Does the accounting of the local symmetry fragments in quasi-SMILES improve the predictive potential of the QSAR models of toxicity toward tadpoles?

Models of toxicity to tadpoles have been developed as single parameters based on special descriptors which are sums of correlation weights, molecular features, and experimental conditions. This information is presented by quasi-SMILES. Fragments of local symmetry (FLS) are involved in the development of the model and the use of FLS correlation weights improves their predictive potential. In addition, the index of ideality correlation (IIC) and correlation intensity index (CII) are compared. These two potential predictive criteria were tested in models built through Monte Carlo optimization. The CII was more effective than IIC for the models considered here.

Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity.

The minimal inhibitory concentrations (pMIC) are a valuable measure of the biological activity of polypeptides. Numerical data on the pMIC are necessary to systematize knowledge on polypeptides' biochemical behaviour. The model of negative decimal logarithm of pMIC of polypeptides in the form of a mathematical function of a sequence of amino acids is suggested. The suggested model is based on the so-called correlation weights of amino acids together with the correlation weights of fragments of local symmetry (FLS). Three kinds of the FLS are considered: (i) three-symbol fragments '…xyx…', (ii) four-symbol fragments '…xyyx…', and (iii) five-symbol fragments '…xyzyx…'. The models built using the Monte Carlo technique improved by applying the index of ideality of correlation (IIC) and correlation intensity index (CII).

A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data.

BACKGROUND: The widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles. RESULTS: Tree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance with R2 and Q2-based metrics of 0.95, 0.80, and 0.79 for training, internal validation, and external validation subsets, respectively. Several nano-descriptors linked to the core-type and surface coating reactivity properties were identified as the most relevant characteristics to predict human lung nano-cytotoxicity. CONCLUSIONS: The proposed model suggests that a decrease in the ENMs diameter could significantly increase their potential ability to access lung subcellular compartments (e.g., mitochondria and nuclei), promoting strong nano-cytotoxicity and epithelial barrier dysfunction. Additionally, the presence of polyethylene glycol (PEG) as a surface coating could prevent the potential release of cytotoxic metal ions, promoting lung cytoprotection. Overall, the current work could pave the way for efficient decision-making, prediction, and mitigation of the potential occupational and environmental ENMs risks.

The system of self-consistent models based on quasi-SMILES as a tool to predict the potential of nano-inhibitors of human lung carcinoma cell line A549 for different experimental conditions.

The different features of the impact of nanoparticles on cells, such as the structure of the core, presence/absence of doping, quality of surface, diameter, and dose, were used to define quasi-SMILES, a line of symbols encoded the above physicochemical features of the impact of nanoparticles. The correlation weight for each code in the quasi-SMILES has been calculated by the Monte Carlo method. The descriptor, which is the sum of the correlation weights, is the basis for a one-variable model of the biological activity of nano-inhibitors of human lung carcinoma cell line A549. The system of models obtained by the above scheme was checked on the self-consistence, i.e., reproducing the statistical quality of these models observed for different distributions of available nanomaterials into the training and validation sets. The computational experiments confirm the excellent potential of the approach as a tool to predict the impact of nanomaterials under different experimental conditions. In conclusion, our model is a self-consistent model system that provides a user to assess the reliability of the statistical quality of the used approach.

CORAL: Model of Ecological Impact of Heavy Metals on Soils via the Study of Modification of Concentration of Biomolecules in Earthworms (Eisenia fetida).

The traditional application for quantitative structure-property/activity relationships (QSPRs/QSARs) in the fields of thermodynamics, toxicology or drug design is predicting the impact of molecular features using data on the measurable characteristics of substances. However, it is often necessary to evaluate the influence of various exposure conditions and environmental factors, besides the molecular structure. Different enzyme-driven processes lead to the accumulation of metal ions by the worms. Heavy metals are sequestered in these organisms without being released back into the soil. In this study, we propose a novel approach for modeling the absorption of heavy metals, such as mercury and cobalt by worms. The models are based on optimal descriptors calculated for the so-called quasi-SMILES, which incorporate strings of codes reflecting experimental conditions. We modeled the impact on the levels of proteins, hydrocarbons, and lipids in an earthworm's body caused by different combinations of concentrations of heavy metals and exposure time observed over two months of exposure with a measurement interval of 15 days.

