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Objective: to evaluate the effectiveness of psychocorrection based on the methods of self­regulation in athletes.Materials and methods: the effectiveness was evaluated on 104 male athletes who had an optimal level of psychoemotional state of the body during a background examination by electroencephalography (EEG). The subjects were randomly assigned to three groups of psychocorrection: control, EEGtraining, and VR­therapy. Mathematical and statistical processing was carried out in Statistica 7.Results: the average values of the wave intensity index (WII) after psychological correction using the following methods: EEG­training and VRtherapy showed positive EEG dynamics in athletes of the second group (EEG­training) 65,6 % (21) and the third group (Vr­therapy) 73,8 % (31) of the subjects, respectively. Whereas in the control group, self­healing was recorded in 9 (30 %) athletes without psychocorrection procedures.Conclusion: the quantitative criterion of WII is a universal informative indicator of the functional activity of the brain for evaluating the effectiveness of psychocorrection aimed at optimizing the psychoemotional state. Psychocorrection of athletes using EEG­training and VR­therapy provides efficiency in 73.8 and 65.6 % of cases, which significantly softens pre­start tension as a result of reducing anxiety and restoring the psychoemotional state of the body to an optimal level by involving the psychological reserve.

Levetiracetam (LEV) is one of the most commonly prescribed antiepileptic drugs (AED). However, there were no studies on its efficacy and safety in terms of the correlation with epileptiform activity index (EAI) performed among the Russian population.Aim. To evaluate the efficacy and tolerability of LEV monotherapy in patients with newly-diagnosed epilepsy using epileptiform activity index (EAI) assessment.Materials and methods. The study included 107 patients (46 (43.0%) male and 61 (57.0%) female) with focal epilepsy (FE) (39.3%; n=42) or idiopathic generalized epilepsy (IGE) (60.7%; n=65). At each visit, video-electroencephalographic (video-EEG) monitoring was performed (baseline and in 1, 3, 6, and 12 months of the therapy). Therapeutic drug monitoring was performed at dose titration in 1 month of the therapy or in case of therapy correction. Treatment efficacy was assessed using the criteria of seizure absence (medically induced remission), seizure rate decrease by >50% (responders), seizure rate decrease by Results. Total EAI at baseline was 5.2-fold higher in patients with IGE compared to FE patients (23.4±3.0 and 4.5±0.97, respectively). After 1 month of LEV therapy, EAI decreased to 3.4±1.1 and 1.9±0.4 in patients with IGE and FE, respectively (pConclusions. LEV is an effective drug of choice for the initial treatment of newly-diagnosed FE and IGE in monotherapy along with a significant decrease in EAI. EAI is an objective measure of LEV treatment efficacy.

We present the cases of three patients with severe, life-threatening coronavirus disease 2019 (COVID-19) who had failed to achieve substantial improvement on intial treatment. They subsequently received pulse therapy with methylprednisolone [1,000 mg/day intravenously (IV) for three consecutive days] and IV immunoglobulin (20 g/day). This treatment regimen was associated with a prompt resolution of respiratory failure, elimination of clinical manifestations of the cytokine release syndrome (CRS), and reversal of pulmonary CT changes. The treatment was generally safe and well-tolerated. There was no evidence of protracted persistence of the virus in the patients. Further randomized controlled trials are required to better understand the efficacy and safety of high-dose methylprednisolone and IV immunoglobulin, separately or in combination with each other, in the treatment of severe, life-threatening COVID-19.

Three patients with severe life-threatening COVID-19 who failed to achieve substantial improvement on tocilizumab, received pulse therapy with corticosteroids (methylprednisolone, 1000 mg/day IV for three consecutive days) and intravenous immunoglobulin (20 g/day IV). This was associated with a prompt resolution of respiratory failure, elimination of cytokine release syndrome, and reversal of pulmonary CT changes. The treatment was generally safe and well tolerated. There was no evidence of protracted persistence of the virus in the patients who received pulse therapy. Randomized controlled trials are necessary to specify the efficacy and safety of high-dose methylprednisolone and intravenous immunoglobulin in the treatment of severe life-threatening COVID-19 separately or in combination.

Objective: to develop an algorithm for determining the extent of surgical intervention for pain in the heel region based on a comprehensive clinical and instrumental assessment of the functional state of the lower extremities. Materials and methods: during the examination of 18 athletes with pain in the heel area, we used a thermal imaging examination (at rest and after exercise) in combination with magnetic resonance imaging data. Infrared thermography was performed using an infrared camera. Data analysis was carried out using the specialized online service tvision.pro, which allows you to track the dynamics of the patient's thermographic picture. The combination of images of thermographic and MRI studies makes it possible to assess the functional state of the structures of the heel region with the identification of the structures most involved in the pathological process. Results: based on the experience of treating athletes with pain in the heel area, we developed an algorithm for determining the extent of surgical intervention for Haglund deformity. The algorithm is based on the analysis of MRI data and their comparison with the results of the examination of the soft tissue of the heel area using functional thermal imaging. Conclusions: a comprehensive analysis of the data of thermographic and tomographic studies allows you to perform an objective assessment of the functional state of the patient based on the primary localization of the pathological process. The developed algorithm is applicable for planning the extent of surgical intervention and making a choice of preferable technique. The proposed tactic contributes to early return to competitive load. The infrared thermography technique provides important additional data on the functional state of the athlete's musculoskeletal system. It is absolutely safe for both the patient and the specialist who conducts the study.

Achilles tendon injury frequency increases. The lack of standardized methods of rehabilitation after surgery on the Achilles tendon leading to increased risk of repeat injury tendon and reduces the possibility return to physical activity. Recovery of function the main task of rehabilitation programs, especially when working with athletes. We have a program of rehabilitation of 147 patients was per- formed after surgery on the Achilles tendon, which began from the first day after the operation and was as much as possible early onset of axial load.

Active physical exercises and growth are associated with mineral imbalances in young athletes. The purpose of this study was to examine the impact of sport-related factors on tissue mineral status in adolescent female athletes. Saliva and hair samples were used for the analysis of immediate and more permanent tissue mineral status, respectively. Samples taken from a control non-athletic female group and two groups of female athletes (field hockey and fencing) were analyzed for seven essential minerals: calcium, chromium, iron, potassium, magnesium, selenium and zinc. Inductively-coupled plasma mass spectrometry was used for the quantification of elements having very low concentration range in samples (Se, Cr and Zn) whereas inductively coupled plasma optical emission spectrometry was used for quantification of more ubiquitous elements (Mg, К, Са, Fe). The obtained results for athletic groups were compared with control. Female athletes had increased levels of selenium in both saliva and hair as well as chromium in saliva. Field hockey players had the higher level of zinc in hair whereas fencers had the lower levels of salivary calcium. Strong negative correlation between potassium levels in saliva and hair was identified. Iron and magnesium did not differ between the studied groups. In conclusion, novel sport-specific features of chromium tissue levels in female athletes were found. The studied sport disciplines have different impact on the distribution of osteoporosis-related minerals (calcium and zinc). Our finding can help in the development of osteoporosis preventive trainings and in the proper nutrient supplementation to correct mineral imbalances in female athletes.

Stochastic effects in biomolecular systems have now been recognized as a major physiologically and evolutionarily important factor in the development and function of many living organisms. Nevertheless, they are often thought of as providing only moderate refinements to the behaviors otherwise predicted by the classical deterministic system description. In this work we show by using both analytical and numerical investigation that at least in one ubiquitous class of (bio)chemical-reaction mechanisms, enzymatic futile cycles, the external noise may induce a bistable oscillatory (dynamic switching) behavior that is both quantitatively and qualitatively different from what is predicted or possible deterministically. We further demonstrate that the noise required to produce these distinct properties can itself be caused by a set of auxiliary chemical reactions, making it feasible for biological systems of sufficient complexity to generate such behavior internally. This new stochastic dynamics then serves to confer additional functional modalities on the enzymatic futile cycle mechanism that include stochastic amplification and signaling, the characteristics of which could be controlled by both the type and parameters of the driving noise. Hence, such noise-induced phenomena may, among other roles, potentially offer a novel type of control mechanism in pathways that contain these cycles and the like units. In particular, observations of endogenous or externally driven noise-induced dynamics in regulatory networks may thus provide additional insight into their topology, structure, and kinetics.

