Designing and construction a DNA vaccine encoding the fusion fragment of cfp10 and Ag85A immunodominant genes of Mycobacterium tuberculosis

Background: Pathogenic mycobacteria are one of major causes of human morbidity and mortality. Mycobacterium tuberculosis (M. tuberculosis) is an etiological agent of human tuberculosis. Designing new vaccines including DNA vaccines may be considered as new approaches for preventing of TB.Materials and Methods: M. tuberculosis H37Rv was grown on Lowenstein Jensen medium for 4 weeks at 37ºC and then DNA was extracted. The cfp10 gene was amplified by PCR. After digesting the PCR product and the plasmid, cfp10 fragment was ligated into the vector using T4 DNA ligase. Then, Ag85A was subcloned into pcDNA/cfp10. Escherichia coli strain JM109 bacteria were transformed by the desired construct. Clone confirmations were performed by colony PCR, restriction enzyme digestion and DNA sequencing. Recombinant vector was transfected into HeLa cells and total RNA was extracted, then cDNA was synthesized using oligo-dT. Finally PCR was performed by cfp10 primers.Results: The cfp10 was amplified by PCR method and the PCR products were visualized by agarose gel electrophoresis. The cfp10 fragments showed 303 bp in length. The cfp10 cloned into pcDNA. Then, Ag85Awas ligated into pcDNA/cfp10 after digestion correctly. Colony-PCR and restriction enzyme digestion and sequencing confirmed the cloning the fusion Ag85A/cfp10 fragment. Finally, after cDNA synthesis, expression of vector was confirmed in eukaryotic system.Conclusion: Cloning of Ag85A/cfp10 genes of M. tuberculosis were performed correctly. It can use as a DNA vaccine for investigation the immune responses in animal models in future studies

Frequency and antibiotic resistance patterns of isolated bacteria from positive blood culture of hospitalized patients

Background: Bloodstream infections are the most important causes of morbidity and mortality in hospitalized patients. Blood culture plays an important role in identifying most of bacterial agents of bloodstream infections. Knowledge about bacterial agents of bloodstream infections and also antibiotic resistance of these bacteria are important. Antibiotic resistance among bacterial agents of bloodstream infection including Acinetobacter, Klebisella, Pseudomonas, Escherichia coli, Enterobacter, Enterococcus, Staphylococcus aureus and Staphylococcus coagulase negative (CoNS) is one of the major challenges faced by physicians in treating. Therefore, this study was aimed to determine the frequency and antibiotic resistant patterns of bacterial isolates from hospitalized patient's blood cultured samples in the hospital, Tehran, Iran. Methods: This research is a descriptive and retrospective study based on recorded data in Shariati hospital laboratory and under the supervision of Tehran University of Medical Sciences. The bacterial isolates were collected from positive blood cultures from October 2013 to March 2014. The frequency of bacterial isolates were determined by phenotypic and biochemical tests. The antibiotic resistance patterns of isolated bacteria were found by disk diffusion agar method. The diameters of inhibition zone were recorded and interpreted according to Clinical and Laboratory Standards Institute (CLSI) 2013. Results: The frequency of bacterial isolates was determined among 595 positive blood cultures as followed: 41% Pseudomonas, 20% Staphylococcus epidermidis, 10% Escherichia coli, 6% Acinetobacter lwoffii, 6% Staphylococcus aureus, 5% Stenotrophomonas, 3% Acinetobacter baumannii. The antibiogram test showed that 96.2% of Acinetobacter lwoffii, 92.8% of Acinetobacter baumannii, 66% of Pseudomonas aeruginosa, 85.7% of Staphylococcus epidermidis, 65% of Staphylococcus aureus, 75% of Klebsiella, 73.7% of Escherichia coli, and 50% of Stenotrophomonas were resistant to imipenem, piperacillin, piperacillin, erythromycin, erythromycin, ciprofloxacin, trimethoprim-sulfamethoxazole, and ceftazidime respectively. Conclusion: The most prevalent bacterial isolate among the blood cultures of patients was Pseudomonas. The patients more than 50 years were more susceptible to blood stream infections. The most bacteria were isolated from the internal medicine department of hospital. The antibiotic resistance was also increasing especially in Acinetobacter, Staphylococcus coagulase negative, Escherichia coil and Klebsiell

Comparison of the prevalence of enteroviruses in blood samples of patients with and without unstable angina

BACKGROUND: Although the role of enteroviruses has been proved in heart diseases, extensive information is not available on the association between enteroviruses and unstable angina. In the present study, the authors compared the prevalence of enteroviruses in patients with and without unstable angina. METHODS: Blood samples were taken from 51 patients with unstable angina and 55 patients without unstable angina or myocardial infarction that were admitted to Imam Reza and Ghaem hospitals (Mashhad, northeast of Iran). Reverse transcription polymerase chain reaction (RT-PCR) was performed using specific primers for the detection of the enteroviruses in blood samples of study subjects. RESULTS: Patients with and without unstable angina were similar in age with mean &plusmn; standard deviation of 62.6 &plusmn; 12.8 and 59.7 &plusmn; 12.7 years, respectively (P = 0.243) and there were no differences in gender in these two groups (P = 0.174). Prevalence of the enteroviruses in patients with unstable angina was higher only in 66-80 years age group compared to the control group (patients without unstable angina, P = 0.032). There was a higher prevalence of enterovirus RNA positivity in the blood samples of women with unstable angina (75.9%) than those without unstable angina (41.7%, P = 0.011), however, no significant difference was observed in men (P = 0.983). CONCLUSION: Our data showed that enteroviral RNA positivity was higher in patients with unstable angina compared to those without unstable angina. However, the differences between the two groups were not statistically significant. &nbsp;&nbsp;</p

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BackgroundEstimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.Methods22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.FindingsGlobal all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.InterpretationGlobal adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic