Antimicrobial activities of a novel biflavonoid and other constituents from Rhus natalensis

Phytochemical studies on Rhus natalensis root bark collected from Kenya led to the isolation and identification of a new biflavonoid (3-(1-(2,4-dihydroxyphenyl)-3,3-bis(4-hydroxyphenyl)-1-oxopropan-2- yl)-7-methoxy-4H-chromone-4-one (1), named rhuschromone, in addition to two other known compounds; 2',4'-dihydroxychalcone-(4-O-5''')-4'',2''',4'''-trihydroxychalcone (2) and 3-((Z)-heptadec-13- enyl) benzene-1,2-diol (3). The chemical structures of the isolated compounds were established using spectroscopic techniques including high field nuclear magnetic resonance (NMR). The total extracts and the isolated compounds were tested for their antimicrobial activities against different strains of bacteria

Ent-kaurene and ent-beyerene diterpenoids and other constituents of Thecacoris batesii

Two novel diterpenoids, thecacorins A (1) and B (2), were isolated from Thecacoris batesii and their structures were established as ent-3b,20-epoxy-16-kaurene-3a,12b-diol and ent-15-beyerene-2b,3b-diol, respectively, on the basis of extensive spectroscopic analysis, especially, 1D NMR spectra, in conjunction with 2D experiments, COSY, NOESY, HMQC and HMBC. KEY WORDS: Diterpenoids, Thecacorin A, Thecacorin B, Ent-3b,20-epoxy-16-kaurene-3a,12b-diol, Ent-15-beyerene-2b,3b-diol, Thecacoris batesii  Bull. Chem. Soc. Ethiop. 2007, 21(1), 89-94

ISOLATION AND CHARACTERIZATION OF CHEMICAL CONSTITUENTS FROM Chrysophyllum albidum G. DON-HOLL. STEM-BARK EXTRACTS AND THEIR ANTIOXIDANT AND ANTIBACTERIAL PROPERTIES

Background: The plant, Chrysophyllum albidum is indigenous to Nigeria and its stem-bark has found relevance in folkloric medicine for infections and oxidative stress linked diseases medicaments. The study targets to isolate the chemical constituents accountable for the antioxidant and antibacterial actions of the plant stem-bark to substantiate some of its ethnomedicinal uses. Materials and Methods: Stem-bark extract of Chrysophyllum albidum was obtained from 80 % ethanol was partitioned in sequence with ethyl acetate (EtOAc) and n-butanol. The solvent fractions and isolated compounds were verified for antioxidant chattels utilizing 2-2-diphenyl-1-picrylhydrazyl. Antibacterial actions were also assessed by agency of agar-diffusion and broth micro dilution methods. EtOAc fraction was on many occasions chromatographed on silica and Sephadex LH-20 column to afford four compounds and their chemical structures were proven with the employment of NMR (1D and 2D) and MS. Results: Chromatographic fractionation of EtOAc fraction with the premier antioxidant and antimicrobial activities afforded stigmasterol (1), epicatechin (2), epigallocatechin (3) and procyanidin B5 (4). Procyanidin B5 isolated for the first time from Chrysophyllum genus proven the supreme antioxidant activity with IC50 values of 8.8 µM and 11.20 µM in DPPH and nitric oxide assays respectively and equally established the ultmost inhibitory activity against Escherichia coli (MIC 156.25 μg/mL), Staphylococcus aureus (MIC 156.25 μg/mL), Pseudomonas aeruginosa (MIC 625 μg/mL) and Bacillus subtilis (MIC 156.25 μg/mL). Conclusion: The antibacterial and antioxidant activities of epicatechin, epigallocatechin and procyanidin B5 isolated from Chrysophyllum albidum stem-bark substantiate the folkloric use

NEAT: An efficient network enrichment analysis test

Background: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. Results: We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. Conclusions: NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat )

Spatio-temporal land use/cover dynamics and its implication for sustainable land use in Wanka watershed, northwestern highlands of Ethiopia

Long-term land use and land cover (LULC) dynamics information is essential to understand the trends and make necessary land management interventions, such as in the highlands of Ethiopia. This study analyzed six decades of LULC dynamics of Wanka watershed, Northwestern Ethiopian highlands. Two sets of aerial photographs (1957 and 2017), SPOT 5 and sentinel satellite imageries were analyzed. In addition, key informant interviews, focus group discussions and field observations were used to identify the drivers and impact of LULC change. It was found that cultivated and rural settlement land (CRSL), bare land, and urban built up area have been continuously expanded at the expenses of mainly forest and shrub lands. Over the entire study period (1957–2017) while the bare land and CRSL have increased by about 59% and 20% respectively, forest and shrub lands have declined by 59% and 57% respectively. Urban built up area has also expanded. The impact of popula- tion pressure and expansion of CRSL land were considerable. The trend of LULC dynamics in the study watershed implies adverse impact on the quality and quantity of the land resource. Hence, appropriate land use planning and strategies that reduce expansion of cultivated land need to be practiced

Global burden of 87 risk factors in 204 countries and territories, 1990–2019 : a systematic analysis for the Global Burden of Disease Study 2019

Background Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk-outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk-outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk-outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10.8 million (95% uncertainty interval [UI] 9.51-12.1) deaths (19.2% [16.9-21.3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8.71 million (8.12-9.31) deaths (15.4% [14.6-16.2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253-350) DALYs (11.6% [10.3-13.1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0-9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10-24 years, alcohol use for those aged 25-49 years, and high systolic blood pressure for those aged 50-74 years and 75 years and older. Interpretation Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public

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 development 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

The Gaussian graphical model in cross-sectional and time-series data

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in 3 kinds of psychological datasets: datasets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered datasets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means---the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.Comment: Accepted pending revision in Multivariate Behavioral Researc

Tracking development assistance for health and for COVID-19 : a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990–2050

Background: The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods: We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings: In 2019, health spending globally reached$8·8 trillion (95% uncertainty interval [UI] 8·7–8·8) or $1132 (1119–1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total,$40·4 billion (0·5%, 95% UI 0·5–0·5) was development assistance for health provided to low-income and middle-income countries, which made up 24·6% (UI 24·0–25·1) of total spending in low-income countries. We estimate that $54·8 billion in development assistance for health was disbursed in 2020. Of this,$13·7 billion was targeted toward the COVID-19 health response. $12·3 billion was newly committed and$1·4 billion was repurposed from existing health projects. $3·1 billion (22·4$2·4 billion (17·9%) was for supply chain and logistics. Only $714·4 million (7·7$1519 (1448–1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation: Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Funding: Bill & Melinda Gates Foundation