Stroudgoodman0983

Enhancing financial protection in health is one of the main goals of Iran's health transformation program (HTP), a recent reform conducted in early 2014. This study aimed to measure financial protection using the fair financial contribution index (FFCI) in urban and rural areas before (2008-2013) and after (2014-2018) the HTP implementation. Using a retrospective study on annual national cross-sectional surveys of households' income and expenditure, FFCI was measured. The total sample sizes for urban and rural areas from 2008 to 2018 were 207,980 and 212,249 households, respectively.

The worst fair contributions to health expenditure in urban (FFCI = 0.684) and rural areas (FFCI = 0.530) were related to 2010 and 2009, respectively. learn more Otherwise, the best fair contributions for urban (FFCI = 0.858) and rural (FFCI = 0.836) areas were made in 2011. Before the HTP implementation began, FFCI showed minor changes from 0.834 in 2008 to 0.833 in 2013. Following the HTP implementation, the FFCI values in urban and rural populations declined (worsened) from 0.842 to 0.836 and 0.816 to 0.809, respectively.On average more fair financial contributions had been made following five years after the HTP, especially in rural areas, but less than that expected in upstream documents (as determined 0.9).

The worst fair contributions to health expenditure in urban (FFCI = 0.684) and rural areas (FFCI = 0.530) were related to 2010 and 2009, respectively. Otherwise, the best fair contributions for urban (FFCI = 0.858) and rural (FFCI = 0.836) areas were made in 2011. Before the HTP implementation began, FFCI showed minor changes from 0.834 in 2008 to 0.833 in 2013. Following the HTP implementation, the FFCI values in urban and rural populations declined (worsened) from 0.842 to 0.836 and 0.816 to 0.809, respectively.On average more fair financial contributions had been made following five years after the HTP, especially in rural areas, but less than that expected in upstream documents (as determined 0.9).

Coronavirus disease 2019 (COVID-19) is a global health problem that causes millions of deaths worldwide. The clinical manifestation of COVID-19 widely varies from asymptomatic infection to severe pneumonia and systemic inflammatory disease. It is thought that host genetic variability may affect the host's response to the virus infection and thus cause severity of the disease. The SARS-CoV-2 virus requires interaction with its receptor complex in the host cells before infection. The transmembrane protease serine 2 (TMPRSS2) has been identified as one of the key molecules involved in SARS-CoV-2 virus receptor binding and cell invasion. Therefore, in this study, we investigated the correlation between a genetic variant within the human TMPRSS2 gene and COVID-19 severity and viral load.

We genotyped 95 patients with COVID-19 hospitalised in Dr Soetomo General Hospital and Indrapura Field Hospital (Surabaya, Indonesia) for the TMPRSS2 p.Val160Met polymorphism. Polymorphism was detected using a TaqMan assay. We then analysed the association between the presence of the genetic variant and disease severity and viral load. We did not observe any correlation between the presence of TMPRSS2 genetic variant and the severity of the disease. However, we identified a significant association between the p.Val160Met polymorphism and the SARS-CoV-2 viral load, as estimated by the Ct value of the diagnostic nucleic acid amplification test. Furthermore, we observed a trend of association between the presence of the C allele and the mortality rate in patients with severe COVID-19.

Our data indicate a possible association between TMPRSS2 p.Val160Met polymorphism and SARS-CoV-2 infectivity and the outcome of COVID-19.

Our data indicate a possible association between TMPRSS2 p.Val160Met polymorphism and SARS-CoV-2 infectivity and the outcome of COVID-19.

While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility.

Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb)) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed wements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at http//cpag.oit.duke.edu and the software code at https//github.com/tbalmat/iCPAGdb .

Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at http//cpag.oit.duke.edu and the software code at https//github.com/tbalmat/iCPAGdb .Epigenetics studies heritable genomic modifications that occur with the participation of epigenetic modifying enzymes but without alterations of the nucleotide structure. Small-molecule inhibitors of these epigenetic modifying enzymes are known as epigenetic drugs (epi-drugs), which can cause programmed death of tumor cells by affecting the cell cycle, angiogenesis, proliferation, and migration. Epi-drugs include histone methylation inhibitors, histone demethylation inhibitors, histone deacetylation inhibitors, and DNA methylation inhibitors. Currently, epi-drugs undergo extensive development, research, and application. Although epi-drugs have convincing anti-tumor effects, the patient's sensitivity to epi-drug application is also a fundamental clinical issue. The development and research of biomarkers for epi-drugs provide a promising direction for screening drug-sensitive patients. Here, we review the predictive biomarkers of 12 epi-drugs as well as the progress of combination therapy with chemotherapeutic drugs or immunotherapy.