their fixation.Conclusion. An analysis of literature sources does not provide a clear answer to the question of choosing the most effective method of treating this pathology. The number of modern publications shows that the problem of improving the treatment of such patients is currently relevant and requires in-depth research.

This paper is devoted to the memory of Volodymyr Methodyevich Yakovenko, an outstanding scientist in the field of solid-state radio physics, Honored Worker of Science and Technology of Ukraine, Full Member of the National Academy of Sciences (NAS) of Ukraine, Honorary Director of the O.Ya. Usykov Institute for Radiophysics and Electronics (IRE) within the NAS. At the IRE, Dr. Yakovenko founded a scientific school that is well-known for its studies of charged particles’ interaction with electromagnetic fields in inhomogeneous conducting media. In addition, he initiated research in a new branch of today’s solid state physics, specifically investigation of plasma instabilities in semiconductors that occur because of heating of conduction electrons by the existing elecromagnetic fields.

One of the urgent directions in solving medical diagnostic tasks is to develop new and improved decision support systems capable of efficiently processing polymodal data. Humans cannot always process large arrays of medical information and determine an accurate diagnosis in complex situations. Thus, improving the functioning of the industry requires implementing a variety of systems capable of supporting decision-making of one kind or another. The presented technique aims to steadily increase the level and speed, and demonstrate the feasibility of integrating non-classical logic into the structure of the decision-making system in medical research by using non-classical logic complexes. The main advantage of the proposed approach is that it achieves the necessary level of information criteria; in particular, it provides the required information quality, high reliability of the decision, its value, preserves the amount of information, and searches and decision-making take relatively small-time intervals. This paper presents an overview of various non-classical logics and, based on the analytical findings, delineates the optimal choice of logic for each stage in the development of a decision support system. The processing and feedforward structures for DSS are presented based on selected types of non-classical logic. The algorithms presented for solving decision-making problems are based on discrete-logic approximations of a priori and actual data, which are optimal or suboptimal, and they use information and value criteria. The abstraction of any problem situation relies on using means operating with frequency and comparative logic to provide logical approximations of the sought characteristics. The accuracy of the diagnostic decisions reached 97% when using the developments presented in this study. Doi: 10.28991/HIJ-2024-05-04-010 Full Text: PDF

Various research fields, such as biology and medicine, have increasingly used techniques for the machine estimation of generated pulses. For example, in biological medicine, classifying information makes it possible to automate the interpretation of incoming data obtained owing to diagnosis, which is necessary when processing large volumes of information arrays. This study aims to develop algorithms that enable the selection of single-type objects in images with subsequent image splitting into a set list of segments with heterogeneous tension, even when the number of reference images is very small. Analysis of existing algorithms allowed us to determine the focus area for developing and improving the efficiency of the algorithms. The existing algorithms show poor performance results in analyzing the dark parts of images, so it makes sense to develop an algorithm for image intensity normalization. The developed algorithm simplifies the procedure of partitioning the training base for the classifier owing to the use of the feature vectors. A random forest algorithm was used for image classification, followed by boundary refinement using a Markov field. The image-splitting algorithm precisely separates parts of the brain structure by applying a Markov field to refine classification results. The proposed classification algorithm showed strong results in comparison with existing algorithms, particularly in the comparison of the Dice criterion. The proposed method shows an average increase of 10% in classification accuracy. One way to improve the presented algorithm is to add texture elements to the feature vector, which allows the identification of distinguishing features of the elements, such as shape and length, which could improve this algorithm for a more accurate classification of substructures. Doi: 10.28991/HEF-2024-05-04-011 Full Text: PDF

