Association of Increased Homocysteine Levels with Impaired Folate Metabolism and Vitamin B Deficiency in Early-Onset Multiple Sclerosis.

The contents of homocysteine (HCy), cyanocobalamin (vitamin B12), folic acid (vitamin B9), and pyridoxine (vitamin B6) were analyzed and the genotypes of the main gene polymorphisms associated with folate metabolism (C677T and A1298C of the MTHFR gene, A2756G of the MTR gene and A66G of the MTRR gene) were determined in children at the onset of multiple sclerosis (MS) (with disease duration of no more than six months), healthy children under 18 years (control group), healthy adults without neurological pathology, adult patients with MS at the onset of disease, and adult patients with long-term MS. A significant increase in the HCy levels was found in children at the MS onset compared to healthy children of the corresponding age. It was established that the content of HCy in children has a high predictive value. At the same time, an increase in the HCy levels was not accompanied by the deficiency of vitamins B6, B9, and B12 in the blood. The lack of correlation between the laboratory signs of vitamin deficiency and HCy levels may be due to the polymorphic variants of folate cycle genes. An increased HCy level should be considered as a marker of functional disorders of folate metabolism accompanying the development of pathological process in pediatric MS. Our finding can be used to develop new approaches to the prevention of demyelination in children and treatment of pediatric MS.

Oxygen profiles and oxygen consumption in the isolated mouse retina.

The retina has a large demand for oxygen, but there is only limited information on differences between oxygen utilization (QO2) in the inner and outer retina, and limited data on mouse, which has become a prevalent animal model. This study utilized the isolated mouse retina, which allowed more detailed spatial analysis of QO2 than other methods. Oxygen sensitive microelectrodes were used to obtain profiles of oxygen tension across the isolated mouse retina, and mathematical models of retinal oxygen diffusion with four and five layers were fitted to the data to obtain values for QO2 of the outer retina (QOR) and inner retina (QIR). The boundaries between layers were free parameters in these models. The five-layer model resulted in lower error between the model and data, and agreed better with known anatomy. The three layers for the outer retina occupied half of the retina, as in prior work on rat, cat, and monkey, and the inner half of the retina could be divided into two layers, in which the one closer to the vitreous (layer 5) had much lower QO2 than the more distal inner retina (layer 4). QIR in darkness was 3.9 ml O2-100 g-1-min-1, similar to the value for intact cat retina, and did not change during light. QOR in darkness was 2.4 ml O2-100 g-1-min-1, lower than previous values in cat and rat, possibly because of damage to photoreceptors during isolation. There was a tendency for QOR to be lower in light, but it was not significant in this preparation.

Prediction of Adverse Effects of Drug-Drug Interactions on Cardiovascular System Based on the Analysis of Structure-Activity Relationships.

Co-administration of drugs often leads to drug-drug interactions, which could be accompanied by various adverse drug reactions that pose a threat to life and health of the patient. The effect caused by adverse drug reactions on cardiovascular system is one of the most significant manifestations of drug-drug interaction. Clinical assessment of adverse drug reactions resulting from drug-drug interaction between all drug pairs used in therapeutic practice is not possible. The purpose of this work was to build models using structure-activity analysis to predict adverse effects of drugs on cardiovascular system, mediated by pairwise interactions between the drug pairs when they are taken together. Data on the adverse effects resulting from drug-drug interaction were obtained from the DrugBank database. The data on drug pairs that do not cause such effects, which are necessary for building accurate structure-activity models, were obtained from the TwoSides database, which contains the results of analysis of the spontaneous reports. Two types of descriptors were used to describe a pair of drug structures: PoSMNA descriptors and probabilistic estimates of the prediction of biological activities obtained using the PASS program. Structure-activity relationships were established using the Random Forest method. Prediction accuracy was calculated by means of five-fold cross-validation. The highest accuracy values were obtained using PASS probabilistic estimates as descriptors. The area under the ROC curve was 0.94 for bradycardia, 0.96 for tachycardia, 0.90 for arrhythmia, 0.90 for ECG QT prolongation, 0.91 for hypertension, 0.89 for hypotension.

