COMPARATIVE ASSESSMENT OF REMOTE RESULTS OF NON-PHARMACOLOGICAL METHODS OF COMPLEX TREATMENT OF OBLITERATING ATHEROSCLEROSIS OF THE LOWER EXTREMITIES IN PATIENTS OF ELDERLY AND SENILE AGE

Aim - to estimate the remote results of treatment of obliterating atherosclerosis of arteries of the lower extremities of the II stage in patients of advanced and senile age after the combined use of ozone therapy and gravitational therapy. Materials and methods. A prospective randomized study in three parallel groups comprised 139 patients. The first group (n = 57) received standard medical therapy in combination with ozone therapy; the group was divided into two subgroups: the patients of subgroup 1a (n = 28) received intravenous ozonated physiological solution, in subgroup 1b (n = 29) - major ozonated autohemotherapy (MOA). Patients of the second group (n = 62) underwent comprehensive treatment, including gravitational therapy (GT) in addition to medical ozone. In this group we also identified two subgroups: subgroup 2a (n = 31), in which the patients received standard medical therapy in combination with ozonated physiological solution and gravitational therapy, and subgroup 2b (n = 31), in which the treatment was amplified by MOA and GT. The third group was the control group (n = 20) that included patients who received only standard medical therapy. Dynamics of changes of disease stages and the number of surgeries in the remote period was estimated (up to 7 years). Results. After 6 months of follow-up observation, the distribution of the disease stages among the patients did not differ significantly (p> 0.05) from the initial amount. Analysis of survival and probable risks after 7 years of follow-up by Cox regression method in relation to the applied method of treatment revealed maximum efficiency of combined treatment in subgroup 2a. Conclusion. As a result of complex conservative treatment, including ozonotherapy and gravitational therapy, the probability of surgical interventions as well as disease stage advancing were effectively reduced

Visualization of Nd3+-doped Laf3 Nanoparticles For Near Infrared Bioimaging via Upconversion Luminescence at Multiphoton Excitation Microscopyvisualization of Nd3+-doped Laf3 Nanoparticles For Near Infrared Bioimaging via Upconversion Luminescence at Multiphoton Excitation Microscopy

Recent developments in the field of biophotonics facilitate the raise of interest to inorganic nanoparticles (NPs) doped with Nd 3+ ions, because of their near-infrared (NIR) absorption. These NPs are interesting bioimaging probes for deep tissue visualization, while they can also act as local thermometers in biological tissues. Despite the good possibilities for visualization of NPs with Nd 3+ ions in NIR spectral range, difficulties arise when studying the cellular uptake of these NPs using commercially available fluorescence microscopy systems, since the selection of suitable luminescence detectors is limited. However, Nd 3+ ions are able to convert NIR radiation into visible light, showing upconversion properties. In this paper we found optimal parameters to excite upconversion luminescence of Nd 3+ :LaF 3 NPs in living cells and to compare the distribution of the NPs inside the cell culture of human macrophages THP-1 obtained by two methods. Firstly, by detecting the upconversion luminescence of the NPs in VIS under NIR multiphoton excitation using laser scanning confocal microscopy and secondly, using transmission electron microscopy

On the effects of mechanical stress of biological membranes in modeling of swelling dynamics of biological systems

We highlight mechanical stretching and bending of membranes and the importance of membrane deformations in the analysis of swelling dynamics of biological systems, including cells and subcellular organelles. Membrane deformation upon swelling generates tensile stress and internal pressure, contributing to volume changes in biological systems. Therefore, in addition to physical (internal/external) and chemical factors, mechanical properties of the membranes should be considered in modeling analysis of cellular swelling. Here we describe an approach that considers mechanical properties of the membranes in the analysis of swelling dynamics of biological systems. This approach includes membrane bending and stretching deformations into the model, producing a more realistic description of swelling. We also discuss the effects of membrane stretching on swelling dynamics. We report that additional pressure generated by membrane bending is negligible, compared to pressures generated by membrane stretching, when both membrane surface area and volume are variable parameters. Note that bending deformations are reversible, while stretching deformation may be irreversible, leading to membrane disruption when they exceed a certain threshold level. Therefore, bending deformations need only be considered in reversible physiological swelling, whereas stretching deformations should also be considered in pathological irreversible swelling. Thus, the currently proposed approach may be used to develop a detailed biophysical model describing the transition from physiological to pathological swelling mode.National Aeronautics & Space Administration (NASA):80NSSC19M0049; PR Space Grant (NASA):NNX15AI11Hinfo:eu-repo/semantics/publishedVersio

A Survey on the Krein-von Neumann Extension, the corresponding Abstract Buckling Problem, and Weyl-Type Spectral Asymptotics for Perturbed Krein Laplacians in Nonsmooth Domains

In the first (and abstract) part of this survey we prove the unitary equivalence of the inverse of the Krein--von Neumann extension (on the orthogonal complement of its kernel) of a densely defined, closed, strictly positive operator, $S\geq \varepsilon I_{\mathcal{H}}$ for some $\varepsilon >0$ in a Hilbert space $\mathcal{H}$ to an abstract buckling problem operator. This establishes the Krein extension as a natural object in elasticity theory (in analogy to the Friedrichs extension, which found natural applications in quantum mechanics, elasticity, etc.). In the second, and principal part of this survey, we study spectral properties for $H_{K,\Omega}$, the Krein--von Neumann extension of the perturbed Laplacian $-\Delta+V$ (in short, the perturbed Krein Laplacian) defined on $C^\infty_0(\Omega)$, where $V$ is measurable, bounded and nonnegative, in a bounded open set $\Omega\subset\mathbb{R}^n$ belonging to a class of nonsmooth domains which contains all convex domains, along with all domains of class $C^{1,r}$, $r>1/2$.Comment: 68 pages. arXiv admin note: extreme text overlap with arXiv:0907.144

Prediction of pathological stage in patients with prostate cancer: a neuro-fuzzy model

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582)

Modular Mass Spectrometric Tool for Analysis of Composition and Phosphorylation of Protein Complexes

The combination of high accuracy, sensitivity and speed of single and multiple-stage mass spectrometric analyses enables the collection of comprehensive sets of data containing detailed information about complex biological samples. To achieve these properties, we combined two high-performance matrix-assisted laser desorption ionization mass analyzers in one modular mass spectrometric tool, and applied this tool for dissecting the composition and post-translational modifications of protein complexes. As an example of this approach, we here present studies of the Saccharomyces cerevisiae anaphase-promoting complexes (APC) and elucidation of phosphorylation sites on its components. In general, the modular concept we describe could be useful for assembling mass spectrometers operating with both matrix-assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) ion sources into powerful mass spectrometric tools for the comprehensive analysis of complex biological samples

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases