Analysis of Ibuprofen and Naproxen Based Two NSAID Candidates with Theoretical DFT Calculations

Theoretical quantum calculations have been carried out for two new ibuprofen and naproxen based Non Steroid Anti inflammatory Drug candidates. DFT (Density Functional Theory) calculations of molecules have been carried out with a general quantum chemistry software package GAMESS (the General Atomic and Molecular Electronic Structure System). Both molecules have been optimised successfully, and IR (Infra Red) spectra have been obtained via vibrational HESSIAN analyses. The frontier orbitals, HOMO (the Highest Occupied Molecular Orbital) and LUMO (the Lowest Unoccupied Molecular Orbital) have been obtained, and also molecular dynamic descriptors, such as chemical hardness, electronegativity and electrophilicity have been calculated for both molecules. Electrostatic potentials, total molecular electron densities also have been calculated for optimised molecules. All the result has been represented comparatively for both molecules.Keywords: Ibuprofen, Naproxen, DFT, GAMESS.DOI: 10.7176/JHMN/78-0

Effect of Initial Configuration on DFT Calculations for Transition Metal Complexes

Computational methods, which solves the Schrödinger’s equation for molecules, have become an indispensable tool in last decades. And Density Functional Theory is one of the most used, and most effective computational method. Transition Metal complexes, on the other hand, have been being used extensively in many important applications in many fields, such as chemical catalysts, atomic thin films, and pharmaceutical industry. Applying computational methods to transition metal complexes has become inevitable to understand better, to control and to design these compounds. As it is known, it is very difficult to handle transition metals computationally, mostly due to near degeneracy in their electronic states. The computational algorithms usually cannot achieve as successive result as they can do for other typical elements, like carbon or nitrogen for instance. Computational methods are needed to be improved for properly deal with transition metal complexes. To find computationally cheaper but still effective methods to deal with these complexes is a major challenge. Unlike the analogue calculations, computational methods solve all equations iteratively, so there are major differences between these two calculation types. The starting point in state space (the assumed initial conformation of molecule) is could have a stronger effect then the expected, on the flow of the iterative solving algorithm of the computational approach. Here we present a comparative study for a Ruthenium complex. We have optimised the molecule several times. Each of the optimisations started from different initial molecular conformations. Then we have compared the result in different ways, like calculation times and minimum energy that had reached, to see effect of starting configurations on the calculation. It is showed that, starting configuration is an important parameter for computational calculations of transition metal complexes, and it is needed to be carefully chosen to improve success of calculations

Atypical Depression

Atypical depression is defined as a specifier of major depressive disorder. Columbia criteria for atypical depression are commonly used to make a diagnosis. Female sex, onset at early age, chronic course, and higher rate of comorbidity (especially anxiety disorder and bipolar disorder) is noteworthy in atypical depression. Although, the atypical depression seems to support the familial genetic transition, there is not any specific study supporting these data. In the treatment of atypical depression, monoamine oxidase inhibitors are reported to be more effective than tricyclic antidepressants. In recent studies, selective serotonin reuptake inhibitors have also proven to be efficient

Effective Hamiltonian for non-minimally coupled scalar fields

Performing a relativistic approximation as the generalization to a curved spacetime of the flat space Klein-Gordon equation, an effective Hamiltonian which includes non-minimial coupling between gravity and scalar field and also quartic self-interaction of scalar field term is obtained.Comment: 4 page

Self-perception but not peer reputation of bullying victimization is associated with non-clinical psychotic experiences in adolescents

Background Bullying victimization may be linked to psychosis but only self-report measures of victimization have been used so far. This study aimed (a) to investigate the differential associations of peer-nominated versus self-reported victim status with non-clinical psychotic experiences in a sample of young adolescents, and (b) to examine whether different types of self-reported victimization predict non-clinical psychotic experiences in these adolescents. Method A combination of standard self-report and peer nomination procedures was used to assess victimization. The sample (n = 724) was divided into four groups (exclusively self-reported victims, self- and peer-reported victims, exclusively peer-reported victims, and non-victims) to test for a group effect on non-clinical psychotic experiences. The relationship between types of victimization and non-clinical psychotic experiences was examined by a regression analysis. Results Self-reported victims, along with self- and peer-reported victims, scored higher than peer-reported victims and non-victims on non-clinical psychotic experiences. Self-reports of direct relational, indirect relational and physical victimization significantly improved the prediction of non-clinical psychotic experiences whereas verbal and possession-directed victimization had no significant predictive value. Conclusions The relationship between victimization and non-clinical psychotic experiences is only present for self-reported victimization, possibly indicative of an interpretation bias. The observed discrepancy between self-report and peer-report highlights the importance of implementing a combination of both measures for future research. Copyright © Cambridge University Press 2012

Replicated evidence that endophenotypic expression of schizophrenia polygenic risk is greater in healthy siblings of patients compared to controls, suggesting gene-environment interaction. The EUGEI study

Background First-degree relatives of patients with psychotic disorder have higher levels of polygenic risk (PRS) for schizophrenia and higher levels of intermediate phenotypes. Methods We conducted, using two different samples for discovery (n = 336 controls and 649 siblings of patients with psychotic disorder) and replication (n = 1208 controls and 1106 siblings), an analysis of association between PRS on the one hand and psychopathological and cognitive intermediate phenotypes of schizophrenia on the other in a sample at average genetic risk (healthy controls) and a sample at higher than average risk (healthy siblings of patients). Two subthreshold psychosis phenotypes, as well as a standardised measure of cognitive ability, based on a short version of the WAIS-III short form, were used. In addition, a measure of jumping to conclusion bias (replication sample only) was tested for association with PRS. Results In both discovery and replication sample, evidence for an association between PRS and subthreshold psychosis phenotypes was observed in the relatives of patients, whereas in the controls no association was observed. Jumping to conclusion bias was similarly only associated with PRS in the sibling group. Cognitive ability was weakly negatively and non-significantly associated with PRS in both the sibling and the control group. Conclusions The degree of endophenotypic expression of schizophrenia polygenic risk depends on having a sibling with psychotic disorder, suggestive of underlying gene–environment interaction. Cognitive biases may better index genetic risk of disorder than traditional measures of neurocognition, which instead may reflect the population distribution of cognitive ability impacting the prognosis of psychotic disorder

Evidence, and replication thereof, that molecular-genetic and environmental risks for psychosis impact through an affective pathway

Background There is evidence that environmental and genetic risk factors for schizophrenia spectrum disorders are transdiagnostic and mediated in part through a generic pathway of affective dysregulation. Methods We analysed to what degree the impact of schizophrenia polygenic risk (PRS-SZ) and childhood adversity (CA) on psychosis outcomes was contingent on co-presence of affective dysregulation, defined as significant depressive symptoms, in (i) NEMESIS-2 (n = 6646), a representative general population sample, interviewed four times over nine years and (ii) EUGEI (n = 4068) a sample of patients with schizophrenia spectrum disorder, the siblings of these patients and controls. Results The impact of PRS-SZ on psychosis showed significant dependence on co-presence of affective dysregulation in NEMESIS-2 [relative excess risk due to interaction (RERI): 1.01, p = 0.037] and in EUGEI (RERI = 3.39, p = 0.048). This was particularly evident for delusional ideation (NEMESIS-2: RERI = 1.74, p = 0.003; EUGEI: RERI = 4.16, p = 0.019) and not for hallucinatory experiences (NEMESIS-2: RERI = 0.65, p = 0.284; EUGEI: -0.37, p = 0.547). A similar and stronger pattern of results was evident for CA (RERI delusions and hallucinations: NEMESIS-2: 3.02, p < 0.001; EUGEI: 6.44, p < 0.001; RERI delusional ideation: NEMESIS-2: 3.79, p < 0.001; EUGEI: 5.43, p = 0.001; RERI hallucinatory experiences: NEMESIS-2: 2.46, p < 0.001; EUGEI: 0.54, p = 0.465). Conclusions The results, and internal replication, suggest that the effects of known genetic and non-genetic risk factors for psychosis are mediated in part through an affective pathway, from which early states of delusional meaning may arise

Examining the association between exposome score for schizophrenia and functioning in schizophrenia, siblings, and healthy controls: Results from the EUGEI study.

Background. A cumulative environmental exposure score for schizophrenia (exposome score for schizophrenia [ES-SCZ]) may provide potential utility for risk stratification and outcome prediction. Here, we investigated whether ES-SCZ was associated with functioning in patients with schizophrenia spectrum disorder, unaffected siblings, and healthy controls. Methods. This cross-sectional sample consisted of 1,261 patients, 1,282 unaffected siblings, and 1,525 healthy controls. The Global Assessment of Functioning (GAF) scale was used to assess functioning. ES-SCZ was calculated based on our previously validated method. The association between ES-SCZ and the GAF dimensions (symptom and disability) was analyzed by applying regression models in each group (patients, siblings, and controls). Additional models included polygenic risk score for schizophrenia (PRS-SCZ) as a covariate. Results. ES-SCZ was associated with the GAF dimensions in patients (symptom: B = 1.53, p-value = 0.001; disability: B = 1.44, p-value = 0.001), siblings (symptom: B = 3.07, p-value < 0.001; disability: B = 2.52, p-value < 0.001), and healthy controls (symptom: B = 1.50, p-value < 0.001; disability: B = 1.31, p-value < 0.001). The results remained the same after adjusting for PRS-SCZ. The degree of associations of ES-SCZ with both symptom and disability dimensions were higher in unaffected siblings than in patients and controls. By analyzing an independent dataset (the Genetic Risk and Outcome of Psychosis study), we replicated the results observed in the patient group. Conclusions. Our findings suggest that ES-SCZ shows promise for enhancing risk prediction and stratification in research practice. From a clinical perspective, ES-SCZ may aid in efforts of clinical characterization, operationalizing transdiagnostic clinical staging models, and personalizing clinical management

Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study

Exposures constitute a dense network of the environment: exposome. Here, we argue for embracing the exposome paradigm to investigate the sum of nongenetic "risk" and show how predictive modeling approaches can be used to construct an exposome score (ES; an aggregated score of exposures) for schizophrenia. The training dataset consisted of patients with schizophrenia and controls, whereas the independent validation dataset consisted of patients, their unaffected siblings, and controls. Binary exposures were cannabis use, hearing impairment, winter birth, bullying, and emotional, physical, and sexual abuse along with physical and emotional neglect. We applied logistic regression (LR), Gaussian Naive Bayes (GNB), the least absolute shrinkage and selection operator (LASSO), and Ridge penalized classification models to the training dataset. ESs, the sum of weighted exposures based on coefficients from each model, were calculated in the validation dataset. In addition, we estimated ES based on meta-analyses and a simple sum score of exposures. Accuracy, sensitivity, specificity, area under the receiver operating characteristic, and Nagelkerke's R2 were compared. The ESMeta-analyses performed the worst, whereas the sum score and the ESGNB were worse than the ESLR that performed similar to the ESLASSO and ESRIDGE. The ESLR distinguished patients from controls (odds ratio [OR] = 1.94, P < .001), patients from siblings (OR = 1.58, P < .001), and siblings from controls (OR = 1.21, P = .001). An increase in ESLR was associated with a gradient increase of schizophrenia risk. In reference to the remaining fractions, the ESLR at top 30%, 20%, and 10% of the control distribution yielded ORs of 3.72, 3.74, and 4.77, respectively. Our findings demonstrate that predictive modeling approaches can be harnessed to evaluate the exposome