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The underlying assumption of the study is that ERP systems can crucially facilitate information exchange; yet, the agricultural sector is slow in their adoption due to different reasons, including a shortage of skilled personnel as well as a lack of knowledge about ERP capabilities among top managers and key employees. The study intends to identify challenges and prospects for ERP implementation in agriculture. The applied methods include the analysis of WoS publications and questionnaire surveys of executives of 55 companies operating in the Middle Urals’ agricultural sector. ERP systems can be defined as comprehensive software solutions aimed to integrate business and management processes through a holistic approach and a single information system. According to expert estimates, in today’s Russia the projects related to the agro-industrial sector account for 1-2% to 10-15% of the projects from the leading ERP vendors, including 1C, Bars Group, and Navigator-Agro. ERP systems in agriculture help improve business performance, reduce and monitor costs. These systems are effective in decision-making and can serve as the basis for precision agriculture. The main barriers are poor personnel skills and competencies, shortage of funds for ERP adoption, poorly developed or absent infrastructure, difficulties of fitting and adapting of ERP systems to agricultural business. In addition, agricultural business owners show no confidence in high-tech solutions and poor knowledge of the above systems. Other problems include operation complexity and insufficient government support in ERP implementation. The results of the study can be used by government authorities in their programs for innovative development and technical upgrading of the agriculture industry.

At present, the entities of the agrarian sector are moving towards the digital, intellectual and robotic technologies or the robotization of the industry. Robotics is used in the various fields of the agrarian sector, the most widespread is the milking robotics. From 2006 to 2016, in the agricultural enterprises of the Russian Federation, 393 units of robotics were introduced. While in the Sverdlovsk region at the beginning of 2018, 37 units of this equipment have been used. We suggest to use the indicator of “density of agricultural robotization” to characterize the effectiveness of the transition to robotics. This indicator for Russia increased from 0.02 to 0.78 units, and for the Sverdlovsk region — from 0.3 to 3.17 for the periods under review. However, the slowdown and low values of the density of agricultural robotics make it necessary to develop appropriate measures. The article proposes a model of an organizational and economic mechanism for the transition to robotics. It includes target, investment, technical and technological as well as personnel sections. The initial data on the state of the introduction of robotics in agricultural organizations was provided by the ministries and departments, as well as statistical bodies. We determined scenarios of inertial, targeted and system development of robotics in agriculture depending on the level of state support. We have determined that the investments needed for the robotization of agricultural enterprises in the Sverdlovsk region are at the level of 472 million rubles. Moreover, we have formulated the tasks, recommendations and the expected result from the training of personnel capable to master robotics in the industry. The most important measure is the reduction in the cost of this equipment due to its domestic production. The results of the research can be used by the executive authorities in developing programmes for the innovative development and technical modernization of agriculture.

At present, the entities of the agrarian sector are moving towards the digital, intellectual and robotic technologies or the robotization of the industry. Robotics is used in the various fields of the agrarian sector, the most widespread is the milking robotics. From 2006 to 2016, in the agricultural enterprises of the Russian Federation, 393 units of robotics were introduced. While in the Sverdlovsk region at the beginning of 2018, 37 units of this equipment have been used. We suggest to use the indicator of “density of agricultural robotization” to characterize the effectiveness of the transition to robotics. This indicator for Russia increased from 0.02 to 0.78 units, and for the Sverdlovsk region — from 0.3 to 3.17 for the periods under review. However, the slowdown and low values of the density of agricultural robotics make it necessary to develop appropriate measures. The article proposes a model of an organizational and economic mechanism for the transition to robotics. It includes target, investment, technical and technological as well as personnel sections. The initial data on the state of the introduction of robotics in agricultural organizations was provided by the ministries and departments, as well as statistical bodies. We determined scenarios of inertial, targeted and system development of robotics in agriculture depending on the level of state support. We have determined that the investments needed for the robotization of agricultural enterprises in the Sverdlovsk region are at the level of 472 million rubles. Moreover, we have formulated the tasks, recommendations and the expected result from the training of personnel capable to master robotics in the industry. The most important measure is the reduction in the cost of this equipment due to its domestic production. The results of the research can be used by the executive authorities in developing programmes for the innovative development and technical modernization of agriculture.

Autoimmunity is intrinsically driven by memory T and B cell clones inappropriately targeted at self-antigens. Selective depletion or suppression of self-reactive T cells remains a holy grail of autoimmune therapy, but disease-associated T cell receptors (TCRs) and cognate antigenic epitopes remained elusive. A TRBV9-containing CD8 + TCR motif was recently associated with the pathogenesis of ankylosing spondylitis, psoriatic arthritis and acute anterior uveitis, and cognate HLA-B*27-presented epitopes were identified. Following successful testing in nonhuman primate models, here we report human TRBV9 + T cell elimination in ankylosing spondylitis. The patient achieved remission within 3 months and ceased anti-TNF therapy after 5 years of continuous use. Complete remission has now persisted for 4 years, with three doses of anti-TRBV9 administered per year. We also observed a profound improvement in spinal mobility metrics and the Bath Ankylosing Spondylitis Metrology Index (BASMI). This represents a possibly curative therapy of an autoimmune disease via selective depletion of a TRBV-defined group of T cells. The anti-TRBV9 therapy could potentially be applicable to other HLA-B*27-associated spondyloarthropathies. Such targeted elimination of the underlying cause of the disease without systemic immunosuppression could offer a new generation of safe and efficient therapies for autoimmunity. Targeted depletion of TRBV9 + T cells induces remission in a single patient with ankylosing spondylitis, with significant improvements in functional and mobility metrics.

Immune system plays a central role in the pathogenesis of cancer and autoimmune diseases. An entire field has emerged to identify separate minor cell subpopulations carrying potential molecular targets or activation markers to study their prognostic role in disease progression and severity or predictive potential to use immunotherapy. However, the biomarker potential of minor populations is limited, as it does not take into account systemic interactions between populations of the immune system. A number of studies in the COVID era have shown that the certain balance between immune cell populations in donor’s blood, called ‘immunotype’, can predict the outcome of treatment and the onset of a cytokine storm. This observation was extended to other diseases, including cancer and autoimmunity. It was shown that the immunotype can be used to diagnose both the presence of the disease itself, as well as its form or progression, to stratify patients in the risk groups and to predict the effectiveness of therapy. The most important advantages of immunotype-based diagnostics are its low invasiveness, the possibility of multiple biomaterial sampling, and the complexity of the analysis by the simultaneous assessment of blood cell composition and their functional activity. In this review, we summarize currently available studies of immunotypes and defined key subpopulations, their possible impact in diagnostics and personalization of the therapy in clinical routine practice in various diseases.

This case report describes a novel intronic mutation, CYP21A2:c.738+75C>T (rs1463196531), identified in a 4-year-old male with congenital adrenal insufficiency, and expands the known mutation spectrum associated with this condition. The patient, born full-term to unrelated parents, presented with adrenal failure within the first month of life, characterized by acute adrenal crisis symptoms such as vomiting, dehydration, weight loss, hypotension, and electrolyte imbalances. Hormonal evaluations confirmed primary adrenocortical insufficiency, necessitating ongoing hydrocortisone and fludrocortisone therapy. Using family trio-based amplicon sequencing of the CYP21A2 gene, we identified compound heterozygosity consisting of a full gene deletion and a novel pathogenic intronic mutation. Additionally, analysis of WGS data was performed to rule out pathogenic variants in genes that might lead to a similar phenotype, thereby eliminating the possibility of other genes contributing to the proband’s disease. This case demonstrates the potential of using amplicon sequencing in molecular genetic diagnostic testing to detect rare intronic variants in the CYP21A2 gene in cases of early-onset adrenal failure. It also contributes to a better understanding of the genetic basis of congenital adrenal hyperplasia (CAH), which remains a significant autosomal recessive disorder affecting cortisol and aldosterone production, with an incidence of 1 in 10,000 to 1 in 15,000 globally.

The worldwide spread of multidrug-resistant Mycobacterium tuberculosis strains prompted the development of new strategies to combat tuberculosis, one of which is antisense therapy based on targeting bacterial mRNA by oligonucleotide derivatives. However, the main limitation of antisense antibacterials is poor cellular uptake because of electrostatic charge. Phosphoryl guanidine oligo-2′- O -methylribonucleotides (2′-OMe PGOs) are a novel type of uncharged RNA analogues with high RNA affinity, which penetrate through the bacterial cell wall more efficiently. In this study, we investigated the uptake and biological effects of 2′-OMe PGO in mycobacteria. The results indicated that 2′-OMe PGO specific for the alanine dehydrogenase-encoding ald gene inhibited the growth of Mycobacterium smegmatis and downregulated ald expression at both the transcriptional and translational levels through an RNase H-independent mechanism, showing higher biological activity than its phosphorothioate oligonucleotide counterpart. Confocal microscopy revealed that the anti- ald 2′-OMe PGO was taken up by intracellular mycobacteria residing in RAW 264.7 macrophages without exerting toxic effects on eukaryotic cells, indicating that 2′-OMe PGO was able to efficiently cross two cellular membranes. In addition, 2′-OMe PGO inhibited the transcription of the target ald gene in M. smegmatis -infected macrophages. Thus, we demonstrated, for the first time, a possibility of targeting gene expression and inhibiting growth of intracellular mycobacteria by antisense oligonucleotide derivatives. Strong antisense activity and efficient uptake of the new RNA analogue, 2′-OMe PGO, by intracellular microorganisms revealed here may promote the development of novel therapeutic strategies to treat TB and prevent the emergence of drug-resistant mycobacterial strains.