In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES.

A simulation of the effect of metal nano-oxides at various concentrations (25, 50, 100, and 200 milligrams per millilitre) on cell viability in THP-1 cells (%) based on data on the molecular structure of the oxide and its concentration is proposed. We used a simplified molecular input-line entry system (SMILES) to represent the molecular structure. So-called quasi-SMILES extends usual SMILES with special codes for experimental conditions (concentration). The approach based on building up models using quasi-SMILES is self-consistent, i.e., the predictive potential of the model group obtained by random splits into training and validation sets is stable. The Monte Carlo method was used as a basis for building up the above groups of models. The CORAL software was applied to building the Monte Carlo calculations. The average determination coefficient for the five different validation sets was R2 = 0.806 ± 0.061.

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives.

Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)-as a known target of toxins in fathead minnows and Daphnia magna, causing the inhibition of AChE-was analyzed. Finally, the structural aquatic toxicity alerts obtained from ToxAlert were used to confirm the possible mechanism of action. Machine learning and cheminformatics tools were used to analyze the data. Counter-propagation artificial neural network (CPANN) models were used to determine key binding properties of FDs to proteins associated with aquatic toxicity. Predicting the binding affinity of unknown FDs using quantitative structure-activity relationship (QSAR) models eliminates the need for complex and time-consuming calculations. The results of the study show which structural features of FDs have the greatest impact on aquatic organisms and help prioritize FDs and make manufacturing decisions.

Using the Correlation Intensity Index to Build a Model of Cardiotoxicity of Piperidine Derivatives.

The assessment of cardiotoxicity is a persistent problem in medicinal chemistry. Quantitative structure-activity relationships (QSAR) are one possible way to build up models for cardiotoxicity. Here, we describe the results obtained with the Monte Carlo technique to develop hybrid optimal descriptors correlated with cardiotoxicity. The predictive potential of the cardiotoxicity models (pIC50, Ki in nM) of piperidine derivatives obtained using this approach provided quite good determination coefficients for the external validation set, in the range of 0.90-0.94. The results were best when applying the so-called correlation intensity index, which improves the predictive potential of a model.

The System of Self-Consistent Models: The Case of Henry's Law Constants.

Data on Henry's law constants make it possible to systematize geochemical conditions affecting atmosphere status and consequently triggering climate changes. The constants of Henry's law are desired for assessing the processes related to atmospheric contaminations caused by pollutants. The most important are those that are capable of long-term movements over long distances. This ability is closely related to the values of Henry's law constants. Chemical changes in gaseous mixtures affect the fate of atmospheric pollutants and ecology, climate, and human health. Since the number of organic compounds present in the atmosphere is extremely large, it is desirable to develop models suitable for predictions for the large pool of organic molecules that may be present in the atmosphere. Here, we report the development of such a model for Henry's law constants predictions of 29,439 compounds using the CORAL software (2023). The statistical quality of the model is characterized by the value of the coefficient of determination for the training and validation sets of about 0.81 (on average).

The system of self-consistent models for pesticide toxicity to Daphnia magna.

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool of modern theoretical and computational chemistry. The self-consistent model system is both a method to build up a group of QSPR/QSAR models and an approach to checking the reliability of these models. Here, a group of models of pesticide toxicity toward Daphnia magna for different distributions into training and test sub-sets is compared. This comparison is the basis for formulating the system of self-consistent models. The so-called index of the ideality of correlation (IIC) has been used to improve the above models' predictive potential of pesticide toxicity. The predictive potential of the suggested models should be classified as high since the average value of the determination coefficient for the validation sets is 0.841, and the dispersion is 0.033 (on all five models). The best model (number 4) has an average determination coefficient of 0.89 for the external validation sets (related to all five splits).

