Munckbarker8233

Higher A1C and fasting blood glucose values were found to be associated with higher LDL cholesterol levels. Twenty-seven percent of patients with indications for treatment with statins were receiving them. Of those being treated with statins, 42.6% had an LDL cholesterol level ≥100 mg/dL.

In South Indian patients with type 2 diabetes and fair glycemic control, high LDL cholesterol is the predominant lipid abnormality. There remains a huge potential for ASCVD risk reduction in this population if the knowledge practice gap is addressed.

In South Indian patients with type 2 diabetes and fair glycemic control, high LDL cholesterol is the predominant lipid abnormality. There remains a huge potential for ASCVD risk reduction in this population if the knowledge practice gap is addressed.Coronavirus disease 2019 (COVID-19), a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global health concern, as the World Health Organization declared this outbreak to be a global pandemic in March 2020. The need for an effective treatment is urgent because the development of an effective vaccine may take years given the complexity of the virus and its rapid mutation. One promising treatment target for COVID-19 is SARS-CoV-2 main protease. Thus, this study was aimed to examine whether Sulawesi propolis compounds produced by Tetragonula sapiens inhibit the enzymatic activity of SARS-CoV-2 main protease. In this study, molecular docking was performed to analyze the interaction profiles of propolis compounds with SARS-CoV-2 main protease. The results illustrated that two compounds, namely glyasperin A and broussoflavonol F, are potential drug candidates for COVID-19 based on their binding affinity of -7.8 kcal/mol and their ability to interact with His41 and Cys145 as catalytic sites. Both compounds also displayed favorable interaction profiles with SARS-CoV-2 main protease with binding similarities compared to inhibitor 13b as positive control 63% and 75% respectively.The COVID-19 pandemic has produced mass market failure in global private health, particularly in tertiary care. Low-and-middle income countries (LMICs) dependent on private providers as a consequence of neglect of national health systems or imposed conditionalities under neoliberal governance were particularly effected. When beds were most needed for the treatment of acute COVID-19 cases, private providers suffered a liquidity crisis, itself propelled by the primary effects of lockdowns, government regulations and patient deferrals, and the secondary economic impacts of the pandemic. This led to a private sector response-involving, variously, hospital closures, furloughing of staff, refusals of treatment, and attempts to profit by gouging patients. A crisis in state and government relations has multiplied across LMICs. Amid widespread national governance failures-either crisis bound or historic-with regards to poorly resourced public health services and burgeoning private health-governments have responded with increasing legal and financial interventions into national health markets. In contrast, multilateral governance has been path dependent with regard to ongoing commitments to privately provided health. Indeed, the global financial institutions appear to be using the COVID crisis as a means to recommit to the roll out of markets in global health, this involving the further scaling back of the state.This review draws pragmatic lessons for developing countries to address COVID-19-induced recessions and to sustain a developmental recovery. These recessions are unique, caused initially by supply disruptions, largely due to government-imposed 'stay-in-shelter lockdowns'. These have interacted with falling incomes and demand, declining exports (and imports), collapsing commodity prices, shrinking travel and tourism, decreasing remittances and foreign exchange shortages. Highlighting implications for employment, wellbeing and development, it argues that governments need to design comprehensive relief measures and recovery policies to address short-term problems. These should prevent cash-flow predicaments from becoming full-blown solvency crises. Instead of returning to the status quo ante, developing countries' capacities and capabilities need to be enhanced to address long-term sustainable development challenges. Multilateral financial institutions should intermediate with financial sources at low cost to supplement the International Monetary Fund's Special Drawing Rights to lower borrowing costs for relief and recovery.The premises of the feminist economist tradition from the Global South center their analysis in the wellbeing of people and the planet, under the human rights framework, gender equality and environmental integrity, as cross-cutting principles. The pandemic brought to the surface what the feminist movement has been saying all along, namely that the wellbeing of persons, and the planet they live in, depends on a complex web of elements beyond a limited notion of bodily health. The current capitalistic system has always kindled a tension between life and profits, a game that has undermined human rights of all persons by prioritizing the circulation of merchandises, goods and capitals. SU056 That struggle is more acutely felt now with the confinement measures imposed all around the world, and the ensuing impossibility for millions of people in precarious circumstances of respecting the lockdown measures. Women are even more carrying the burden of subsidizing entire economies. The feminist movement is now looking at solutions of solidarity at the crossroad between and within social movements, public policy, local and community resistance, while refusing to go back to a world where women may have to subsidize even more entire economies under recession.Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past. In this work, we present a deep learning forecasting framework which is capable to predict tomorrow's news topics on Twitter and news feeds based on yesterday's content and topic-interaction features. The proposed framework starts by generating topics from words using word embeddings and K-means clustering. Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics. Structural and dynamic metrics calculated from networks along with content features and past activity, are used as input of a long short-term memory (LSTM) model, which predicts the number of mentions of a specific topic on the subsequent day. Utilizing dependencies among topics, our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic's historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.