Molecular epidemiology of hcv among health care workers of khyber pakhtunkhwa

<p>Abstract</p> <p>Background</p> <p>Studies of the molecular epidemiology and risk factors for hepatitis C virus (HCV) in health care workers (HCWs) of Peshawar, Khyber Pakhtunkhwa region are scarce. Lack of awareness about the transmission of HCV and regular blood screening is contributing a great deal towards the spread of hepatitis C. This study is an attempt to investigate the prevalence of HCV and its possible association with both occupational and non-occupational risk factors among the HCWs of Peshawar.</p> <p>Results</p> <p>Blood samples of 824 HCWs, aged between 20-59 years were analysed for anti-HCV antibodies, HCV RNA and HCV genotypes by Immunochromatographic tests and PCR. All relevant information was obtained from the HCWs with the help of a questionnaire. The study revealed that 4.13% of the HCWs were positive for HCV antibodies, while HCV RNA was detected in 2.79% of the individuals. The most predominant HCV genotype was 3a and 2a.</p> <p>Conclusion</p> <p>A program for education about occupational risk factors and regular blood screening must be implemented in all healthcare setups of Khyber Pakhtunkhwa province in order to help reduce the burden of HCV infection.</p

Hepatitis C virus to hepatocellular carcinoma

Hepatitis C virus causes acute and chronic hepatitis and can lead to permanent liver damage and hepatocellular carcinoma (HCC) in a significant number of patients via oxidative stress, insulin resistance (IR), fibrosis, liver cirrhosis and HCV induced steatosis. HCV induced steatosis and oxidative stress causes steato-hepatitis and these pathways lead to liver injury or HCC in chronic HCV infection. Steatosis and oxidative stress crosstalk play an important role in liver damage in HCV infection. This Review illustrates viral and host factors which induce Oxidative stress, steatosis and leads toward HCC. It also expresses Molecular cascade which leads oxidative stress and steatosis to HCC

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

INSWF DNA

DNA signals mainly differ from standard digital signals due to their biological data contents. Owing to unique properties of DNA signals the conventional signal processing techniques, such as digital filters, suffers with spectral leakage and results in insignificant noise suppression in DNA sequence analysis. This article presents an intelligent noise suppression window filter (INSWF) for DNA signal analysis. The filter demises the signal by separating high-level frequency contents and by identifying nucleotides with high fuzzy membership contribution at particular locations. The nucleotide contents of signals are later filtered by application of median filtering employing a combination of s-shaped and z-shaped filters. The fundamental characteristic of codons usage that causes uneven nucleotides segmentation has been tackled by finding the best fit of the curve in biological contents of filter. One of the fuzzy correlations existing between codons and median that nucleotides incorporated to reduce the signal noise to a larger magnitude. The INSWF filter outperformed the existing fixed-length digital filters tested over 250 benchmarked and random datasets of various species. A notable enhancement of 45% to 130% was achieved by significantly suppressing signal noise as compared with conventional digital filters in DNA sequence analysis