Open and Percutaneous Trigeminal Nucleotractotomy: A Case Series and Literature Review.

INTRODUCTION: Nucleotractotomy is an efficient surgical technique that provides a high pain relief rate for specific clinical indications. There are two main approaches for performing this operation: an open and percutaneous technique. METHODS: In the Federal Center of Neurosurgery (Novosibirsk, Russia) from 2016 to 2022, 13 trigeminal nucleotractotomies (7 open and 6 percutaneous) were performed in 12 patients (5 women and 7 men). The indications for surgery were deafferentation pain and chronic drug-resistant pain syndrome caused by malignancy in the facial region. A neurological examination was done on each patient 1 day before the surgery, right after the surgery, and at the follow-up (examinations were done after 1, 6, and 12 months, or when the patient independently applied to our hospital). In the early postoperative period, patients underwent brain MRI. RESULTS: The average pain intensity score before nucleotractotomy on the 11-point (0-10) visual analog scale (VAS) was 9.3. The effectiveness of open interventions was somewhat higher; the average VAS score in the early postoperative period for the open technique was 1.57, in the group of patients who underwent percutaneous nucleotractotomy were 2.66. Complete regression of the pain syndrome was achieved in 6 patients; in 5 patients, the pain in the face decreased by more than 50%. One case had an unsatisfactory outcome. In the open-surgery group in the early postoperative period, according to MRI, the average length of the visualized area of signal change was longer (21.5 mm, the average diameter was 3.75 mm) than in a percutaneous nucleotractotomy group (16 mm, the average diameter was 3.75 mm). During the postoperative period (average follow-up 40 months), the pain recurred in 3 patients (30%): 2 patients after percutaneous nucleotractotomy (3 and 18 months after surgery) and in 1 patient 4 months after the open surgery. The mean VAS score at the last follow-up was 2.6. CONCLUSION: Trigeminal nucleotractotomy is an effective approach to the treatment of intractable facial pain. Our experience suggests this technique is highly effective in patients with drug-resistant pain caused by craniofacial tumors and deafferentation conditions after treating trigeminal neuralgia.

CLC-Pred 2.0: A Freely Available Web Application for In Silico Prediction of Human Cell Line Cytotoxicity and Molecular Mechanisms of Action for Druglike Compounds.

In vitro cell-line cytotoxicity is widely used in the experimental studies of potential antineoplastic agents and evaluation of safety in drug discovery. In silico estimation of cytotoxicity against hundreds of tumor cell lines and dozens of normal cell lines considerably reduces the time and costs of drug development and the assessment of new pharmaceutical agent perspectives. In 2018, we developed the first freely available web application (CLC-Pred) for the qualitative prediction of cytotoxicity against 278 tumor and 27 normal cell lines based on structural formulas of 59,882 compounds. Here, we present a new version of this web application: CLC-Pred 2.0. It also employs the PASS (Prediction of Activity Spectra for Substance) approach based on substructural atom centric MNA descriptors and a Bayesian algorithm. CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data (128,545 structures) with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against an NCI60 tumor cell-line panel based on the Developmental Therapeutics Program's NCI60 data (22,726 structures) with different thresholds of IG50 data (100, 10 and 1 nM) and a mean accuracy of prediction from 0.870 to 0.945 (LOO CV) and from 0.869 to 0.942 (20F CV), respectively; (3) 2170 molecular mechanisms of actions based on ChEMBL and PubChem data (656,011 structures) with a mean accuracy of prediction 0.979 (LOO CV) and 0.978 (20F CV). Therefore, CLC-Pred 2.0 is a significant extension of the capabilities of the initial web application.

Formation of color solitons and domains in fiber lasers.

Using numerical simulation, we have investigated the generation of color solitons consisting of two radiation fragments with different carrier frequencies in a dual-wavelength laser. The proposed mechanism for the formation of such solitons involves nonlinear losses that increase with increasing intensity, the dispersion of the refractive index, spectral gain inhomogeneity, and the generation of a doublet radiation spectrum, owing to the corresponding spectral-dependent losses in the laser. The proposed theory explains the main features of the experimentally observed formation of color domains in fiber lasers and has the potential for further development of methods for controlling the nonlinear dynamics of laser radiation.

Extracellular K+ reflects light-evoked changes in retinal energy metabolism.

Retinal neurons spend most of their energy to support the transmembrane movement of ions. Light-induced electrical activity is associated with a redistribution of ions, which affects the energy demand and results in a change in metabolism. Light-induced metabolic changes are expected to be different in distal and proximal retina due to differences in the light responses of different retinal cells. Extracellular K+ concentration ([K+]o) is a reliable indicator of local electrophysiological activity, and the purpose of this work was to compare [K+]o changes evoked by steady and flickering light in distal and proximal retina. Data were obtained from isolated mouse (C57Bl/6J) retinae. Double-barreled K+-selective microelectrodes were used to simultaneously record [K+]o and local ERGs. In the distal retina, photoreceptor hyperpolarization led to suppression of ion transfer, a decrease in [K+]o by 0.3-0.5 mM, reduced energy demand, and, as previously shown in vivo, decreased metabolism. Flickering light had the same effect on [K+]o in the distal retina as steady light of equivalent illumination. The conductance and voltage changes in postreceptor neurons are cell-specific, but the overall effect of steady light in the proximal retina is excitation, which is reflected in a [K+]o increase there (by a maximum of 0.2 mM). In steady light the [K+]o increase lasts only 1-2 s, but a sustained [K+]o increase is evoked by flickering light. A squarewave low frequency (1 Hz) flicker of photopic intensity produced the largest increases in [K+]o. Judging by measurements of [K+]o, steady illumination decreases energy metabolism in the distal retina, but not in the proximal retina (except for the first few seconds). Flickering light evokes the same decrease in the distal retina, but also evokes a sustained [K+]o increase in the proximal retina, suggesting an increase of metabolic demand there, especially at 1 Hz, when neurons of both on- and off-pathways appear to contribute maximally. This proximal retinal metabolic response to flicker correlates to the increase in blood flow during flicker that constitutes neurovascular coupling.

Metformin Influence on the Intestinal Microbiota and Organism of Rats with Metabolic Syndrome.

Metformin is a first-line drug for DM2 treatment and prevention, but its complex effect on impaired glucose tolerance (IGT), including its influence on myocardial resistance to ischemia-reperfusion injury, is not completely studied. We aimed to evaluate the influence of metformin on the intestinal microbiota (IM), metabolism, and functional and morphological characteristics of myocardium in rats with IGT. IGT was modelled in SPF Wistar rats with a high-fat diet and streptozotocin and nicotinamide injection. Rats were divided into three groups: IGT (without treatment), IGT MET (metformin therapy), and CRL (without IGT induction and treatment). IGT group was characterized by: higher body weight, increased serum glucose and total cholesterol levels, atherogenic coefficient, impairment in the functional parameters of the isolated heart during perfusion, and larger myocardium infarction (MI) size in comparison with the CRL group. IM of IGT rats differed from that of CRL: an increase of Bacteroides, Acinetobacter, Akkermansia, Roseburia, and a decrease of Lactobacillus genera representation. Metformin therapy led to the diminishing of metabolic syndrome (MS) symptoms, which correlated with IM restoration, especially with the growth of Akkermansia spp. and decline of Roseburia populations and their influence on other members of IM. The obtained results allow us to consider from a new point of view the expediency of probiotic A. muciniphila use for MS treatment.

Computational Prediction of Inhibitors and Inducers of the Major Isoforms of Cytochrome P450.

Human cytochrome P450 enzymes (CYPs) are heme-containing monooxygenases. This superfamily of drug-metabolizing enzymes is responsible for the metabolism of most drugs and other xenobiotics. The inhibition of CYPs may lead to drug-drug interactions and impair the biotransformation of drugs. CYP inducers may decrease the bioavailability and increase the clearance of drugs. Based on the freely available databases ChEMBL and PubChem, we have collected over 70,000 records containing the structures of inhibitors and inducers together with the IC50 values for the inhibitors of the five major human CYPs: 1A2, 3A4, 2D6, 2C9, and 2C19. Based on the collected data, we developed (Q)SAR models for predicting inhibitors and inducers of these CYPs using GUSAR and PASS software. The developed (Q)SAR models could be applied for assessment of the interaction of novel drug-like substances with the major human CYPs. The created (Q)SAR models demonstrated reasonable accuracy of prediction. They have been implemented in the web application P450-Analyzer that is freely available via the Internet.

A simple method to purify recombinant HCV core protein expressed in Pichia pastoris for obtaining virus-like particles and producing monoclonal antibodies.

In this study, we describe an optimized method of obtaining virus-like particles (VLPs) of the recombinant hepatitis C virus (HCV) core protein (HCcAg) expressed in yeast cells (Pichia pastoris), which can be used for the construction of diagnostic test systems and vaccine engineering. The described simplified procedure was developed to enable in vitro self-assembly of HCcAg molecules into VLPs during protein purification. In brief, the HCcAg protein was precipitated from yeast cell lysates with ammonium sulfate and renatured by gel filtration on Sephadex G-25 under reducing conditions. VLPs were self-assembled after the removal of the reducing agent by gel filtration on Sephadex G-25. Protein purity and specificity were evaluated by SDS-PAGE and immunoblotting analysis. The molecular mass of VLPs and their relative quantity were measured by HPLC, followed by confirmation of VLPs production and estimation of their shape and size by transmission electron microscopy. As a result, we obtained recombinant HCcAg preparation (with ~90% purity) in the form of VLPs and monomers, which has been used to produce hybridomas secreting monoclonal antibodies (mAbs) against HCcAg.

K+-dependent Müller cell-generated components of the electroretinogram.

The electroretinogram (ERG) has been employed for years to collect information about retinal function and pathology. The usefulness of this noninvasive test depends on our understanding of the cell sources that generate the ERG. Important contributors to the ERG are glial Müller cells (MCs), which are capable of generating substantial transretinal potentials in response to light-induced changes in extracellular K+ concentration ([K+]o). For instance, the MCs generate the slow PIII (sPIII) component of the ERG as a reaction to a photoreceptor-induced [K+]o decrease in the subretinal space. Similarly, an increase of [K+]o related to activity of postreceptor retinal neurons also produces transretinal glial currents, which can potentially influence the amplitude and shape of the b-wave, one of the most frequently analyzed ERG components. Although it is well documented that the majority of the b-wave originates from On-bipolar cells, some contribution from MCs was suggested many years ago and has never been experimentally rejected. In this work, detailed information about light-evoked [K+]o changes in the isolated mouse retina was collected and then analyzed with a relatively simple linear electrical model of MCs. The results demonstrate that the cornea-positive potential generated by MCs is too small to contribute noticeably to the b-wave. The analysis also explains why MCs produce the large cornea-negative sPIII subcomponent of the ERG, but no substantial cornea-positive potential.

Extraction of Data on Parent Compounds and Their Metabolites from Texts of Scientific Abstracts.

The growing amount of experimental data on chemical objects includes properties of small molecules, results of studies of their interaction with human and animal proteins, and methods of synthesis of organic compounds (OCs). The data obtained can be used to identify the names of OCs automatically, including all possible synonyms and relevant data on the molecular properties and biological activity. Utilization of different synonymic names of chemical compounds allows researchers to increase the completeness of data on their properties available from publications. Enrichment of the data on the names of chemical compounds by information about their possible metabolites can help estimate the biological effects of parent compounds and their metabolites more thoroughly. Therefore, an attempt at automated extraction of the names of parent compounds and their metabolites from the texts is a rather important task. In our study, we aimed at developing a method that provides the extraction of the named entities (NEs) of parent compounds and their metabolites from abstracts of scientific publications. Based on the application of the conditional random fields' algorithm, we extracted the NEs of chemical compounds. We developed a set of rules allowing identification of parent compound NEs and their metabolites in the texts. We evaluated the possibility of extracting the names of potential metabolites based on cosine similarity between strings representing names of parent compounds and all other chemical NEs found in the text. Additionally, we used conditional random fields to fetch the names of parent compounds and their metabolites from the texts based on the corpus of texts labeled manually. Our computational experiments showed that usage of rules in combination with cosine similarity could increase the accuracy of recognition of the names of metabolites compared to the rule-based algorithm and application of a machine-learning algorithm (conditional random fields).