DETERMINANTS OF VACCINE HESITANCY AMONG PARENTS IN KYIV.

OBJECTIVE: The aim: Assess of the determinants of vaccine hesitancy among parents regarding their children in Kyiv, Ukraine. PATIENTS AND METHODS: Materials and methods: Direct interview with survey on parents' attitudes and behaviors regarding their children's immunization. Vaccination hesitancy was determined with the median of responses and by self-reported question. The study was conducted among parents, whose children were patients at Children's hospitals, attend schools and kindergarten in Kyiv, Ukraine. RESULTS: Results: The median of vaccine hesitancy was 14,2% of 797 parents in Kyiv. The results show that 81.5% of parents with university degree agree that vaccination of their child is important for the health of others in the community, whereas only 67% (p≤0.05) of people who graduated from high school supported this view. The only reason to vaccinate their child is so they can enter daycare or school was marked by only 4.5% of parents with university education background and 15.3% of people who graduated from high school (p≤0,05). CONCLUSION: Conclusions: Vast majority of interviewed parents think that vaccines are important for their children; meanwhile only half of the parents fully trust the current National Immunization Schedule and fully agree that question of child vaccination is their responsibility. Consulting pediatricians and GPs are associated with more parental confidence than other medical workers. Main source of negative information about vaccines is the Internet, but some part of parents who received negative information indicates health care workers as a source of this information. Majority of parents thinks that their religion is compatible with vaccines.

Diplocloster agilis gen. nov., sp. nov. and Diplocloster modestus sp. nov., two novel anaerobic fermentative members of Lachnospiraceae isolated from human faeces.

Three novel strains of Gram-stain-negative, obligately anaerobic, spore-forming straight or slightly curved rods with pointed ends occurring singly or in pairs were isolated from the faeces of healthy human children. The strains were characterized by mesophilic fermentative metabolism and production of acetate, ethanol and H2 as the end metabolic products. Strains ASD3451 and ASD5720T were motile, fermented lactose and raffinose, and weakly fermented maltose. Strain ASD4241T was non-motile and did not ferment the carbohydrates listed above but fermented starch. Strains ASD3451 and ASD5720T shared average nucleotide identity higher than 98.5 % with each other, while ASD4241T had only 88.5-89 % identity to them. Based on phylogenetic and chemotaxonomic analyses, we propose Diplocloster agilis gen. nov., sp. nov. (ASD5720T=JCM 34353T=VKM B-3497T) and Diplocloster modestus sp. nov. (ASD4241T=JCM 34351T=VKM B-3498T) within the family Lachnospiraceae.

Dynamics of luminescence characteristics of Haematococcus lacustris cultures in different cultivation conditions.

Luminescence of microalgae cultures is a valuable property for the fast diagnostics of their physiological state; however, it has been rarely used in algaculture practice. In this work, luminescence spectrum characteristics of two-stage batch cultures of the green carotenogenic microalga Haematococcus lacustris (Girod-Chantrans) Rostafinski 1875 (Chlorophyceae, Chlamydomonadales) under conditions of autotrophic and mixotrophic growth were investigated. The dynamics of the heterotrophy indices in cultures at different stages of their development in different growth media was determined. The transition of H. lacustris cultures from the initially autotrophic to mixotrophic growth regime was registered during the induction of the astaxanthin biosynthesis by complex physicochemical stressing, including nutritional deficiencies, exposure to high concentrations of sodium acetate and chloride and increased illuminance and temperature. The applicability of luminescence spectrometry in vivo for a rapid assessment of the state of H. lacustris cultures in various growth media and with different methods of secondary carotenogenesis induction was shown. The results obtained can be used in experimental studies on optimizing cultivation methods for this species, as well as for the express control of the physiological state of its industrial cultures.

