Intervertebral Canals and Intracanal Ligaments as New Terms in Terminologia anatomica.

This study addresses the cervical part of the vertebral column. Clinical pictures of dystrophic diseases of the cervical part of the vertebral column do not always correspond only to the morphological changes-they may be represented by connective tissue formation and nerve and vessel compression. To find out the possible reason, this morphometric study of the cervical part of the vertebral column in 40 cadavers was performed. CT scans were performed on 17 cadaveric material specimens. A total of 12 histological samples of connective tissue structures located in intervertebral canals (IC) were studied. One such formation, an intracanal ligament (IL) located in the IC, was found. Today, there is no term "intervertebral canal", nor is there a detailed description of the intervertebral canal in the cervical part of the vertebral column. Cervical intervertebral canals make up five pairs in segments C2-C7. On cadavers, the IC lateral and medial apertures were 0.9-1.5 cm and 0.5-0.9 cm, correspondingly. According to our histological study, the connective tissue structures in the IC are ligaments-IL. According to the presence of these ligaments, ICs were classified into three types. Complete regional anatomy characterization of the IC of the cervical part of the vertebral column with a description of its constituent anatomical elements was provided. The findings demonstrate the need to include the terms "intervertebral canal" and "intervertebral ligament" in the Terminologia anatomica.

Surgical Treatment in Patients with Toxic Phosphorus Osteonecrosis of Facial Skull Middle Zone.

During the last few years, in the territory of the Russian Federation, the number of cases of toxic phosphoric osteonecrosis of the jaws has increased against the background of taking drugs of "artisanal" production (pervitin, desomorphin). The aim of our study was to increase the effectiveness of surgical treatment of patients with a diagnosis of toxic phosphorus necrosis of the maxilla. We performed a comprehensive treatment of patients with a history of drug addiction and the above diagnosis. Surgical intervention in the volume of complete resection of pathologically altered tissues and reconstructive techniques using local tissues and a replaced flap made it possible to achieve good aesthetic and functional results in the early and late postoperative period. Thus, our proposed method of surgical treatment can be used in similar clinical situations.

Structural Role of Plasma Membrane Sterols in Osmotic Stress Tolerance of Yeast Saccharomyces cerevisiae.

Yeast S. cerevisiae has been shown to suppress a sterol biosynthesis as a response to hyperosmotic stress. In the case of sodium stress, the failure to suppress biosynthesis leads to an increase in cytosolic sodium. The major yeast sterol, ergosterol, is known to regulate functioning of plasma membrane proteins. Therefore, it has been suggested that the suppression of its biosynthesis is needed to adjust the activity of the plasma membrane sodium pumps and channels. However, as the sterol concentration is in the range of thirty to forty percent of total plasma membrane lipids, it is believed that its primary biological role is not regulatory but structural. Here we studied how lowering the sterol content affects the response of a lipid bilayer to an osmotic stress. In accordance with previous observations, we found that a decrease of the sterol fraction increases a water permeability of the liposomal membranes. Yet, we also found that sterol-free giant unilamellar vesicles reduced their volume during transient application of the hyperosmotic stress to a greater extent than the sterol-rich ones. Furthermore, our data suggest that lowering the sterol content in yeast cells allows the shrinkage to prevent the osmotic pressure-induced plasma membrane rupture. We also found that mutant yeast cells with the elevated level of sterol accumulated propidium iodide when exposed to mild hyperosmotic conditions followed by hypoosmotic stress. It is likely that the decrease in a plasma membrane sterol content stimulates a drop in cell volume under hyperosmotic stress, which is beneficial in the case of a subsequent hypo-osmotic one.

Correction: Materials space of solid-state electrolytes: unraveling chemical composition-structure-ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches.

Correction for 'Materials space of solid-state electrolytes: unraveling chemical composition-structure-ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches' by Natalia Kireeva et al., Phys. Chem. Chem. Phys., 2017, 19, 20904-20918, DOI: 10.1039/c7cp00518k.

