Retrospective Analysis of Metabolic syndrome: Prevalence and Distribution in Executive Population in Urban Pakistan.

Background. Metabolic Syndrome (MetS) is a major public health concern. Objective. The aim of this study was to estimate the frequency of MetS, its components, and factors associated with MetS amongst apparently healthy individuals in Pakistan. Methods. A retrospective cross-sectional study was conducted at the executive Clinics of Aga Khan Hospital, Pakistan. Medical records of patients aged ≥18 years visiting the clinics from July 2011 to December 2011 were consecutively reviewed. Records in which either MetS components data or 10% of overall data was missing were excluded. A total of 1329 participants’ records was included in final analysis. Data was analyzed using SPSS version 19 and multivariable logistic regression was used to identify the factors associated with MetS. Results. A total of 847 (63.7%) participants had MetS; mean age of the participants were 47.6 ± 11.6 years. About 70.4% were males and 29.6% were females. Approximately 70% of participants had BMI ≥25 kg/m2. MetS was associated with male gender (AOR = 2.1; 95% C.I: 1.6–3.2) and history of diabetes among parents (AOR = 3.0; 95% C.I: 1.6–6.0). Conclusion. This study shows that a large proportion of population has MetS and is overweight or obese. This requires urgent interventions on part of health care providers’ especially family physicians. Educating masses about life style factors can make a difference. Further researches on this issue are warranted

Consumers' Adoption of Apparel Fashion: The Role of Innovativeness, Involvement, and Social Values

Fashion adoption is concerned with how consumers accept a prevailing style during a particular time. Fashion is accepted by the consumers and influences their behavior in many ways. It is one of the strongest drivers to influence and motivate their intention to buy and adopt new products. Either through advertisement and promotion of brands or through endorsement of celebrities, companies motivate consumers of different socioeconomic backgrounds to follow the new fashion trends and buy their products. The purpose of this study is to investigate the factors that compel consumers to adopt apparel fashion in Pakistan. It has been investigated that, how and why different factors i.e., Fashion innovativeness, Consumer innovativeness, Fashion involvement, Opinion leadership, and Status, influence consumers’ intention to adopt new apparel fashion and how that intention influence their actual fashion adoption. Total 500 questionnaires were distributed among the respondents in different places (i.e., shopping plazas or malls, public places such as bus stops, playgrounds, and inside the educational institutions, in the cities of Islamabad and Lahore, Pakistan. Out of the total, only 332 (66.4%) questionnaires were retrieved. The regression test and the model fit results using Structural Equation Modeling (SEM) analysis revealed that, except fashion innovativeness, and consumer innovativeness, the fashion involvement, opinion leadership and status of consumers positively influence their intention to adopt new fashion. However, Intention to adopt has no significant effect on the consumers’ actual adoption of fashionable clothes. The positive and negative effects are discussed accordingly. In the light of the results and findings, the implications and future research recommendations are also debated at the end.© 2014 The Authors. Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).fi=vertaisarvioitu|en=peerReviewed

Supervised classification for object identification in urban areas using satellite imagery

This paper presents a useful method to achieve classification in satellite imagery. The approach is based on pixel level study employing various features such as correlation, homogeneity, energy and contrast. In this study gray-scale images are used for training the classification model. For supervised classification, two classification techniques are employed namely the Support Vector Machine (SVM) and the Naive Bayes. With textural features used for gray-scale images, Naive Bayes performs better with an overall accuracy of 76% compared to 68% achieved by SVM. The computational time is evaluated while performing the experiment with two different window sizes i.e., 50x50 and 70x70. The required computational time on a single image is found to be 27 seconds for a window size of 70x70 and 45 seconds for a window size of 50x50.Comment: 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET

MICROBIAL METABOLISM OF AN ANTI-HIV AND ANTI-MALARIAL NATURAL PRODUCT ANDROGRAPHOLIDE

Objective: Andrographolide (1), the main crystalline bitter principle of Andrographis paniculata nees. (also known as rice bitter in the West Indies) was first isolated by Gorter, and characterized as trihydroxy lactone. It was also isolated from Holmskilodia sanguinea in very good yield. It possesses a wide range of biological activities, which is also important in the therapeutic fields including anti-inflammatory, anti-malarial, anti-viral, immuno-stimulant, anti-HIV, and cardiovascular properties. In the present study, we first time studied the microbial metabolism of andrographolide (1) with Cunninghamella elegans (TSY 0865) and Cephalosporium aphidicola (IMI-68689). Methods: Microbial cultures of the C. elegans and C. aphidicola were grown on Potato dextrose agar (PDA) at 25°C and stored at 4°C. Medium for C. aphidicola was prepared by mixing Glucose (50.0 g), KH2PO4 (1.0 g), MgSO4.7H2O (2.0 g), Glycin (2.0 g), KCl (1.0 g) and Gibberella trace element solution (2.0 mL) into distilled water (1 L) and maintained pH at 5.6. While C. elegans medium was prepared by adding Glucose (10.0 g), peptone (5.0 g), KH2PO4 (5.0 g), yeast extract (5.0 g), NaCl (5.0 g) and glycerol (10 mL) into distilled water (1 L) and maintained pH at 5.6. Results: Two compounds were obtained as transformed products. Based on physical and spectroscopic data, these have been identified as andropanolide (2) and 14-deoxy-11,12-didehydro andrographolide (3). Both compounds were previously obtained by the phytochemical investigation of A. paniculata and biotransformed product as well. Conclusion: It could be concluded that C. elegans and C. aphidicola were able to produce oxidative derivatives of 1 in a regio- and stereoselective manner. Present investigation has been conducted for the first time with C. elegans and C. aphidicola. Incubation of 1 for 9 days with fungal strains yielded isomerized and oxidative products 2 and 3. Structures of all metabolites were elucidated by using spectroscopic techniques

Post Thrombolytic St-Segment Resolution Outcome in Acute Myocardial Infarction Patients

OBJECTIVES: The main objective of this study was to assess post thrombolytic resolution of ST-segment and its outcome in patients with acute myocardial infarction. METHODOLOGY: This Prospective Comparative Study was carried out at the Cardiology Unit of Ayub Teaching Hospital, Abbottabad. All patients irrespective of gender and age with ST-Segment elevation myocardial infarction (STEMI), having no immediate access to angioplasty and thrombolysed with streptokinase, were included in this study. ECG was taken at the beginning and 90 minutes after the administration of streptokinase. Based on ST-segment resolution on ECG taken at 90 minutes these patients were classified into group A and B. Group A included patients with ST-segment resolution while group B showed no resolution of ST-segment after streptokinase administration. These patients were followed during their hospital stay for complications such as arrhythmias, cardiogenic shock, acquired ventricular septal defects (VSD) aneurysm and death. RESULTS: Among 115 patients, 94 were male and 21 female. Group A included 102 (89%) patients and group B included 13 (11%). In group A, only 1 (0.98 %) patient developed complications and in group B, 13 patients (100%) developed complications. Arrhythmias were the most common complication among MI patients in group A while cardiogenic shock was the commonest complication in group B. CONCLUSION: ST-segment resolution is a practical and applicable indicator of successful thrombolysis and has a significant correlation with clinical outcome in acute myocardial patients after thrombolysis with streptokinase

BIOTRANSFORMATION OF DEHYDROABIETIC ACID WITH MICROBIAL CELL CULTURES AND α-GLUCOSIDASE INHIBITORY ACTIVITY OF RESULTING METABOLITES

Dehydroabietic acid (DHA, 1), a natural occurring diterpene resin acid, is an abundant resin acid in conifers, representing a natural wood protectant. The aim of this study was to use microbial cell cultures as tools for modification of 1 in order to obtain value-added functional derivatives. A scaled-up biotransformation of 1 by filamentous fungus Cunninghamella elegans, Rhizopus stolonifer, Gibberella fujikuroi, and Cephalosporium aphidicola were conducted for the first time. Three hydroxylated metabolites; 1b-hydroxydehydroabietic acid (2); 15-hydroxy dehydroabietic acid (3); and 16-hydroxy dehydroabietic acid (4). The structure of the hydroxylated metabolites were elucidated by 1-D (1H, 13C) and 2-D NMR (COSY, HMBC, HMQC, NOESY) techniques and MS analyses. Dehydroabietic acid (1) and their transformed products 2-4 exhibited a promising α-Glucosidase inhibitory activity. Compound 1 showed 38 times more active than the standard α-Glucosidase inhibitor, deoxynojirimycin. Compound 1 and its transformed metabolites 2-4 also showed significant antibacterial activities

Movie Tags Prediction and Segmentation Using Deep Learning

The sheer volume of movies generated these days requires an automated analytics for ef cient classi cation, query-based search, and extraction of desired information. These tasks can only be ef ciently performed by a machine learning based algorithm. We address the same issue in this paper by proposing a deep learning based technique for predicting the relevant tags for a movie and segmenting the movie with respect to the predicted tags. We construct a tag vocabulary and create the corresponding dataset in order to train a deep learning model. Subsequently, we propose an ef cient shot detection algorithm to nd the key frames in the movie. The extracted key frames are analyzed by the deep learning model to predict the top three tags for each frame. The tags are then assigned weighted scores and are ltered to generate a compact set of most relevant tags. This process also generates a corpus which is further used to segment a movie based on a selected tag. We present a rigorous analysis of the segmentation quality with respect to the number of tags selected for the segmentation. Our detailed experiments demonstrate that the proposed technique is not only ef cacious in predicting the most relevant tags for a movie, but also in segmenting the movie with respect to the selected tags with a high accuracy

New class of acetylcholinesterase inhibitors from the stem bark of Knema laurina and their structural insights

Bioassay-guided extraction of the stem bark of Knema laurina showed the acetylcholinesterase (AChE) inhibitory activity of DCM and hexane fractions. Further repeated column chromatography of hexane and DCM fractions resulted in the isolation and purification of five alkenyl phenol and salicylic acid derivatives. New compounds, (+)-2-hydroxy-6-(10'-hydroxypentadec-8'(E)-enyl)benzoic acid (1) and 3-pentadec-10'(Z)-enylphenol (2), along with known 3-heptadec-10'(Z)-enylphenol (3), 2-hydroxy-6-(pentadec-10'(Z)-enyl)benzoic acid (4), and 2-hydroxy 6-(10'(Z)-heptadecenyl)benzoic acid (5) were isolated from the stem bark of this plant. Compounds (1-5) were tested for their acetylcholinesterase inhibitory activity. The structures of these compounds were elucidated by the 1D and 2D NMR spectroscopy, mass spectrometry and chemical derivatizations. Compound 5 showed strong acetylcholinesterase inhibitory activity with IC(50) of 0.573 ± 0.0260 μM. Docking studies of compound 5 indicated that the phenolic compound with an elongated side chain could possibly penetrate deep into the active site of the enzyme and arrange itself through π-π interaction, H-bonding, and hydrophobic contacts with some critical residues along the complex geometry of the active gorge

Association Mapping for Improving Fiber Quality in Upland Cottons

Improved fiber yield is considered a constant goal of upland cotton (Gossypium hirsutum) breeding worldwide, but the understanding of the genetic basis controlling yield-related traits remains limited. Dissecting the genetic architecture of complex traits is an ongoing challenge for geneticists. Two complementary approaches for genetic mapping, linkage mapping and association mapping have led to successful dissection of complex traits in many crop species. Both of these methods detect quantitative trait loci (QTL) by identifying marker–trait associations, and the only fundamental difference between them is that between mapping populations, which directly determine mapping resolution and power. Nowadays, the availability of genomic tools and resources is leading to a new revolution of plant breeding, as they facilitate the study of the genotype and its relationship with the phenotype, in particular for complex traits. Next Generation Sequencing (NGS) technologies are allowing the mass sequencing of genomes and transcriptomes, which is producing a vast array of genomic information with the development of high-throughput genotyping, phenotyping will be a major challenge for genetic mapping studies. We believe that high-quality phenotyping and appropriate experimental design coupled with new statistical models will accelerate progress in dissecting the genetic architecture of complex traits