Background The COVID-19 pandemic offers a powerful opportunity to develop methods for monitoring the spread of infectious diseases based on their signatures in population immunity. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become the method of choice for identifying T cell receptor (TCR) biomarkers encoding pathogen specificity and immunological memory. AIRR-seq can detect imprints of past and ongoing infections and facilitate the study of individual responses to SARS-CoV-2, as shown in many recent studies. Methods The new batch effect correction method allowed us to use data from different batches together, as well as combine the analysis for data obtained using different protocols. Proper standardization of AIRR-seq batches, access to human leukocyte antigen (HLA) typing, and the use of both α- and β-chain sequences of TCRs resulted in a high-quality biomarker database and a robust and highly accurate classifier for COVID-19 exposure. Results Here, we have applied a machine learning approach to two large AIRR-seq datasets with more than 1,200 high-quality repertoires from healthy and COVID-19-convalescent donors to infer TCR repertoire features that were induced by SARS-CoV-2 exposure. Conclusions This developed classifier is applicable to individual TCR repertoires obtained using various protocols, paving the way to AIRR-seq-based immune status assessment in large cohorts of donors.

Minimally invasive diagnostics based on liquid biopsy makes it possible early detection of lung cancer (LC). The blood plasma circulating cell-free DNA (cfDNA) fragments reflect the genome and chromatin status and are considered as integral cancer biomarkers and the biological entities for 'cancer-of-origin' prediction. The aim of this work is to create a method for processing next-generation sequencing (NGS) data and an interpretable binary classification model (CM), which analyzed cfDNA fragmentation features for distinguishing healthy subjects and subjects with LC. 148 healthy subjects and 138 subjects with LC were included in the study. cfDNA fractions, isolated from blood plasma biospecimens, were used for DNA libraries preparations and NGS on the NovaSeq 6,000 Illumina system with a coverage of 100 million reads/sample. Twelve variables, describing the abundance and length distribution of cfDNA fragments within each genomic interval, and 40 variables based on the values of position-weight matrices, describing combinations of 5-bp-long terminal motifs of cfDNA fragments, were used to characterize genomic fragmentation. Classification models of the first phase of machine learning were based either on logistic regression with L1- and L2-regularization or were probabilistic CMs based on Gaussian processes. The second phase CM was based on kernel logistic regression. The final CM can distinguish healthy subjects and subjects with LC with AUC values of 0.872-0.875. The performance of developed CM was evaluated using datum and testing sets for each LC stage category. Sensitivity values ranged from 66.7 to 85.7%, from 77.8 to 100%, and from 70 to 80% for LC stages I, II, and III, respectively. Specificity values ranged from 79.3 to 90.0%. Thus, the CM has a good diagnostic value and does not require clinical or other data on tumor-associated biomarkers. The current method for LC detection has some advantages for future clinical implementation as a decision-making support system due to the performance of the CM requires data exclusively from NGS-analysis of blood plasma cfDNA fragmentation; the accuracy of the CM does not depend on any additional clinical data; the CM is highly interpretable and traceable; CM has appropriate modular architecture.

Nearly identical, repetitive elements in the genome contribute to the variability in genetic inheritance patterns, particularly in regions like the RCCX locus, where such repeats can lead to structural variations. In addition, during the formation of gametes as a result of meiosis, variants of loci with repetitive elements that do not code for the required proteins may occur. As a result, an individual with certain genetic rearrangements in this region may have an increased risk of developing a congenital disorder, particularly in cases where the non-functional allele is inherited dominantly. At the same time, there is still no routine or generally recognized diagnostic method to determine the sequence of the repetitive fragments. The functionally important RCCX locus consists of such repetitive fragments. The available knowledge about the genomic variants of the RCCX locus is fragmented, as there is no standardized method to determine its structure. It should be noted that in some structural variants of the RCCX locus, the sequence of protein-coding genes is disrupted, leading to the development of diseases such as congenital adrenal hyperplasia (CAH). Although genetic testing is generally accepted as a gold standard for CAH diagnosis, there are a myriad of strategies on which exact methods to use and in which order. The reason for this inconsistency lies in the complexity of the RCCX locus and the fact that each patient or carrier may have a highly individualized mutation or combination thereof. In this review, we have discussed all known methods that can be used to study the structure of the RCCX locus. As a result, optimal approaches are proposed for the diagnosis of the most common disease caused by lesions in the RCCX–CAH due to CYP21A2 deficiency.