An audit of the quality of care indicators for the management of diabetes in family practice clinics in Karachi, Pakistan

BACKGROUND: Management of diabetes is a painstaking and careful approach. This study was aimed to evaluate the quality of care for the management of diabetes provided by family practitioners to their patients having diabetes. This is a retrospective audit of medical records conducted in a tertiary care teaching hospital of private sector in Karachi for one month. METHODS: For this study, 150 medical records of patients with type 2 diabetes that visited family practice clinics for their diabetes care were examined. A total of 88 patient\u27s medical records were selected and analyzed who attended the studied clinics for at least one year and had minimum of four out-patient visits. Majority (68%) of the audited medical records were of females. RESULTS: Of the total medical records analyzed, only one-quarter of the cases qualified the criteria of \u27excellent\u27 or \u27good\u27 diabetes care. Monitoring of body weight of the patient was only one indicator which was according the recommendations in 100% case at every visit. The other nearest quality of care indicator documented was blood glucose advice at every visit in 79.5% (95% CI: 71.1-87.9) of cases. Physical activity advised/reinforced at every visit was least observed (27.3%; 95% CI: 18.0-36.6). In addition, blood sugar control was reported in less than a quarter (23.9%) with 95% CI of 15.0-32.8. CONCLUSION: This work has identified a big gap in the management of type 2 diabetes provided by family practitioners. In addition, majority of the patients found to have poor glycemic control. Interventions are suggested to improve the quality of diabetes care. More such audits and research are recommended at the larger scale

Human Capital Development Typology: A Case Study of the Saudi Arabia

Saudi government is struggling to build knowledge based society to encounter social and economic challenges for the year 2030, when oil supply will be just sufficient to meet local Saudi demands. This study embarks upon the importance of the mixed-economy for sustainable growth in the 21st century. This study investigates three objectives. Firstly, it highlights Saudi socio-economic challenges. Secondly, it identifies alternative ways to realize the vision of mixed economic model for oil driven economy. Thirdly, it identifies the relationship between human capital and Saudi economic indicators. This research presents a typology based upon econometric models using secondary data, collected from World-Bank, World Health Organization (2013) and Saudi Monitory Agency annual statistical data-streams. It is recommended that the Saudi youth can play a vital role in economic growth subject to change in their mindset to overcome artificial joblessness among the Saudis

Analyzing the Parameters of Multidimensional Poverty in Taluka Naushahro Feroze: A Case Study

This research paper tackles the multidimensional poverty applying Foster and Alkire methods of Taluka Naushahro Feroze’s 14 Union councils on the basic figures. No any single navigator gives clear value for deprivation as naturally it is multidimensional. Three dimensions are selected having unequal weights in health, education, and living standard. These areas have been extra distributed in ten indicators, two for education, two for health while six for living standards. The out-put shows that Union Council Waggan has the most multidimensional poverty while least multidimensional poverty was found in Union Councils of Cheeho Taluka Naushahro Feroze. It further suggests an indicator which has highest contributions for multidimensional poverty such as life expectancy, child school attendance, school quality, child mortality, year of schooling, walls, cooking fuel, overcrowding and which contribute lowest is electricity and improved drinking water. Percentage of people for those who are MPI poor of Taluka Naushahro is 47.95 % (incidence of poverty), while average deprivation of people is 55.75 % furthermore, multidimensional poverty index (MPI) is 26.73 % in Naushahro Feroze. Keywords: Multidimensional poverty, incidence of poverty, Average deprivation DOI: 10.7176/JPID/53-06 Publication date: March 31st 202

Proximate chemical composition of sea grapes Caulerpa racemosa (J. Agardh, 1873) collected from a sub-tropical coast

Background: Nutritional fact study has prime importance to make the species edible and commercially viable to the consumers. Proximate chemical composition and amino acid profile were investigated to understand the nutritional value and protein quality of an edible algae Caulerpa racemosa. Methods: Samples were collected randomly by hand from the intertidal zone of the sub-tropical coastal Island St. Martin’s Island from February 2013 to May 2014. Samples were preserved using standard methods for chemical analysis. Proximate composition was determined using standard methods, Kjeldahl method for protein, Soxhlet method for crude lipid, H2SO4 (0.3 N) and NaOH (0.5 N) for dietary fibre, muffle furnace method for moisture content, ion-exchange chromatography for amino acid and statistical package used for validating the data. Results: The result of the study reveals that C. racemosa contains higher amount of proteins (19.72±0.77%), crude lipid (7.65±1.19%) and fibre (11.51±1.32%) compared to other green and brown algae. The higher concentration of aspartic acid (12.7±0.2%) and glutamic acid (9.2±0.7%) were observed in C. racemosa, while histidine (2.6±0.7%), methionine (1.4±0.4%) and tyrosine (3.8±0.2%) were the limiting amino acids. Lysine (6.6±0.2%), leusine (6.9±0.6%), glycine (6.5±0.4%), arginine (6.4±0.3%), alanine (7.6±0.6%) and threonine (6.2±0.5%) were obtained at a higher percentage of total amino acids. Conclusion: This study suggests that C. racemosa could be potentially used as a nutritious and functional food item for human consumption. Further studies on this edible species should be focused on fatty acid composition, vitamins, non-starch polysaccharide constituents, trace elements and sensory perceptions in order to depict safer and versatile utilization

Patterns of suicide and self-harm in Pakistan: a retrospective descriptive study protocol