Label-Free Protein Detection by Micro-Acoustic Biosensor Coupled with Electrical Field Sorting. Theoretical Study in Urine Models.

Diagnostic devices for point-of-care (POC) urine analysis (urinalysis) based on microfluidic technology have been actively developing for several decades as an alternative to laboratory based biochemical assays. Urine proteins (albumin, immunoglobulins, uromodulin, haemoglobin etc.) are important biomarkers of various pathological conditions and should be selectively detected by urinalysis sensors. The challenge is a determination of different oligomeric forms of the same protein, e.g., uromodulin, which have similar bio-chemical affinity but different physical properties. For the selective detection of different types of proteins, we propose to use a shear bulk acoustic resonator sensor with an additional electrode on the upper part of the bioliquid-filled channel for protein electric field manipulation. It causes modulation of the protein concentration over time in the near-surface region of the acoustic sensor, that allows to distinguish proteins based on their differences in diffusion coefficients (or sizes) and zeta-potentials. Moreover, in order to improve the sensitivity to density, we propose to use structured sensor interface. A numerical study of this approach for the detection of proteins was carried out using the example of albumin, immunoglobulin, and oligomeric forms of uromodulin in model urine solutions. In this contribution we prove the proposed concept with numerical studies for the detection of albumin, immunoglobulin, and oligomeric forms of uromodulin in urine models.

The logic of ionic homeostasis: Cations are for voltage, but not for volume.

Neuronal activity is associated with transmembrane ionic redistribution, which can lead to an osmotic imbalance. Accordingly, activity-dependent changes of the membrane potential are sometimes accompanied by changes in intracellular and/or extracellular volume. Experimental data that include distributions of ions and volume during neuronal activity are rare and rather inconsistent partly due to the technical difficulty of performing such measurements. However, progress in understanding the interrelations among ions, voltage and volume has been achieved recently by computational modelling, particularly "charge-difference" modelling. In this work a charge-difference computational model was used for further understanding of the specific roles for cations and anions. Our simulations show that without anion conductances the transmembrane movements of cations are always osmotically balanced, regardless of the stoichiometry of the pump or the ratio of Na+ and K+ conductances. Yet any changes in cation conductance or pump activity are associated with changes of the membrane potential, even when a hypothetically electroneutral pump is used in calculations and K+ and Na+ conductances are equal. On the other hand, when a Cl- conductance is present, the only way to keep the Cl-equilibrium potential in accordance with the changed membrane potential is to adjust cell volume. Importantly, this voltage-evoked Cl--dependent volume change does not affect intracellular cation concentrations or the amount of energy that is necessary to support the system. Taking other factors into consideration (i.e. the presence of internal impermeant poly-anions, the activity of cation-Cl- cotransporters, and the buildup of intra- and extracellular osmolytes, both charged and electroneutral) adds complexity, but does not change the main principles.

Computer-Aided Estimation of Biological Activity Profiles of Drug-Like Compounds Taking into Account Their Metabolism in Human Body.

Most pharmaceutical substances interact with several or even many molecular targets in the organism, determining the complex profiles of their biological activity. Moreover, due to biotransformation in the human body, they form one or several metabolites with different biological activity profiles. Therefore, the development and rational use of novel drugs requires the analysis of their biological activity profiles, taking into account metabolism in the human body. In silico methods are currently widely used for estimating new drug-like compounds' interactions with pharmacological targets and predicting their metabolic transformations. In this study, we consider the estimation of the biological activity profiles of organic compounds, taking into account the action of both the parent molecule and its metabolites in the human body. We used an external dataset that consists of 864 parent compounds with known metabolites. It is shown that the complex assessment of active pharmaceutical ingredients' interactions with the human organism increases the quality of computer-aided estimates. The toxic and adverse effects showed the most significant difference: reaching 0.16 for recall and 0.14 for precision.

Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation.

Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter-mediated disposition of the victim drug. Accurate assessment of clinically relevant DDIs for novel drug candidates represents one of the significant tasks of contemporary drug research and development and is important for practicing physicians. This work is a development of our previous investigations and aimed to create a model for the severity of DDIs prediction. PASS program and PoSMNA descriptors were implemented for prediction of all five classes of DDIs severity according to OpeRational ClassificAtion (ORCA) system: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). Prediction can be carried out both for known drugs and for new, not yet synthesized substances using only their structural formulas. Created model provides an assessment of DDIs severity by prediction of different ORCA classes from the first most dangerous class to the fifth class when DDIs do not take place in the human organism. The average accuracy of DDIs class prediction is about 0.75.

On-Chip Waveguide Coupling of a Layered Semiconductor Single-Photon Source.

Fully integrated quantum technology based on photons is in the focus of current research, because of its immense potential concerning performance and scalability. Ideally, the single-photon sources, the processing units, and the photon detectors are all combined on a single chip. Impressive progress has been made for on-chip quantum circuits and on-chip single-photon detection. In contrast, nonclassical light is commonly coupled onto the photonic chip from the outside, because presently only few integrated single-photon sources exist. Here, we present waveguide-coupled single-photon emitters in the layered semiconductor gallium selenide as promising on-chip sources. GaSe crystals with a thickness below 100 nm are placed on Si3N4 rib or slot waveguides, resulting in a modified mode structure efficient for light coupling. Using optical excitation from within the Si3N4 waveguide, we find nonclassicality of generated photons routed on the photonic chip. Thus, our work provides an easy-to-implement and robust light source for integrated quantum technology.

MetaTox: Web Application for Predicting Structure and Toxicity of Xenobiotics' Metabolites.

A new freely available web-application MetaTox ( http://www.way2drug.com/mg ) for prediction of xenobiotic's metabolism and calculation toxicity of metabolites based on the structural formula of chemicals has been developed. MetaTox predicts metabolites, which are formed by nine classes of reactions (aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation). The calculation of probability for generated metabolites is based on analyses of "structure-biotransformation reactions" and "structure-modified atoms" relationships using a Bayesian approach. Prediction of LD50 values is performed by GUSAR software for the parent compound and each of the generated metabolites using quantitative structure-activity relationahip (QSAR) models created for acute rat toxicity with the intravenous type of administration.

QNA-Based Prediction of Sites of Metabolism.

Metabolism of xenobiotics (Greek xenos: exogenous substances) plays an essential role in the prediction of biological activity and testing for the subsequent research and development of new drug candidates. Integration of various methods and techniques using different computational and experimental approaches is one of the keys to a successful metabolism prediction. While multiple structure-based and ligand-based approaches to metabolism prediction exist, the most important problem arises at the first stage of metabolism prediction: detection of the sites of metabolism (SOMs). In this paper, we describe the application of Quantitative Neighborhoods of Atoms (QNA) descriptors for prediction of the SOMs using potential function method, as well as several different machine learning techniques: naïve Bayes, random forest classifier, multilayer perceptron with back propagation and convolutional neural networks, and deep neural networks.

SOMP: web server for in silico prediction of sites of metabolism for drug-like compounds.

UNLABELLED: A new freely available web server site of metabolism predictor to predict the sites of metabolism (SOM) based on the structural formula of chemicals has been developed. It is based on the analyses of 'structure-SOM' relationships using a Bayesian approach and labelled multilevel neighbourhoods of atoms descriptors to represent the structures of over 1000 metabolized xenobiotics. The server allows predicting SOMs that are catalysed by 1A2, 2C9, 2C19, 2D6 and 3A4 isoforms of cytochrome P450 and enzymes of the UDP-glucuronosyltransferase family. The average invariant accuracy of prediction that was calculated for the training sets (using leave-one-out cross-validation) and evaluation sets is 0.9 and 0.95, respectively. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://www.way2drug.com/SOMP.