Prediction and prenosological diagnostics of heart diseases based on energy characteristics of acupuncture points and fuzzy logic

Many theories of reflexology use ancient concepts which do not coincide with the modern medical terminology of anatomy, physiology and biophysics. This substantially reduces the trust of physicians in reflexology methods. During this research, several mathematical models for the interaction of the internal and biological active points of meridian structures have been proposed. The analysis of these models allows the specification of a list of heart diseases for which reflex diagnostics and reflex therapy methods are most effective and also allows increasing the effectiveness of these procedures. It is shown that good results for the prediction and early diagnosis of diseases from the reaction energy of biologically active points (acupuncture points) are obtained using fuzzy logic decision making.     

Prediction and prenosological diagnostics of heart diseases based on energy characteristics of acupuncture points and fuzzy logic

Many theories of reflexology use ancient concepts which do not coincide with the modern medical terminology of anatomy, physiology and biophysics. This substantially reduces the trust of physicians in reflexology methods. During this research, several mathematical models for the interaction of the internal and biological active points of meridian structures have been proposed. The analysis of these models allows the specification of a list of heart diseases for which reflex diagnostics and reflex therapy methods are most effective and also allows increasing the effectiveness of these procedures. It is shown that good results for the prediction and early diagnosis of diseases from the reaction energy of biologically active points (acupuncture points) are obtained using fuzzy logic decision making.     

Experimental study of displays in contralateral acoustic reflex auditory stimulation

The results of an experimental study of manifestations of the acoustic reflex with contralateral auditory stimulation at a frequency of 1 kHz are presented, and the principal possibility and informativeness of its use for diagnosing the diseases of the organ of hearing are demonstrated. The principal difference of the developed approach is the use of polyharmonic signal for measuring acoustic reflex manifestations during contralateral stimulation, which allows accelerating the examination procedure.     

A Method of Estimation of Change Points in Multiphasic Growth Models

The least-squares estimation (LSE) of change points and the phase parameters is considered for two growth models with piecewise continuously connected exponential and stationary phases. The calculation of a LSE is reduced to the solution of a finite number of simpler least-squares problems for which methods of calculation are well known.     

Direct and Model Free Calculation of Force-Dependent Dissociation Rates from Force Spectroscopic Data

Force spectroscopy allows testing the free energy landscapes of molecular interactions. Usually, the dependency of the most probable rupture force on the force rate or the shape of the rupture force histogram is fitted with different models that contain approximations and basic assumptions. We present a simple and model free approach to extract the force-dependent dissociation rates directly from the force curve data. Simulations show that the dissociation rates at any force are given directly by the ratio of the number of detected rupture events to the time this force was acting on the bond. To calculate these total times of acting forces, all force curve data points of all curves measured are taken into account, which significantly increases the amount of information which is considered for data analysis compared to other methods. Moreover, by providing force-dependent dissociation rates this method allows direct testing and validating of any energy landscape model.     

Semi‐Markov Models with Phase‐Type Sojourn Distributions

Summary Continuous‐time multistate models are widely used for categorical response data, particularly in the modeling of chronic diseases. However, inference is difficult when the process is only observed at discrete time points, with no information about the times or types of events between observation times, unless a Markov assumption is made. This assumption can be limiting as rates of transition between disease states might instead depend on the time since entry into the current state. Such a formulation results in a semi‐Markov model. We show that the computational problems associated with fitting semi‐Markov models to panel‐observed data can be alleviated by considering a class of semi‐Markov models with phase‐type sojourn distributions. This allows methods for hidden Markov models to be applied. In addition, extensions to models where observed states are subject to classification error are given. The methodology is demonstrated on a dataset relating to development of bronchiolitis obliterans syndrome in post‐lung‐transplantation patients.     

Analysis of Nonlinear Gene Expression Progression Reveals Extensive Pathway and Age-Specific Transitions in Aging Human Brains

Several recent gene expression studies identified hundreds of genes that are correlated with age in brain and other tissues in human. However, these studies used linear models of age correlation, which are not well equipped to model abrupt changes associated with particular ages. We developed a computational algorithm for age estimation in which the expression of each gene is treated as a dichotomized biomarker for whether the subject is older or younger than a particular age. In addition, for each age-informative gene our algorithm identifies the age threshold with the most drastic change in expression level, which allows us to associate genes with particular age periods. Analysis of human aging brain expression datasets from three frontal cortex regions showed that different pathways undergo transitions at different ages, and the distribution of pathways and age thresholds varies across brain regions. Our study reveals age-correlated expression changes at particular age points and allows one to estimate the age of an individual with better accuracy than previously published methods.     

