Effect of Autism on the Individual and Their Family, A Study Conducted Among Autism Spectrum Disorder (ASD) Population in Kerala

This study explores and analyses the experience of family members of ASD individuals in Kerala .This paper examines the socio-psychological and cultural impact of ASD to the parents and siblings of ASD kids. This study was undertaken using an exploratory design and conducted 4 case studies along with in person semi – structured interviews to address the research question, apart from the collective case study research method, cross case analysis were done. This study was conducted in Trivandrum district of Kerala state India .The results emerged include 4 major things; one, all aspects of the family were affected with ASD. Second the parents of the ASD individuals are facing stress, depression and social isolation. Third there are some serious character aberrations found among the siblings. Fourth lack of support from the spouse especially husbands in the proper upbringing of the ASD child; Discussion of these research findings and the recommendations contributed to the current research and existing literature on the impact of ASD to the family

IN VITRO-IN VIVO EVALUATION OF FAST-DISSOLVING TABLETS CONTAINING SOLID DISPERSION OF OXCARBAZEPINE

Objective: Investigation of in vitro/in vivo behaviour of fast-dissolving tablets containing solid dispersions of oxcarbazepine is the focus of the present research work.Methods: The effect of various hydrophilic polymers on the aqueous solubility of oxcarbazepine was studied. Polyethylene glycol 6000 carrier was selected and solid dispersions were prepared by various methods. A total of nine formulations were compressed into fast-dissolving tablets using avicel PH 102 as a directly compressible filler and ac-di-sol, sodium starch glycolate and crospovidone as super disintegrants and evaluated for pre and post compression parameters and in vitro drug release. In vivo studies of the pure drug, optimized formulation and marketed formulation were carried out on male Wistar rats and pharmacokinetic parameters were calculated using the pk function for Microsoft excel.Results: Mathematical analysis of in vitro data suggested that the first order was the most suitable mathematical model for describing the optimized formulation. The first-order plot was found to be fairly linear for optimized formulation as indicated by its high regression value. Stability studies indicated that the effect of storage was insignificant at 5% level of confidence. The optimized formulation has shown Tmax of 0.5 h, which was highly significant (P<0.05) when compared with pure drug and marketed formulation.Conclusion: Therefore, the solid dispersions prepared by melting method using polyethylene glycol 6000 as hydrophilic carrier can be successfully used for the improvement of dissolution of oxcarbazepine and resulted in faster onset of action as indicated by in vitro and in vivo studies

Effect of essentiale in diabetic subjects with non-alcoholic fatty liver

Nonalcoholic fatty liver (NAFL) has been reported to be common among subjects with diabetes. However, there are not much therapeutic options for NAFL. In this open labeled clinical trial we studied the effect of Essentiale in diabetic subjects with NAFL. Twenty-eight type 2 diabetic patients attending the out-patient division of M.V. Diabetes Specialities Centre, Chennai and satisfying the inclusion criteria were recruited for the study. High resolution B mode ultrasonography was carried out for diagnosis of NAFL. Liver function markers [Alanine aminotransferase (ALT), Aspartate aminotransferase (AST) and Gamma Glutamyl transferase (GGT)] were measured. 22 out of the 28 patients (78.5%) were available for follow up. The mean age of the study subjects was 41±8 years and 50% were males. A significant reduction in all the liver enzymes were observed after Essentiale treatment (baseline vs. six months after treatment: ALT: 54.5± 29.6 IU/L vs. 37.1±18.7 IU/L, p< 0.05, AST: 38.0±18.0 IU/L vs. 27.6±12.4 IU/L, p< 0.05, GGT: 38.7±27.5 IU/L vs. 29.6±13.8 IU/L, p< 0.05). Ultrasound studies revealed that the hepatic echotexture improved after Essentiale treatment in 12/22 (54.5%) of the study subjects, while there was no change in 9/22 (40.9%), and it worsened in only one patient (4.5%). The study results suggest that Essentiale protects and improves liver function in diabetic subjects with NAFL. Prospective, blinded clinical trials are required to confirm these findings

Cost of Dry Eye Treatment in an Asian Clinic Setting

10.1371/journal.pone.0037711PLoS ONE76

Sustainable Waste-to-Energy Technologies: Bioelectrochemical Systems

The food industry produces a large amount of waste and wastewater, of which most of the constituents are carbohydrates, proteins, lipids, and organic fibers. Therefore food wastes are highly biodegradable and energy rich. Bioelectrochemical systems (BESs) are systems that use microorganisms to biochemically catalyze complex substrates into useful energy products, in which the catalytic reactions take place on electrodes. Microbial fuel cells (MFCs) are a type of bioelectrochemical systems that oxidize substrates and generate electric current. Microbial electrolysis cells (MECs) are another type of bioelectrochemical systems that use an external power source to catalyze the substrate into by-products such as hydrogen gas, methane gas, or hydrogen peroxide. BESs are advantageous due to their ability to achieve a degree of substrate remediation while generating energy. This chapter presents an extensive literature review on the use of MFCs and MECs to remediate and recover energy from food industry waste. These bioelectrochemical systems are still in their infancy state and further research is needed to better understand the systems and optimize their performance. Major challenges and limitations for the use of BESs are summarized and future research needs are identified

Frequency selective surfaces based high performance microstrip antenna

This book focuses on performance enhancement of printed antennas using frequency selective surfaces (FSS) technology. The growing demand of stealth technology in strategic areas requires high-performance low-RCS (radar cross section) antennas. Such requirements may be accomplished by incorporating FSS into the antenna structure either in its ground plane or as the superstrate, due to the filter characteristics of FSS structure. In view of this, a novel approach based on FSS technology is presented in this book to enhance the performance of printed antennas including out-of-band structural RCS reduction. In this endeavor, the EM design of microstrip patch antennas (MPA) loaded with FSS-based (i) high impedance surface (HIS) ground plane, and (ii) the superstrates are discussed in detail. The EM analysis of proposed FSS-based antenna structures have been carried out using transmission line analogy, in combination with the reciprocity theorem. Further, various types of novel FSS structures are considered in designing the HIS ground plane and superstrate for enhancing the MPA bandwidth and directivity. The EM design and performance analyses of FSS-based antennas are explained here with the appropriate expressions and illustrations

Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images

An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root cause of gliomas, which are the most prevalent type of primary brain tumor. After receiving a diagnosis of glioblastoma, it is anticipated that the average patient will have a survival time of less than 14 months. Magnetic resonance imaging (MRI) is a well-known non-invasive imaging technology that can detect brain tumors and gives a variety of tissue contrasts in each imaging modality. Until recently, only neuroradiologists were capable of performing the tedious and time-consuming task of manually segmenting and analyzing structural MRI scans of brain tumors. This was because neuroradiologists have specialized training in this area. The development of comprehensive and automatic segmentation methods for brain tumors will have a significant impact on both the diagnosis and treatment of brain tumors. It is now possible to recognize tumors in photographs because of developments in computer-aided design (CAD), machine learning (ML), and deep learning (DL) approaches. The purpose of this study is to develop, through the application of MRI data, an automated model for the detection and classification of brain tumors based on deep learning (DLBTDC-MRI). Using the DLBTDC-MRI method, brain tumors can be detected and characterized at various stages of their progression. Preprocessing, segmentation, feature extraction, and classification are all included in the DLBTDC-MRI methodology that is supplied. The use of adaptive fuzzy filtering, often known as AFF, as a preprocessing technique for photos, results in less noise and higher-quality MRI scans. A method referred to as “chicken swarm optimization” (CSO) was used to segment MRI images. This method utilizes Tsallis entropy-based image segmentation to locate parts of the brain that have been injured. In addition to this, a Residual Network (ResNet) that combines handcrafted features with deep features was used to produce a meaningful collection of feature vectors. A classifier developed by combining DLBTDC-MRI and CSO can finally be used to diagnose brain tumors. To assess the enhanced performance of brain tumor categorization, a large number of simulations were run on the BRATS 2015 dataset. It would appear, based on the findings of these trials, that the DLBTDC-MRI method is superior to other contemporary procedures in many respects

Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images

An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root cause of gliomas, which are the most prevalent type of primary brain tumor. After receiving a diagnosis of glioblastoma, it is anticipated that the average patient will have a survival time of less than 14 months. Magnetic resonance imaging (MRI) is a well-known non-invasive imaging technology that can detect brain tumors and gives a variety of tissue contrasts in each imaging modality. Until recently, only neuroradiologists were capable of performing the tedious and time-consuming task of manually segmenting and analyzing structural MRI scans of brain tumors. This was because neuroradiologists have specialized training in this area. The development of comprehensive and automatic segmentation methods for brain tumors will have a significant impact on both the diagnosis and treatment of brain tumors. It is now possible to recognize tumors in photographs because of developments in computer-aided design (CAD), machine learning (ML), and deep learning (DL) approaches. The purpose of this study is to develop, through the application of MRI data, an automated model for the detection and classification of brain tumors based on deep learning (DLBTDC-MRI). Using the DLBTDC-MRI method, brain tumors can be detected and characterized at various stages of their progression. Preprocessing, segmentation, feature extraction, and classification are all included in the DLBTDC-MRI methodology that is supplied. The use of adaptive fuzzy filtering, often known as AFF, as a preprocessing technique for photos, results in less noise and higher-quality MRI scans. A method referred to as “chicken swarm optimization” (CSO) was used to segment MRI images. This method utilizes Tsallis entropy-based image segmentation to locate parts of the brain that have been injured. In addition to this, a Residual Network (ResNet) that combines handcrafted features with deep features was used to produce a meaningful collection of feature vectors. A classifier developed by combining DLBTDC-MRI and CSO can finally be used to diagnose brain tumors. To assess the enhanced performance of brain tumor categorization, a large number of simulations were run on the BRATS 2015 dataset. It would appear, based on the findings of these trials, that the DLBTDC-MRI method is superior to other contemporary procedures in many respects