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.

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

Several candidate garnet-related compounds have been recommended for synthesis as potential materials for solid-state electrolytes.

Towards in silico identification of the human ether-a-go-go-related gene channel blockers: discriminative vs. generative classification models

HERG potassium channels have a critical role in the normal electrical activity of the heart. The blockade of hERG channels in heart cells can result in a potentially fatal disorder called long QT syndrome. HERG channels can be blocked by compounds with diverse structures belonging to several drug classes. Presented herein are generative (Generative Topographic Maps) and discriminative (Support Vector Machines) classification models to categorize the compounds in silico into active and inactive classes by using different types of descriptors. The predictive performance of discriminative and generative classification models has been compared. Here, the possibility of using Generative Topographic Maps as an approach for applicability domain analysis and to generate probability-based descriptors was demonstrated to our knowledge for the first time. Comparison of obtained results with the models developed by other teams on the same data set has been performed.

Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure-Activity Modeling and Dataset Comparison

Here, the utility of Generative Topographic Maps (GTM) for data visualization, structure-activity modeling and database comparison is evaluated, on hand of subsets of the Database of Useful Decoys (DUD). Unlike other popular dimensionality reduction approaches like Principal Component Analysis, Sammon Mapping or Self-Organizing Maps, the great advantage of GTMs is providing data probability distribution functions (PDF), both in the high-dimensional space defined by molecular descriptors and in 2D latent space. PDFs for the molecules of different activity classes were successfully used to build classification models in the framework of the Bayesian approach. Because PDFs are represented by a mixture of Gaussian functions, the Bhattacharyya kernel has been proposed as a measure of the overlap of datasets, which leads to an elegant method of global comparison of chemical libraries.

Computer-aided design of new metal binders

Chemoinformatics approaches open new opportunities for computer-aided design of new efficient metal binders. Here, we demonstrate performances of ISIDA and COMET software tools to predict stability constants (log K) of the metal ion/organic ligand complexes in solution and to design in silico new molecules possessing desirable properties. The predictive models for log K of lanthanides complexation in water have been developed. Some new uranyl binders based on monoamides and on phosphoryl-containing podands were suggested theoretically, then synthesized and tested experimentally. Reasonable agreement between experimental uranyl distribution coefficients and theoretically predicted values has been observed.

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).