Modern complexes providing biometric identification face several problems, such as information drift caused by the variability of facial patterns, voice timbres, and current states. Information drift can characteristically exhibit short-term (subjects' states have changed) or long-term changes. Simultaneously, the developed trusted systems should not have the properties of explainable AI to prevent the possibility of intruders, based on understanding the system behavior to perform actions to hack the system. This paper's objective is to improve the reliability of biometric authentication by increasing the informativity of the classified images by transforming the correlations between the information features using the Bayes-Minkowski measure. The paper puts forth the proposition of employing neuroimmune models that are founded upon the principles of both acquired and innate immunity, with an analogy to the natural immune system. In addition, the authors propose to analyze correlations between information features instead of the features themselves. To reduce the influence of data drift, the authors suggest using adaptive learning with a teacher and reinforcement, which helps to work even with small and unrepresentative data samples. The proposed algorithm demonstrates a high degree of accuracy, as evidenced by its equal error rate (EER), and is particularly well-suited to feature recognition tasks due to its adaptive model. The test results have shown that the proposed solutions increase the level of security of personal data and improve the reliability of biometric authentication against fraudulent actions of intruders, including approaches based on adversarial algorithms. The integration of the immune structure into the authentication system enables the algorithm to remain stable even when presented with a limited number of samples. The proposed algorithm mitigates the impact of data drift on the authentication outcome. Doi: 10.28991/ESJ-2024-08-06-018 Full Text: PDF

Background. Antimicrobial resistance is one of the most serious threats to modern clinical medicine. Despite the significant successes of modern pharmacology, the search for effective antiseptics that can influence wound processes continues. In our study, an experimental study of the effect of Anolit solution on pathogenic microorganisms Pseudomonas aeruginosa and Proteus mirabilis was conducted.The aim. To evaluate the antimicrobial activity of the domestic antiseptic “Anolit” against gram-negative pathogens of nosocomial infections using the example of P. aeruginosa and P. mirabilis.Methods. To assess the quality of the effect of “Anolit” on the studied colonies of microorganisms, experimental studies were performed in vitro. The bacteriological study was carried out in three stages using the Koch method.Results. “Anolit” is a modern antiseptic agent with a pronounced antimicrobial effect based on the electrolysis of aqueous solutions. Its ability to lyse pathogenic microorganisms (P. aeruginosa and P. mirabilis) has been confirmed by experimental research.Conclusion. The bactericidal effect of “Anolit” on the growth of gram-negative bacteria is shown, proving its high antimicrobial efficacy.

The results of modeling the accumulation of slag residue in the combustion chamber of a propulsion system on pasty propellant obtained using a multiphase flow evolution model are presented. The influence of composition solutions on the process of slag formation is established; the significance of this process for the quality of such engines is shown. The nature of the influence of the parameters of the burning process for propellants of the type under consideration on the intensity of slag formation is determined. It is shown that the degree of involvement of metallic fuel in the agglomeration process has the greatest influence on slag formation. It has been established that the most effective ''tool'' for suppressing slag formation in the combustion chambers of paste propulsion is to change the regularities of the burning process, mainly associated with the formation of condensed products at the surface of the burning propellant.

In this paper, we considered and characterised the features of information systems management of agricultural production processes, which are based on agricultural technologies to support and increase food security in the domestic and external markets. We revealed management models in this category, which are used in the agrarian sector of Canada and Vietnam. Differences between these two models are connected with several factors, including socioeconomic development, quality of arable land, availability of resources and their cost, level of labour resources' readiness to the implementation of new ICT, and farms’ adopting the necessity of transition to precise farming. The revealed advantages and problems of implementing Agritech at the level of the two countries demonstrate that the focus on sustainable and precise farming has become a new trend in this sphere. The goal of this paper was to establish the features of the formation of approaches to information systems management in Agritech to ensure the food security of countries that produce agricultural products. The scientific novelty of this paper lies in the determination of approaches to the implementation and management of information systems in the considered category, which facilitate the creation of conditions for the growth of efficiency of agricultural productions under the influence of threats of climate change.

Combinatorial strategies, such as targeting different immune checkpoint receptors, hold promise to increase the breadth and duration of the response to cancer therapy. Here we describe the preclinical evaluation of CTX-8371, a protein construct which combines PD-1 and PD-L1 targeting in one bispecific, tetravalent antibody. CTX-8371 matched or surpassed the activity of anti-PD-1 and PD-L1 benchmark antibodies in several in vitro T cell activation assays and outperformed clinically approved benchmarks in the subcutaneous MC38 colon and the B16F10 lung metastasis mouse tumor models. Investigation into the mechanism of action revealed that CTX-8371 co-engagement of PD-1 and PD-L1 induced the proteolytic cleavage and loss of cell surface PD-1, which is a novel and non-redundant mechanism that adds to the PD-1/PD-L1 signaling axis blockade. The combination of CTX-8371 and an agonistic anti-CD137 antibody further increased the anti-tumor efficacy with long-lasting curative therapeutic effect. In summary, CTX-8371 is a novel checkpoint inhibitor that might provide greater clinical benefit compared to current anti-PD-1 and PD-L1 antibodies, especially when combined with agents with orthogonal mechanisms of action, such as agonistic anti-CD137 antibodies.