Introduction Suicide is a major global public health problem. Low-income and middle-income countries contribute 78% of all suicidal deaths. Pakistan, a South Asian country, lacks official statistics on suicides at national level. Statistics on suicide are neither collected nationally nor published in the annual national morbidity and mortality surveys. Medicolegal reports on suicides and self-harm are extremely rich and important source of information but greatly underused in Pakistan. We aim to examine the patterns of suicides and self-harm retrospectively in patients who were registered with medicolegal centres (MLCs) in Karachi, during the period January 2017 to December 2021. Methods and analysis Using retrospective descriptive design, the data will be collected from the medical records maintained at the main office of the Karachi police surgeon. Data from all nine MLCs of Karachi are collated and stored at the main office of Police surgeon. Information on suicide and self-harm cases will be extracted from records of all MLCs. The data will be collected using structured proforma and it will be analysed using descriptive and inferential analysis. Ethics and dissemination The study was approved for exemption from Aga Khan University, Ethical Review Committee. The findings of the study will be disseminated by conducting seminars for healthcare professionals and stakeholders including psychiatrists, psychologists, counsellors, medicolegal officers, police surgeons, mental health nurses, general and public health physicians and policy makers. Findings will be published in local and international peer-reviewed scientific journals

Consensus interferon plus ribavirin for Hepatitis C genotype 3 patients previously treated with pegylated interferon plus ribavirin

Background Not enough data are available about the effectiveness of consensus interferon (CIFN) among HCV genotype 3 patients who failed to respond to pegylated interferon and ribavirin. Objectives We aimed to assess the efficacy and safety of CIFN and ribavirin in non-responders and relapsers to pegylated interferon with ribavirin therapy. Patients and Methods This open-label investigator-initiated study included 44 patients who received CIFN 15 µg /day plus ribavirin 800-1200 mg daily. In patients with an early virological response (EVR), the dose of CIFN was reduced to 15 µg thrice a week for further 36 weeks. Patients with delayed virological response continued to receive daily CIFN plus ribavirin to complete 48 weeks. The patients were considered “non-responders” if there were less than 2 log reduction in HCV RNA at 12 weeks and detectable HCV RNA at 24 weeks. Results Twenty-four patients (55%) were non-responders and 20 patients were relapsers to the previous treatment with pegylated interferon plus ribavirin (mean age 43.6 ± 9.4 years, males 25 (57%)). Nine patients were clinically cirrhotic (Child A). End of treatment virological response was achieved in 19 (43.1%) patients and sustained virological response (SVR) occurred in 12 (27.3%). Out of these 12 patients, eight were non-responders and four were relapsers to the previous treatment. Advanced fibrosis or clinical cirrhosis was associated with low SVR. Adverse events were fever, myalgia, anorexia, depression, and weight loss. Two patients received granulocyte colony stimulating factor for transient neutropenia. Seven patients were given erythropoietin to improve hemoglobin, and six were treated for mild depression. Two patients developed portosystemic encephalopathy. Conclusions More than one-quarter of treatment-experienced patients with HCV genotype 3 achieved SVR after re-treatment with consensus interferon plus ribavirin

Efficacy and Safety of Varenicline for Smoking Cessation in Schizophrenia: A Meta-Analysis

Objective: Smoking represents a major public health problem among patients with schizophrenia. To this end, some studies have investigated the efficacy of varenicline for facilitating smoking cessation in schizophrenia patients. The present review seeks to synthesize the results of these studies as well as document the reported side effects of using this medication.Methods: An electronic search was performed using five major databases: PubMed, Scopus, EMBASE, Web of Science, and Cochrane Library. Included in the current analysis were randomized clinical trials (RCTs) that have investigated the effect of varenicline in promoting smoking cessation in patients with schizophrenia. Risk of bias among included RCTs was assessed using the Cochrane Collaboration's quality assessment tool.Results: Among the 828 screened articles, only four RCTs, which involved 239 participants, were eligible for meta-analysis. In patients with schizophrenia, varenicline treatment when compared to placebo significantly reduced the number of cigarettes consumed per day [SMD (95% CI) = 0.89(0.57–1.22)] and expired carbon monoxide levels [SMD (95% CI) = 0.50 (0.06–0.94)] respectively.Conclusion: Despite a limited number of studies included in the meta-analysis, our results suggest that varenicline is an effective and safe drug to assist smoking cessation in patients with schizophrenia. Future large-scale well-designed RCTs are required to validate these findings

Intelligent ultra-light deep learning model for multi-class brain tumor detection

The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing to the non-linear nature of the size, shape, and textural variation. Radiologists, clinical experts, and brain surgeons examine brain MRI scans using the available methods, which are tedious, error-prone, time-consuming, and still exhibit positional accuracy up to 2−3 mm, which is very high in the case of brain cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra-Light Deep Learning Architecture (UL-DLA) for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM). It forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM), culminating in high prediction accuracy and optimum false negatives with limited network size to fit within the average GPU resources of a modern PC system. The objective of this study is to categorize multi-class publicly available MRI brain tumor datasets with a minimum time thus real-time tumor detection can be carried out without compromising accuracy. Our proposed framework includes a sensitivity analysis of image size, One-versus-All and One-versus-One coding schemes with stringent efforts to assess the complexity and reliability performance of the proposed system with K-fold cross-validation as a part of the evaluation protocol. The best generalization achieved using SVM has an average detection rate of 99.23% (99.18%, 98.86%, and 99.67%), and F-measure of 0.99 (0.99, 0.98, and 0.99) for (glioma, meningioma, and pituitary tumors), respectively. Our results have been found to improve the state-of-the-art (97.30%) by 2%, indicating that the system exhibits capability for translation in modern hospitals during real-time surgical brain applications. The method needs 11.69 ms with an accuracy of 99.23% compared to 15 ms achieved by the state-of-the-art to earlier to detect tumors on a test image without any dedicated hardware providing a route for a desktop application in brain surgery