Metabolic syndrome in schizophrenia: how much is attributable to drug treatment?

Background: This study was planned to investigate new onset metabolic syndrome (MS) and its various components associated with two widely used second generation antipsychotics i.e. olanzapine and quetiapine in the management of schizophrenia using International Diabetic Federation (IDF) criteria.Methods: A total of 60 drug naïve patients with ICD-10 diagnosis of first episode schizophrenia, divided in two groups of 30 patients each, were randomly allocated to receive two different treatments i.e. olanzapine and quetiapine. Metabolic parameters were measured at day 0, then at 6 and 12 weeks. For categorical variables, ‘Chi-square test’ was used for comparison between the two groups.For continuous variables student’s t-test was used.Results: At 6 weeks none of the patient, treated with olanzapine, developed Metabolic Syndrome (MS), but among quetiapine group 3.33% (1 out of 30) developed MS. At the end of 12 weeks, 20% patients (i.e. 6 out of 30) had MS in olanzapine treatment group and 10% (3 out of 30) in quetiapine treatment group.Conclusion: Both olanzapine and quetiapine were found to cause comparable metabolic derangement and metabolic syndrome.

Docosahexaenoic acid supplementation: a need or a commercial hype?

Docosahexaenoic acid (DHA) is an important component of the brain and is essential critical for optimal brain health and function. With revealing of its beneficial effects on cognitive function, neurological, cardiovascular system and anti-inflammatory benefits, DHA has recently gained huge attention. As a result, the market is stocked with products supplemented with DHA claiming various health benefits. This review attempts to elucidate the current findings of DHA supplementation as a pharmacological agent with both preventive and therapeutic value

Multi-Objective Optimization of Friction Stir Welding of Aluminium Alloy Using Grey Relation Analysis with Entropy Measurement Method

The present research focus on optimization of Friction Stir Welding (FSW) process parameters for joining of AA6061 aluminium alloy using hybrid approach. The FSW process parameters considered are tool rotational speed, welding speed and axial force. The quality characteristics considered are tensile strength (TS) and percentage of tensile elongation (TE). Taguchi based experimental design L9 orthogonal array is used for determining the experimental results. The value of weights corresponding to each quality characteristic is determined by using the entropy measurement method so that their importance can be properly explained. Analysis of Variance (ANOVA) is used to determine the contribution of FSW process parameters. The confirmation tests also have been done for verifying the results

Multi-objective optimization of friction stir welding process parameters for joining of dissimilar AA5083/AA6063 aluminum alloys using hybrid approach

Joining of dissimilar aluminum alloys are widely used in automobile, aerospace and shipbuilding industries. The defect-free joining of aluminum alloys using conventional technique is a challenging task for a welding engineer. Friction stir welding has been established as one of the most promising processes for defects-free joining of aluminum alloys. In this study, a hybrid approach of grey relational analysis with principal component analysis, is applied for multi-objective optimization of process parameters for friction stir welding of dissimilar AA5083/AA6063 aluminum alloys. Three responses namely tensile strength, average hardness at weld nugget zone and average grain size at weld nugget zone, and four process parameters with three levels have been selected for the study. Taguchi method based L27 orthogonal array design matrix is used for experiments. The optimal set of process parameters using hybrid approach was found as 900 r/min of tool rotational speed, 60 mm/min of welding speed, 18 mm of shoulder diameter and 5 mm of pin diameter. Improved performance of each response was obtained from the confirmation tests at optimum level of parameters

Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys

The present research investigates the application of artificial intelligence tool for modelling and multi-objective optimization of friction stir welding parameters of dissimilar AA5083-O–AA6063-T6 aluminium alloys. The experiments have been conducted according to a well-designed L27 orthogonal array. The experimental results obtained from L27 experiments were used for developing artificial neural network-based mathematical models for tensile strength, microhardness and grain size. A hybrid approach consisting of artificial neural network and genetic algorithm has been used for multi-objective optimization. The developed artificial neural network-based models for tensile strength, microhardness and grain size have been found adequate and reliable with average percentage prediction errors of 0.053714, 0.182092 and 0.006283%, respectively. The confirmation results at optimum parameters showed considerable improvement in the performance of each response