Materials space of solid-state electrolytes: unraveling chemical composition-structure-ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches.

The organic electrolytes of most current commercial rechargeable Li-ion batteries (LiBs) are flammable, toxic, and have limited electrochemical energy windows. All-solid-state battery technology promises improved safety, cycling performance, electrochemical stability, and possibility of device miniaturization and enables a number of breakthrough technologies towards the development of new high power and energy density microbatteries for electronics with low processing cost, solid oxide fuel cells, electrochromic devices, etc. Currently, rational materials design is attracting significant attention, which has resulted in a strong demand for methodologies that can accelerate the design of materials with tailored properties; cheminformatics can be considered as an efficient tool in this respect. This study was focused on several aspects: (i) identification of the parameters responsible for high Li-ion conductivity in garnet structured oxides; (ii) development of quantitative models to elucidate composition-structure-Li ionic conductivity relationships, taking into account the experimental details of sample preparation; (iii) circumscription of the materials space of solid garnet-type electrolytes, which is attractive for virtual screening. Several candidate compounds have been recommended for synthesis as potential solid state electrolyte materials.

Supervised extensions of chemography approaches: case studies of chemical liabilities assessment.

Chemical liabilities, such as adverse effects and toxicity, play a significant role in modern drug discovery process. In silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Herein, we propose an approach combining several classification and chemography methods to be able to predict chemical liabilities and to interpret obtained results in the context of impact of structural changes of compounds on their pharmacological profile. To our knowledge for the first time, the supervised extension of Generative Topographic Mapping is proposed as an effective new chemography method. New approach for mapping new data using supervised Isomap without re-building models from the scratch has been proposed. Two approaches for estimation of model's applicability domain are used in our study to our knowledge for the first time in chemoinformatics. The structural alerts responsible for the negative characteristics of pharmacological profile of chemical compounds has been found as a result of model interpretation.

Nonlinear dimensionality reduction for visualizing toxicity data: distance-based versus topology-based approaches.

Over the years, a number of dimensionality reduction techniques have been proposed and used in chemoinformatics to perform nonlinear mappings. In this study, four representatives of nonlinear dimensionality reduction methods related to two different families were analyzed: distance-based approaches (Isomap and Diffusion Maps) and topology-based approaches (Generative Topographic Mapping (GTM) and Laplacian Eigenmaps). The considered methods were applied for the visualization of three toxicity datasets by using four sets of descriptors. Two methods, GTM and Diffusion Maps, were identified as the best approaches, which thus made it impossible to prioritize a single family of the considered dimensionality reduction methods. The intrinsic dimensionality assessment of data was performed by using the Maximum Likelihood Estimation. It was observed that descriptor sets with a higher intrinsic dimensionality contributed maps of lower quality. A new statistical coefficient, which combines two previously known ones, was proposed to automatically rank the maps. Instead of relying on one of the best methods, we propose to automatically generate maps with different parameter values for different descriptor sets. By following this procedure, the maps with the highest values of the introduced statistical coefficient can be automatically selected and used as a starting point for visual inspection by the user.

Impact of distance-based metric learning on classification and visualization model performance and structure-activity landscapes.

This study concerns large margin nearest neighbors classifier and its multi-metric extension as the efficient approaches for metric learning which aimed to learn an appropriate distance/similarity function for considered case studies. In recent years, many studies in data mining and pattern recognition have demonstrated that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. The paper describes application of the metric learning approach to in silico assessment of chemical liabilities. Chemical liabilities, such as adverse effects and toxicity, play a significant role in drug discovery process, in silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Here, to our knowledge for the first time, a distance-based metric learning procedures have been applied for in silico assessment of chemical liabilities, the impact of metric learning on structure-activity landscapes and predictive performance of developed models has been analyzed, the learned metric was used in support vector machines. The metric learning results have been illustrated using linear and non-linear data visualization techniques in order to indicate how the change of metrics affected nearest neighbors relations and descriptor space.