Introduction: Playing football (American soccer) at the elite level places extremely high demands on the spine. The choice of treatment for lumbar spinal canal stenosis combined with spondylolysis and anterolisthesis is challenging for surgeons, physicians, and clinicians. Case Presentation: A 28-year-old elite football player from the leading Russian football club is the focus of this case study. Patient complained of constant low back pain radiating to the posterior surface of the right thigh and limited mobility in the lumbar spine. Diagnosis: The patient was diagnosed with degenerative stenosis of the spinal canal at the L5 to S1 level, L4 to L5 disc herniation, spondylolysis, and anterolisthesis of L5 vertebra. Management and Outcomes: Two-stage surgery of combined lumbar spine pathology was performed in May 2021. The first stage of surgical treatment included the spinal and foraminal canals decompression at the L4 to L5 and L5 to S1 levels, as well as posterior fusion with rigid pedicle screws. The second stage involved the subsequent replacement of the rigid to dynamic semirigid rods as signs of the bone defect healing appeared. The athlete returned to team training 6 months after the first stage of surgery and was able to play football at the elite level without restrictions 1 year after the first stage and just 30 days following the second stage of surgical treatment. The athlete continues his career as of the last manuscript's revision. Conclusions: This method holds potential as an effective approach in the treatment of combined lumbar spine pathology among elite athletes. [ABSTRACT FROM AUTHOR]

Abstract Exfoliation of 2D non‐Van der Waals (non‐vdW) semiconductor nanoplates (NPs) from inorganic analogs presents many challenges ahead for further exploring of their advanced applications on account of the strong bonding energies. In this study, the exfoliation of ultrathin 2D non‐vdW chromium sulfide (2D Cr2S3) by means of a combined facile liquid‐phase exfoliation (LPE) method is successfully demonstrated. The morphology and structure of the 2D Cr2S3 material are systematically examined. Magnetic studies show an obvious temperature‐dependent uncompensated antiferromagnetic behavior of 2D Cr2S3. The material is further loaded on TiO2 nanorod arrays to form an S‐scheme heterojunction. Experimental measurements and density functional theory (DFT) calculations confirm that the formed TiO2@Cr2S3 S‐scheme heterojunction facilitates the separation and transmission of photo‐induced electron/hole pairs, resulting in a significantly enhanced photocatalytic activity in the visible region.

The development of 3D scanning technologies has made it possible to obtain an increasing amount of data about the external world, which leads to the need for efficient methods of processing acquired data to recognize objects. Traditional approaches face accuracy, speed, and reliability problems due to the complexity and diversity of object shapes, sizes, degrees of detail, and the presence of noise and artifacts in the data. Therefore, our goal was to improve the object recognition efficiency. It is necessary to determine the method of obtaining geometric and topological parameters. In the paper it is proposed to use the method of Laplace-Beltrami, which allows to calculate distances and angles between points within a given area. Next, it is necessary to determine which parameters will be used to analyze the obtained geometric data. We propose the use of three spectral descriptors – Heat Kernel Signature (HKS), Weave Kernel Signature (WKS) and the wavelet descriptor (SGWT). Then, we develop a high-accuracy recognition method based on spectral and topological invariants processed using a convolutional neural network. Subsequently, the parameters of the descriptors are calculated, and then they are calculated through the neural network, resulting in the classification of the object. In summary, the structure of the proposed method comprises the computation of the Laplace-Beltrami spectrum, the construction of spectral distribution maps, and the subsequent processing of this information using a neural network. After analyzing the results, we found that the proposed method has a recognition rate of 0.9 s and recognition accuracy of 97%. It was shown how much more effective the use of the three descriptors was compared to the use of each one individually. An example of object recognition using the proposed method was also given. The methodology outlined in this paper utilizes machine learning to achieve high levels of accuracy in the classification of different objects. Effectiveness of the proposed method in this study enhance existing recognition systems and open new opportunities for their application in various fields, including robotics, agriculture, and navigation. This research demonstrates considerable potential for further development and application in the field of agriculture, underscoring the continued necessity for research in this area