Monte Carlo Models for Sub-Chronic Repeated-Dose Toxicity: Systemic and Organ-Specific Toxicity.

The risk-characterization of chemicals requires the determination of repeated-dose toxicity (RDT). This depends on two main outcomes: the no-observed-adverse-effect level (NOAEL) and the lowest-observed-adverse-effect level (LOAEL). These endpoints are fundamental requirements in several regulatory frameworks, such as the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) and the European Regulation of 1223/2009 on cosmetics. The RDT results for the safety evaluation of chemicals are undeniably important; however, the in vivo tests are time-consuming and very expensive. The in silico models can provide useful input to investigate sub-chronic RDT. Considering the complexity of these endpoints, involving variable experimental designs, this non-testing approach is challenging and attractive. Here, we built eight in silico models for the NOAEL and LOAEL predictions, focusing on systemic and organ-specific toxicity, looking into the effects on the liver, kidney and brain. Starting with the NOAEL and LOAEL data for oral sub-chronic toxicity in rats, retrieved from public databases, we developed and validated eight quantitative structure-activity relationship (QSAR) models based on the optimal descriptors calculated by the Monte Carlo method, using the CORAL software. The results obtained with these models represent a good achievement, to exploit them in a safety assessment, considering the importance of organ-related toxicity.

The searching for agents for Alzheimer's disease treatment via the system of self-consistent models.

Robust quantitative structure-activity relationships (QSARs) for hBACE-1 inhibitors (pIC50) for a large database (n = 1706) are established. New statistical criteria of the predictive potential of models are suggested and tested. These criteria are the index of ideality of correlation (IIC) and the correlation intensity index (CII). The system of self-consistent models is a new approach to validate the predictive potential of QSAR-models. The statistical quality of models obtained using the CORAL software (http://www.insilico.eu/coral) for the validation sets is characterized by the average determination coefficient R2v= 0.923, and RMSE = 0.345. Three new promising molecular structures which can become inhibitors hBACE-1 are suggested.

RESULTS OF COMPETENCE FORMATION IN PARENTS OF CHILDREN WITH MODERATE INTELLECTUAL DISABILITIES.

OBJECTIVE: The aim: To analyze the levels of competence formation in parents of senior preschoolers with moderate ID and to substantiate the obtained indicators for practical use. PATIENTS AND METHODS: Materials and methods: The sample of the study was 162 persons: experimental group (EG) - parents (or persons substituting them) of 53 preschoolers with moderate ID - 96 persons (out of the total number, 12 children are brought up by mothers without fathers); control group (CG) - parents (or persons substituting them) of 41 preschoolers with MID - 66 persons (out of the total number, 16 children are brought up by mothers without fathers). RESULTS: Results: The use of Fisher's φ-criterion allowed to draw the following conclusions: first, at the ascertaining stage of the experiment the characteristics of the EG and CG according to certain indicators of parental competence were homogeneous (α=0.05). At the control stage - significantly different indicators of the experimental group (α=0.01). Secondly, positive changes in the identified characteristics in the EG and absence of such changes in the CG, gives grounds to argue about the effectiveness of implementation of the developed and presented in the experimental model forms, methods and conditions for the development of parental competence. CONCLUSION: Conclusions: Thus, despite almost identical indicators of competence levels in parents of the EG and CG at the ascertaining stage of the experiment, the molding stage testified to the specific advantage of representatives of the experimental group over representatives of the control group on levels of development of motivational-personal, gnostic, communicative-activity criteria of competence of parents of children with moderate ID.

Pesticides, cosmetics, drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity.

The similarity is an important category in natural sciences. A measure of similarity for a group of various biochemical endpoints is suggested. The list of examined endpoints contains (1) toxicity of pesticides towards rainbow trout; (2) human skin sensitization; (3) mutagenicity; (4) toxicity of psychotropic drugs; and (5) anti HIV activity. Further applying and evolution of the suggested approach is discussed. In particular, the conception of the similarity (dissimilarity) of endpoints can play the role of a "useful bridge" between quantitative structure property/activity relationships (QSPRs/QSARs) and read-across technique.