Background In most sequenced organisms the number of known regulatory genes (e.g., transcription factors (TFs)) vastly exceeds the number of experimentally-verified regulons that could be associated with them. At present, identification of TF regulons is mostly done through comparative genomics approaches. Such methods could miss organism-specific regulatory interactions and often require expensive and time-consuming experimental techniques to generate the underlying data. Results In this work, we present an efficient algorithm that aims to identify a given transcription factor’s regulon through inference of its unknown binding sites, based on the discovery of its binding motif. The proposed approach relies on computational methods that utilize gene expression data sets and knockout fitness data sets which are available or may be straightforwardly obtained for many organisms. We computationally constructed the profiles of putative regulons for the TFs LexA, PurR and Fur in E. coli K12 and identified their binding motifs. Comparisons with an experimentally-verified database showed high recovery rates of the known regulon members, and indicated good predictions for the newly found genes with high biological significance. The proposed approach is also applicable to novel organisms for predicting unknown regulons of the transcriptional regulators. Results for the hypothetical protein D d e0289 in D. alaskensis include the discovery of a Fis-type TF binding motif. Conclusions The proposed motif-based regulon inference approach can discover the organism-specific regulatory interactions on a single genome, which may be missed by current comparative genomics techniques due to their limitations. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0685-y) contains supplementary material, which is available to authorized users.

Motivation: Genome-wide fitness is an emerging type of high-throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behaviour, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. Previous works have shown that genome-wide fitness data can be used to uncover novel gene regulatory interactions, when compared with results of more conventional gene expression analysis. Yet, to date, few algorithms have been proposed for systematically using genome-wide mutant fitness data for gene regulatory network inference. Results: In this article, we describe a model and propose an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state–space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elucidated by estimating unknown model parameters. Unscented Kalman filter is used to cope with the non-linearities introduced in the model, which also enables the algorithm to run in on-line mode for practical use. Here, we demonstrate that the algorithm provides satisfying results for both synthetic data as well as empirical measurements of GAL network in yeast Saccharomyces cerevisiae and TyrR–LiuR network in bacteria Shewanella oneidensis. Availability: MATLAB code and datasets are available to download at http://www.duke.edu/∼lw174/Fitness.zip and http://genomics.lbl.gov/supplemental/fitness-bioinf/ Contact: wangx@ee.columbia.edu or mssamoilov@lbl.gov Supplementary information: Supplementary data are available at Bioinformatics online

Finding conserved motifs in genomic sequences represents one of essential bioinformatic problems. However, achieving high discovery performance without imposing substantial auxiliary constraints on possible motif features remains a key algorithmic challenge. This work describes BAMBI-a sequential Monte Carlo motif-identification algorithm, which is based on a position weight matrix model that does not require additional constraints and is able to estimate such motif properties as length, logo, number of instances and their locations solely on the basis of primary nucleotide sequence data. Furthermore, should biologically meaningful information about motif attributes be available, BAMBI takes advantage of this knowledge to further refine the discovery results. In practical applications, we show that the proposed approach can be used to find sites of such diverse DNA-binding molecules as the cAMP receptor protein (CRP) and Din-family site-specific serine recombinases. Results obtained by BAMBI in these and other settings demonstrate better statistical performance than any of the four widely-used profile-based motif discovery methods: MEME, BioProspector with BioOptimizer, SeSiMCMC and Motif Sampler as measured by the nucleotide-level correlation coefficient. Additionally, in the case of Din-family recombinase target site discovery, the BAMBI-inferred motif is found to be the only one functionally accurate from the underlying biochemical mechanism standpoint. C++ and Matlab code is available at http://www.ee.columbia.edu/~guido/BAMBI or http://genomics.lbl.gov/BAMBI/.

