181 research outputs found
Diesel Engine Emission Reduction Using Catalytic Nanoparitcles: An Experimental
Cerium oxide being a rare earth metal with dual valance state existence has exceptional catalytic activity due to its oxygen buffering capability, especially in the nanosized form. Hence when used as an additive in the diesel fuel it leads to simultaneous reduction and oxidation of nitrogen dioxide and hydrocarbon emissions, respectively, from diesel engine. The present work investigates the effect of cerium oxide nanoparticles on performance and emissions of diesel engine. Cerium oxide nanoparticles were synthesized by chemical method and techniques such as TEM, EDS, and XRD have been used for the characterization. Cerium oxide was mixed in diesel by means of standard ultrasonic shaker to obtain stable suspension, in a two-step process. The influence of nanoparticles on various physicochemical properties of diesel fuel has also been investigated through extensive experimentation by means of ASTM standard testing methods. Load test was done in the diesel engine to investigate the effect of nanoparticles on the efficiency and the emissions from the engine. Comparisons of fuel properties with and without additives are also presented
A Rare Case of Situs Inversus with Mesocardia
A ten year old male child having congenital heart disease admitted with recurrent history of respiratory infection. ECHO cardioraphy showed Mesocardia, congenitally corrected TGA, bidirectional VSD and severe pulmonary valve stenosis.On sonographic evaluation showed intra-abdominal mirror imaging of all the solid organs and vessels which was suggestive of a rare presentation of Sinus inverses with Mesocardia. Corrective surgery as pulmonary valve balloon dilation or valvuloplasty and vestricular septal repair has to be done to the child for better morbidity and reducing the mortality risk. This anomaly complicates the diagnosis and management of acute abdominal conditions like appendicitis, diverticulitis and biliary coli
A study on exoenzyme activities of Candida albicans isolated from oral cavities of HIV-infected patients on HAART
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ASSESSMENT OF DRUG RELATED PROBLEMS IN PATIENTS WITH CHRONIC DISEASES IN THE GENERAL MEDICINE UNITS OF A TERTIARY CARE HOSPITAL
Objective: Patients with chronic diseases are more prone to develop drug-related problems (DRPs), which can further worsen their quality of life. The aim of this study was to determine factors and medications associated with DRPs in patients with chronic disease.Methods: This prospective interventional study was conducted for a duration of 6 mo in the in-patients of general medicine department of PSG Hospital, Coimbatore. DRPs were identified, assessed and recorded as per pharmaceutical care network Europe (PCNE) V5.01 criteria. Chi-square and correlation test were used to analyze the data for identifying factors associated with DRPs.Results: A total of 137 patients were enrolled for the study, of which 66 patients developed DRPs. The most prevalent DRP was found to be drug choice problem. The major causes of DRPs were found to be drug and dose selection. Antidiabetic drugs were found to be more associated with drug-related problems. The incidence of drug-related problems was high in patients aged between 50 to 59 y. Association between gender, length of hospital stays and polypharmacy with DRPs was found to be statistically significant. 58.33% of the total drug-related problems were completely solved and 19.05% were partially solved.Conclusion: The incidence of DRPs in the General Medicine department of the hospital was high. The use of an appropriate tool such as PCNE may assist pharmacists and other healthcare professionals to systematically identify, categorize and report drug-related problems
Acceptability of different oral dosage forms in paediatric patients in hospital setting
Objective The understanding of acceptability of existing dosage forms is limited in most of the world and hinders the development of acceptable, age‐appropriate medicines. The attributes of paediatric medicine acceptability may differ from country to country based on culture, healthcare infrastructure and health policies. This study was designed to map the acceptability of oral medicines in paediatric patients treated in hospital in India.
Methods An observational, cross-sectional study was conducted in patients aged below 18 years and taking any form of oral medication. Acceptability scores were obtained using CAST–ClinSearch Acceptability Score Test tool.
Findings 490 patients were recruited and 193 evaluations of different pharmaceutical products available in 20 dosage forms and 7 routes of administration were studied. Oral liquids (50%) and tablets (35%) were the most commonly prescribed and administered forms. Regardless of the therapeutic class and age, the oral liquids were ‘positively accepted’ in infants and toddlers. Acceptability of tablets improved with age and appeared to be generally good from the age of 6.
Conclusion This study indicates the limited progress towards adoption of age-appropriate dosage forms in India and thus impact on the acceptability of existing oral dosage forms. The key challenges posed by the adoption of age-appropriate formulations in India are (1) awareness of importance of appropriate administration and acceptability of medicines to children in India, (2) availability of age-appropriate dosage forms and (3) lack of child-appropriate medicine policies
THE EFFECT OF CONTRAST ENHANCEMENT ON EPIPHYTE SEGMENTATION USING GENERATIVE NETWORK
The performance of the deep learning-based image segmentation is highly dependent on two major factors as follows: 1) The organization and structure of the architecture used to train the model and 2) The quality of input data used to train the model. The input image quality and the variety of training samples are highly influencing the features derived by the deep learning filters for segmentation. This study focus on the effect of image quality of a natural dataset of epiphytes captured using Unmanned Aerial Vehicles (UAV), while segmenting the epiphytes from other background vegetation. The dataset used in this work is highly challenging in terms of pixel overlap between target and background to be segmented, the occupancy of target in the image and shadows from nearby vegetation. The proposed study used four different contrast enhancement techniques to improve the image quality of low contrast images from the epiphyte dataset. The enhanced dataset with four different methods were used to train five different segmentation models. The segmentation performances of four different models are reported using structural similarity index (SSIM) and intersection over union (IoU) score. The study shows that the epiphyte segmentation performance is highly influenced by the input image quality and recommendations are given based on four different techniques for experts to work with segmentation with natural datasets like epiphytes. The study also reported that the occupancy of the target epiphyte and vegetation highly influence the performance of the segmentation model
MULTIPLE OIL PAD DETECTION USING DEEP LEARNING
Deep learning (DL) algorithms are widely used in object detection such as roads, vehicles, buildings, etc., in aerial images. However, the object detection task is still considered challenging for detecting complex structures, oil pads are one such example: due to its shape, orientation, and background reflection. A recent study used Faster Region-based Convolutional Neural Network (FR-CNN) to detect a single oil pad from the center of the image of size 256 × 256. However, for real-time applications, it is necessary to detect multiple oil pads from aerial images irrespective of their orientation. In this study, FR-CNN was trained to detect multiple oil pads. We cropped images from high spatial resolution images to train the model containing multiple oil pads. The network was trained for 100 epochs using 164 training images and tested with 50 images under 3 different categories. with images containing: single oil pad, multiple oil pad and no oil pad. The model performance was evaluated using standard metrics: precision, recall, F1-score. The final model trained for multiple oil pad detection achieved a weighted average for 50 images precision of 0.67, recall of 0.80, and f1 score of 0.73. The 0.80 recall score indicates that 80% of the oil pads were able to identify from the given test set. The presence of instances in test images like cleared areas, rock structures, and sand patterns having high visual similarity with the target resulted in a low precision score
Design and synthesis of diazine-based panobinostat analogues for HDAC8 inhibition
© 2020 Balasubramaniam et al. Guided by computational analysis, herein we report the design, synthesis and evaluation of four novel diazine-based histone deacetylase inhibitors (HDACis). The targets of interest (TOI) are analogues of panobinostat, one of the most potent and versatile HDACi reported. By simply replacing the phenyl core of panobinostat with that of a diazine derivative, docking studies against HDAC2 and HDAC8 revealed that the four analogues exhibit inhibition activities comparable to that of panobinostat. Multistep syntheses afforded the visualized targets TOI1, TOI2, TOI3-rev and TOI4 whose biological evaluation confirmed the strength of HDAC8 inhibition with TOI4 displaying the greatest efficacy at varying concentrations. The results of this study lay the foundation for future design strategies toward more potent HDACis for HDAC8 isozymes and further therapeutic applications for neuroblastoma
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