Клинический опыт успешного хирургического лечения гигантской метастатической саркомы легкого

Introduction. Sarcomas refer to a group of heterogeneous non-epithelial malignant tumors originating from connective tissue. These tumors are characterized by extremely aggressive local growth, relatively low incidence of lymphogenic metastases, predominant and early hematogenic generalization. These tumors most oft en metastasize to the lungs.Materials and methods. The paper describes a case of successful surgical treatment for a giant retroperitoneal fibrosarcoma metastasis to the lung.Results. Successful treatment for sarcoma depends primarily on a global understanding by oncologists of the complex pathogenesis, histological forms and principles of comprehensive treatment for this complex, polymorphic group of malignant pathologies. The earlier the primary site is diagnosed, the sooner and more definitely the surgical treatment is performed, and the more correctly the drug therapy is carried out, the better the result of the complex treatment approach. Similarly, sarcoma secondary sites should be treated proactively – without waiting until their size and local spread contraindicate surgical treatment. Conclusion. Neglected sarcoma cases indicate the need for combined and extended surgical interventions, one successful example of which is described in this paper.Введение. Саркомы – группа гетерогенных неэпителиальных злокачественных опухолей, происходящих из соединительной ткани. Данные опухоли характеризуются крайне агрессивным местным ростом, относительно малой частотой лимфогенных метастазов, преимущественной и ранней гематогенной генерализацией. Наиболее часто эти опухоли метастазируют в легкие.Материалы и методы. В статье описан случай успешного хирургического лечения гигантского метастаза фибросаркомы забрюшинного пространства в легкое.Результаты. Успех лечения больных саркомами зависит прежде всего от глобального понимания врачами-онкологами сложных вопросов патогенеза, гистологических форм и принципов комплексной терапии этой сложной, полиморфной группы злокачественной патологии. Чем более рано диагностирован первичный очаг, чем скорее и радикальнее выполнено хирургическое лечение и чем более верно проведена лекарственная терапия, тем лучше результат комплексного лечебного подхода. Аналогичным образом следует действовать и при вторичных очагах саркомы, не дожидаясь момента, когда их размеры и местное распространение будут являться противопоказанием к хирургическому лечению.Заключение. Запущенные случаи сарком диктуют необходимость комбинированных и расширенных хирургических вмешательств, один из успешных примеров которых описан в данной статье

An optimal transient growth of small perturbations in thin gaseous discs

A thin gaseous disc with an almost keplerian angular velocity profile, bounded by a free surface and rotating around point-mass gravitating object is nearly spectrally stable. Despite that the substantial transient growth of linear perturbations measured by the evolution of their acoustic energy is possible. This fact is demonstrated for the simple model of a non-viscous polytropic thin disc of a finite radial size where the small adiabatic perturbations are considered as a linear combination of neutral modes with a corotational radius located beyond the outer boundary of the flow.Comment: 15 pages, 5 figures, accepted for publication in Ast

Radial Sizing of Lipid Nanotubes Using Membrane Displacement Analysis

We report a novel method for the measurement of lipid nanotube radii. Membrane translocation is monitored between two nanotube-connected vesicles, during the expansion of a receiving vesicle, by observing a photobleached region of the nanotube. We elucidate nanotube radii, extracted from SPE vesicles, enabling quantification of membrane composition and lamellarity. Variances of nanotube radii were measured, showing a growth of 40-56 nm, upon increasing cholesterol content from 0 to 20%

A Comparative Computer Simulation of Dendritic Morphology

Computational modeling of neuronal morphology is a powerful tool for understanding developmental processes and structure-function relationships. We present a multifaceted approach based on stochastic sampling of morphological measures from digital reconstructions of real cells. We examined how dendritic elongation, branching, and taper are controlled by three morphometric determinants: Branch Order, Radius, and Path Distance from the soma. Virtual dendrites were simulated starting from 3,715 neuronal trees reconstructed in 16 different laboratories, including morphological classes as diverse as spinal motoneurons and dentate granule cells. Several emergent morphometrics were used to compare real and virtual trees. Relating model parameters to Branch Order best constrained the number of terminations for most morphological classes, except pyramidal cell apical trees, which were better described by a dependence on Path Distance. In contrast, bifurcation asymmetry was best constrained by Radius for apical, but Path Distance for basal trees. All determinants showed similar performance in capturing total surface area, while surface area asymmetry was best determined by Path Distance. Grouping by other characteristics, such as size, asymmetry, arborizations, or animal species, showed smaller differences than observed between apical and basal, pointing to the biological importance of this separation. Hybrid models using combinations of the determinants confirmed these trends and allowed a detailed characterization of morphological relations. The differential findings between morphological groups suggest different underlying developmental mechanisms. By comparing the effects of several morphometric determinants on the simulation of different neuronal classes, this approach sheds light on possible growth mechanism variations responsible for the observed neuronal diversity

How Structure Determines Correlations in Neuronal Networks

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks

Independent EEG Sources Are Dipolar

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison)

Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex

Visually induced neuronal activity in V1 displays a marked gamma-band component which is modulated by stimulus properties. It has been argued that synchronized oscillations contribute to these gamma-band activity [... however,] even when oscillations are observed, they undergo temporal decorrelation over very few cycles. This is not easily accounted for in previous network modeling of gamma oscillations. We argue here that interactions between cortical layers can be responsible for this fast decorrelation. We study a model of a V1 hypercolumn, embedding a simplified description of the multi-layered structure of the cortex. When the stimulus contrast is low, the induced activity is only weakly synchronous and the network resonates transiently without developing collective oscillations. When the contrast is high, on the other hand, the induced activity undergoes synchronous oscillations with an irregular spatiotemporal structure expressing a synchronous chaotic state. As a consequence the population activity undergoes fast temporal decorrelation, with concomitant rapid damping of the oscillations in LFPs autocorrelograms and peak broadening in LFPs power spectra. [...] Finally, we argue that the mechanism underlying the emergence of synchronous chaos in our model is in fact very general. It stems from the fact that gamma oscillations induced by local delayed inhibition tend to develop chaos when coupled by sufficiently strong excitation.Comment: 49 pages, 11 figures, 7 table

Micro-connectomics: probing the organization of neuronal networks at the cellular scale.

Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.This work was supported by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Nature Publishing Group

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)