Uropathogenic Escherichia coli (UPEC) represent the predominant cause of urinary tract infections (UTIs). A key UPEC molecular virulence mechanism is type 1 fimbriae, whose expression is controlled by the orientation of an invertible chromosomal DNA element—the fim switch. Temperature has been shown to act as a major regulator of fim switching behavior and is overall an important indicator as well as functional feature of many urologic diseases, including UPEC host-pathogen interaction dynamics. Given this panoptic physiological role of temperature during UTI progression and notable empirical challenges to its direct in vivo studies, in silico modeling of corresponding biochemical and biophysical mechanisms essential to UPEC pathogenicity may significantly aid our understanding of the underlying disease processes. However, rigorous computational analysis of biological systems, such as fim switch temperature control circuit, has hereto presented a notoriously demanding problem due to both the substantial complexity of the gene regulatory networks involved as well as their often characteristically discrete and stochastic dynamics. To address these issues, we have developed an approach that enables automated multiscale abstraction of biological system descriptions based on reaction kinetics. Implemented as a computational tool, this method has allowed us to efficiently analyze the modular organization and behavior of the E. coli fimbriation switch circuit at different temperature settings, thus facilitating new insights into this mode of UPEC molecular virulence regulation. In particular, our results suggest that, with respect to its role in shutting down fimbriae expression, the primary function of FimB recombinase may be to effect a controlled down-regulation (rather than increase) of the ON-to-OFF fim switching rate via temperature-dependent suppression of competing dynamics mediated by recombinase FimE. Our computational analysis further implies that this down-regulation mechanism could be particularly significant inside the host environment, thus potentially contributing further understanding toward the development of novel therapeutic approaches to UPEC-caused UTIs., Author Summary Urinary tract infections (UTIs) represent a major growing threat to global public health. With over 15 million cases a year in the United States alone, UTIs are characterized by very high recurrence/reinfection rates, particularly among women and minority groups [1]. The predominant cause of UTIs is uropathogenic Escherichia coli (UPEC) bacteria, whose wide-spread and increasing antibiotic-resistance has made the development of alternative anti-UPEC treatments progressively more important and urgent. UPEC's foremost virulence factor is hair-like surface structures called type 1 fimbriae. Thus, one such potentially promising therapeutic approach may be to manipulate bacteria's own cellular circuitry toward inducing UPEC to turn off their fimbriae expression—rendering individual microbes benign. This task requires detailed understanding of molecular mechanisms involved, which may be significantly aided by in silico modeling. However, for UPEC fimbriation control circuit and many other systems, low-level all-inclusive quantitative models inevitably become too computationally demanding to remain practical, while high-level qualitative representations frequently prove inadequate owing to the substantial organizational and behavioral complexity of biological processes involved. We have developed an automated multiscale model abstraction methodology that helps address these problems by systematically generating intermediate-level representations that rigorously balance computational efficiency and modeling accuracy. Here, we use such an approach to examine how different temperature settings quantitatively affect UPEC transitions between fimbriate and afimbriate phases, to gain new understanding of the underlying modular circuit switch control logic, and to suggest further insights into ways this knowledge could potentially be used in therapeutic applications.

There are fundamental physical reasons why biochemical processes might be subject to noise and stochastic fluctuations. Indeed, it has long been understood that random molecular-scale mechanisms, such as those that drive genetic mutation, lie at the heart of population-scale evolutionary dynamics. What we can now appreciate is how stochastic fluctuations inherent in biochemical processes contribute to cellular and organismal phenotypes. Advancements in techniques for empirically measuring single cells and in corresponding theoretical methods have enabled the rigorous design and interpretation of experiments that provide incontrovertible proof that there are important endogenous sources of stochasticity that drive biological processes at the scale of individual organisms. Recently, some studies have progressed beyond merely ascertaining the presence of noise in biological systems; they trace its role in cellular physiology as it is passed through and processed by the biomolecular pathways—from the underlying origins of stochastic fluctuations in random biomolecular interactions to their ultimate manifestations in characteristic species phenotypes. These emerging results suggest new biological network design principles that account for a constructive role played by noise in defining the structure, function, and fitness of biological systems. They further show that stochastic mechanisms open novel classes of regulatory, signaling, and organizational choices that can serve as efficient and effective biological solutions to problems that are more complex, less robust, or otherwise suboptimal to deal with in the context of purely deterministic systems. Research in Drosophila melanogaster eye color-vision development and Bacillus subtilis competence induction has elegantly traced the role of noise in vital physiological processes from fluctuations to phenotypes, and is used here to highlight these developments.