Using self-organizing maps to accelerate similarity search.

While self-organizing maps (SOM) have often been used to map and describe chemical space, this paper focuses on their use to accelerate similarity searches based on vectors of high-dimensional real-value descriptors for which classical, binary fingerprint-based similarity speed-up procedures do not apply. Fuzzy tricentric pharmacophore (FPT) and ISIDA substructure counts are herein explored examples. Similarity search speed-up was achieved by positioning compounds on a SOM, then searching for analogues only in the neurons neighbouring the ones in which the query compounds reside. Smaller neighbourhood means shorter virtual screening (VS) time, but lower analogue retrieval rates. An enhancement criterion, conciliating the opposite trends is defined. It depends on map definition and build-up protocol (training set choice, map size, convergence criteria,…). The main goal is to discover and validate SOMs of optimal quality with respect to this criterion. Increasing the size of the training set beyond a certain limit is shown to be unnecessary and even detrimental, suggesting that one SOM built on a relatively small but diverse training set may be an effective VS enhancer of a much larger database. Also, using an excessively large number of training iterations may lead to over-fitting. Gradual training with en-route checking of VS enhancement propensity is the best strategy to follow. Maps were successfully challenged to accelerate the large-scale VS of 12,000 queries against 160,000 compounds, and shown to provide a meaningful mapping of activity-annotated compounds in chemical space.

The One-Class Classification Approach to Data Description and to Models Applicability Domain.

In this paper, we associate an applicability domain (AD) of QSAR/QSPR models with the area in the input (descriptor) space in which the density of training data points exceeds a certain threshold. It could be proved that the predictive performance of the models (built on the training set) is larger for the test compounds inside the high density area, than for those outside this area. Instead of searching a decision surface separating high and low density areas in the input space, the one-class classification 1-SVM approach looks for a hyperplane in the associated feature space. Unlike other reported in the literature AD definitions, this approach: (i) is purely "data-based", i.e. it assigns the same AD to all models built on the same training set, (ii) provides results that depend only on the initial descriptors pool generated for the training set, (iii) can be used for the huge number of descriptors, as well as in the framework of structured kernel-based approaches, e.g., chemical graph kernels. The developed approach has been applied to improve the performance of QSPR models for stability constants of the complexes of organic ligands with alkaline-earth metals in water.

Exhaustive QSPR studies of a large diverse set of ionic liquids: how accurately can we predict melting points?

Several popular machine learning methods--Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), modified version of the partial least-squares analysis (PLSM), backpropagation neural network (BPNN), and Multiple Linear Regression Analysis (MLR)--implemented in ISIDA, NASAWIN, and VCCLAB software have been used to perform QSPR modeling of melting point of structurally diverse data set of 717 bromides of nitrogen-containing organic cations (FULL) including 126 pyridinium bromides (PYR), 384 imidazolium and benzoimidazolium bromides (IMZ), and 207 quaternary ammonium bromides (QUAT). Several types of descriptors were tested: E-state indices, counts of atoms determined for E-state atom types, molecular descriptors generated by the DRAGON program, and different types of substructural molecular fragments. Predictive ability of the models was analyzed using a 5-fold external cross-validation procedure in which every compound in the parent set was included in one of five test sets. Among the 16 types of developed structure--melting point models, nonlinear SVM, ASNN, and BPNN techniques demonstrate slightly better performance over other methods. For the full set, the accuracy of predictions does not significantly change as a function of the type of descriptors. For other sets, the performance of descriptors varies as a function of method and data set used. The root-mean squared error (RMSE) of prediction calculated on independent test sets is in the range of 37.5-46.4 degrees C (FULL), 26.2-34.8 degrees C (PYR), 38.8-45.9 degrees C (IMZ), and 34.2-49.3 degrees C (QUAT). The moderate accuracy of predictions can be related to the quality of the experimental data used for obtaining the models as well as to difficulties to take into account the structural features of ionic liquids in the solid state (polymorphic effects, eutectics, glass formation).