The sequence of amino acids as the basis for the model of biological activity of peptides.

The algorithm of building up a model for the biological activity of peptides as a mathematical function of a sequence of amino acids is suggested. The general scheme is the following: The total set of available data is distributed into the active training set, passive training set, calibration set, and validation set. The training (both active and passive) and calibration sets are a system of generation of a model of biological activity where each amino acid obtains special correlation weight. The numerical data on the correlation weights calculated by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral). The target function aimed to give the best result for the calibration set (not for the training set). The final checkup of the model is carried out with data on the validation set (peptides, which are not visible during the creation of the model). Described computational experiments confirm the ability of the approach to be a tool for the design of predictive models for the biological activity of peptides (expressed by pIC50).

EPIDEMIOLOGICAL ANALIYSIS OF PERTUSSIS MORBIDITY IN UKRAINE.

OBJECTIVE: The aim: To improve epidemiological monitoring of pertussis by analyzing the disease morbidity during 1995-2017 in Ukraine, to make a prognosis. PATIENTS AND METHODS: Materials and methods: Analysis of the pertussis morbidity during 1995-2017 using the data of the Ministry of Health of Ukraine. The cyclicity was determined by Fourier spectral analysis. The models of prognosis were constructed using polyharmonic regression and an exponential smoothing algorithm. Cartographic analysis and integrated indicators (multiyear index of the prevalence rate, mean square deviation, mean the multiyear pace of the gain in the prevalence rate) were used to determine the areas of risk. Summarized data were used to calculate the generalized coefficient. RESULTS: Results: The pertussis morbidity cycle has 5 years intervals in Ukraine. The prognosis is for increasing the pertussis morbidity from 4.91-5.54 to 5.48-7.06 per 100.000 people. The generalized coefficient was significantly higher in western part (83.3%) than in central (50.0%) and eastern (16.6%) parts. The study showed that population reproduction rates, natural population increase, and the proportion of people against vaccination were higher in the western part than in other parts of the country. CONCLUSION: Conclusions: The pertussis cyclicity depends on the internal mechanisms of interaction in the ecological system. There is a prognosis of worsening the epidemic situation of pertussis spreading. The risk area is the western part of Ukraine, which is characterized by active demographic processes and a greater number of people who are negative about vaccination.

INTELLECTUAL DISABILITIES IN PRESCHOOLERS: MENTAL HEALTH DETERMINANTS DURING THE PERINATAL PERIOD OF DEVELOPMENT.

OBJECTIVE: The aim: The aim of the study is to identify correlations between mental health indicators for preschoolers with intellectual disabilities and specifics of their prenatal, natal and postnatal development. PATIENTS AND METHODS: Materials and methods: Analysis of special medical, psychological and pedagogical literature; comparison and systematization of research material to determine mentalhealth indicators for preschoolers with mild and moderate degrees of intellectual disability; mathematical methods of statistics. RESULTS: Results: Empirical research, delineating formation of mental health indicators for senior preschoolers with intellectual disabilities, specified in low level of formation of both criteria, has been conducted. In general, we can conclude that the formation of mental health against the background of intellectual disability of preschool children is associatedwith low levels of self-awareness as a member of a social group; below age expectations' ability to perceive self and produce information about themselves, their preferences and meaningful adults; inability to adhere to social norms and values in behavior; high levels of anxiety; aggressive tendencies; lack of independence; helplessness concerning helping others; expectation of help from adults; content inappropriate emotional outbursts accompanied by frequent mood swings; and desire for solitude. CONCLUSION: Conclusions: The obtained results confirm the conditionality of intellectual disability on negative factors in the perinatal period of development and strong correlation withmental disorders. It is important to correctly diagnose the existing manifestations of comorbid mental health disorders at preschool age and earlier, in order to implement appropriate measures for intervention and development.