The effects of foot reflexology on pain and physiological indicators in children with leukemia under chemotherapy: a clinical trial study

BackgroundFoot reflexology is a popular complementary medicine method; however, previous studies have shown conflicting results. This study aimed to investigate the impact of foot reflexology on pain and physiological responses caused by intrathecal injection of chemotherapy drugs in children with leukemia.Materials and methodsThis clinical trial included 80 children with leukemia. The participants received 20 min foot reflexology (10 min each foot). The primary measured outcomes included pain and physiological indicators (blood pressure and heart rate).ResultsThe results showed that foot reflexology had a significant effect on reducing pain (p = 0.002) and improving physiological indicators [blood pressure (p = 0.002) and heart rate (p = 0.003)].ConclusionBased on the results of the present study, which shows the positive effect of foot reflexology on the improvement of pain and physiological indicators, foot reflexology can be used as a complementary treatment along with conventional therapies.     

Genome editing methods in animal models

ABSTRACT

Genetically engineered animal models that reproduce human diseases are very important for the pathological study of various conditions. The development of the clustered regularly interspaced short palindromic repeats (CRISPR) system has enabled a faster and cheaper production of animal models compared with traditional gene-targeting methods using embryonic stem cells. Genome editing tools based on the CRISPR-Cas9 system are a breakthrough technology that allows the precise introduction of mutations at the target DNA sequences. In particular, this accelerated the creation of animal models, and greatly contributed to the research that utilized them. In this review, we introduce various strategies based on the CRISPR-Cas9 system for building animal models of human diseases and describe various in vivo delivery methods of CRISPR-Cas9 that are applied to disease models for therapeutic purposes. In addition, we summarize the currently available animal models of human diseases that were generated using the CRISPR-Cas9 system and discuss future directions.     

Mathematical modelling of neuronal dendritic branching patterns in two dimensions: application to retinal ganglion cells in the cat and rat

Sholl’s analysis has been used for about 50years to study neuron branching characteristics based on a linear, semi-log or log—log method. Using the linear two- dimensional Sholl’s method, we call attention to a relationship between the number of intersections of neuronal dendrites with a circle and the numbers of branching points and terminal tips encompassed by the circle, with respect to the circle radius. For that purpose, we present a mathematical model, which incorporates a supposition that the number of dendritic intersections with a circle can be resolved into two components: the number of branching points and the number of terminal tips within the annulus of two adjoining circles. The numbers of intersections and last two sets of data are also presented as cumulative frequency plots and analysed using a logistic model (Boltzmann’s function). Such approaches give rise to several new morphometric parameters, such as, the critical, maximal and mean values of the numbers of intersections, branching points and terminal tips, as well as the abscissas of the inflection points of the corresponding sigmoid plots, with respect to the radius. We discuss these parameters as an additional tool for further morphological classification schemes of vertebrate retinal ganglion cells. To test the models, we apply them first to three groups of morphologically different cat’s retinal ganglion cells (the alpha, gamma and epsilon cells). After that, in order to quantitatively support the classification of the rat’s alpha cells into the inner and outer cells, we apply our models to two subgroups of these cells grouped according to their stratification levels in the inner plexiform layer. We show that differences between most of our parameters calculated for these subgroups are statistically significant. We believe that these models have the potential to aid in the classification of biological images.     

Pathway-based Analysis Tools for Complex Diseases:A Review

Genetic studies are traditionally based on single-gene analysis. The use of these analyses can pose tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis provides a technique, which allows a comprehen- sive understanding of the molecular mechanisms underlying complex diseases. Extensive studies uti- lizing the methods and applications for pathway-based analysis have significantly advanced our capacity to explore large-scale omics data, which has rapidly accumulated in biomedical fields. This article is a comprehensive review of the pathway-based analysis methods the powerful methods with the potential to uncover the biological depths of the complex diseases. The general concepts and procedures for the pathway-based analysis methods are introduced and then, a comprehensive review of the major approaches for this analysis is presented. In addition, a list of available path- way-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are dis- cussed. This review will provide a useful guide to dissect complex diseases.     

Cultural learning and the Clovis colonization of North America

The timing of the earliest colonization of North America is debatable, but what is not at issue is the point of origin of the early colonists: Humans entered the continent from Beringia and then made their way south along or near the Pacific Coast and/or through a corridor that ran between the Cordilleran and Laurentide ice sheets in western North America. At some point, they abandoned their Arctic‐based tool complex for one more adapted to an entirely different environment. That new techno‐complex is termed “Clovis”; its dispersal allows us to examine, at a fine scale, how colonization processes played out across a vast continent that at the time had, at best, a very small resident population. Clovis has figured prominently in American archeology since the first Clovis points were identified in eastern New Mexico in the 1930s. However, the successful marriage of learning models grounded in evolutionary theory and modern analytical methods that began roughly a decade ago has begun to pay significant dividends in terms of what we know about the rapid spread of human groups across the last sizable landmass to witness human occupation.     

Stereotaxic Microinjection of Viral Vectors Expressing Cre Recombinase to Study the Role of Target Genes in Cocaine Conditioned Place Preference

Microinjecting recombinant adenoassociated viral (rAAV) vectors expressing Cre recombinase into distinct mouse brain regions to selectively knockout genes of interest allows for enhanced temporally- and regionally-specific control of gene deletion, compared to existing methods. While conditional deletion can also be achieved by mating mice that express Cre recombinase under the control of specific gene promoters with mice carrying a floxed gene, stereotaxic microinjection allows for targeting of discrete brain areas at experimenter-determined time points of interest. In the context of cocaine conditioned place preference, and other cocaine behavioral paradigms such as self-administration or psychomotor sensitization that can involve withdrawal, extinction and/or reinstatement phases, this technique is particularly useful in exploring the unique contribution of target genes to these distinct phases of behavioral models of cocaine-induced plasticity. Specifically, this technique allows for selective ablation of target genes during discrete phases of a behavior to test their contribution to the behavior across time. Ultimately, this understanding allows for more targeted therapeutics that are best able to address the most potent risk factors that present themselves during each phase of addictive behavior.     

A review and critique of some models used in competing risk analysis.

We have introduced a notation which allows one to define competing risk models easily and to examine underlying assumptions. We have treated the actuarial model for competing risk in detail, comparing it with other models and giving useful variance formulae both for the case when times of death are available and for the case when they are not. The generality of these methods is illustrated by an example treating two dependent competing risks.     

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

Background  

Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal.     

High yield bacterial expression of active c-Abl and c-Src tyrosine kinases

The Abl and Src tyrosine kinases are key signaling proteins that are of considerable interest as drug targets in cancer and many other diseases. The regulatory mechanisms that control the activity of these proteins are complex, and involve large-scale conformational changes in response to phosphorylation and other modulatory signals. The success of the Abl inhibitor imatinib in the treatment of chronic myelogenous leukemia has shown the potential of kinase inhibitors, but the rise of drug resistance in patients has also shown that drugs with alternative modes of binding to the kinase are needed. The detailed understanding of mechanisms of protein-drug interaction and drug resistance through biophysical methods demands a method for the production of active protein on the milligram scale. We have developed a bacterial expression system for the kinase domains of c-Abl and c-Src, which allows for the quick expression and purification of active wild-type and mutant kinase domains by coexpression with the YopH tyrosine phosphatase. This method makes practical the use of isotopic labeling of c-Abl and c-Src for NMR studies, and is also applicable for constructs containing the SH2 and SH3 domains of the kinases.     

Tumor micro-ecology and competitive interactions

Three nested models describing the growth of individual subpopulations in a heterogeneous environment are described. The models represent the dynamics of two populations which compete, to varying degrees, for common resources. The first model describes growth in a totally non-competitive micro-environment, the second model describes an ecology in which competition is proportional to competitor population size, and the third model ecology extends the model described by Jansson & Revesz (1974), which allows one population to emerge from the other. The critical points for each model are defined using the isoclines derived from the Ordinary Differential Equations (ODE's) describing competitive growth. The critical points for each model are characterized by the signs of the eigenvalues of the variational matrix at each point. The theoretical results of the analysis show that a competitive model ecology with Verhulstian logistics allows four critical points: the origin which is a repeller, two competitive exclusion points, and an equilibrium state (Waltman, 1983). The extended model ecology of Jansson & Revesz (1974), allows three critical points: the origin which is a repeller, competitive exclusion of the first population, and an equilibrium point. Data from a human adenocarcinoma of the colon and murine mammary tumors are used as qualitative measures of the dynamics of the three micro-ecologies. Issues such as stochastic extension to model small populations either for clonal extinction or heterogeneous emergence are discussed.     

Immunology of fungal infections: lessons learned from animal models

The continuing AIDS epidemic coupled with increased usage of immunosuppressive drugs to prevent organ rejection or treat autoimmune diseases has resulted in an increase in individuals at risk for acquiring fungal diseases. These concerns highlight the need to elucidate mechanisms of inducing protective immune responses against fungal pathogens. Consequently, several experimental models of human mycoses have been developed to study these diseases. The availability of transgenic animal models allows for in-depth analysis of specific components, receptors, and signaling pathways that elicit protection against fungal diseases. This review focuses on recent advances in our understanding of immune responses to fungal infections gained using animal models.     

Experimental Evaluation of Suitability of Selected Multi-Criteria Decision-Making Methods for Large-Scale Agent-Based Simulations

Multi-criteria decision-making (MCDM) can be formally implemented by various methods. This study compares suitability of four selected MCDM methods, namely WPM, TOPSIS, VIKOR, and PROMETHEE, for future applications in agent-based computational economic (ACE) models of larger scale (i.e., over 10 000 agents in one geographical region). These four MCDM methods were selected according to their appropriateness for computational processing in ACE applications. Tests of the selected methods were conducted on four hardware configurations. For each method, 100 tests were performed, which represented one testing iteration. With four testing iterations conducted on each hardware setting and separated testing of all configurations with the–server parameter de/activated, altogether, 12800 data points were collected and consequently analyzed. An illustrational decision-making scenario was used which allows the mutual comparison of all of the selected decision making methods. Our test results suggest that although all methods are convenient and can be used in practice, the VIKOR method accomplished the tests with the best results and thus can be recommended as the most suitable for simulations of large-scale agent-based models.