The Pterygomandibular Space: A Volumetric Evaluation Using the Novel A-Silicone Injections Method.

Inferior alveolar nerve block (IANB) is one of the most common procedures in operative dentistry, and a deep understanding of the normal anatomical variation of the pterygomandibular space (PM) is essential for its safe and successful administration. This cadaveric anatomical study aimed to use A-silicone injections to evaluate the volume of the PM. This study was conducted using 46 human cadaver heads (25 males and 21 females).

Intervertebral Canals and Intracanal Ligaments as New Terms in .

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.

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.

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

Yeast 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.

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.

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.

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.

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.

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.

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.

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.

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.