Project description:Current guidelines recommend that hypothyroid patients should be treated with levothyroxine, which in the vast majority of the cases leads to resolution of the symptoms and normalization of serum free T4 (FT4), T3 and TSH levels. However, a small group of hypothyroid patients remain symptomatic for neurocognitive dysfunction despite normal serum FT4 and TSH, which could be explained by localized brain hypothyroidism. More than half of the T3 in the brain is produced locally via the action of the type II deiodinase (D2) and variability/defects in this pathway could explain the residual symptoms. If this rationale is correct, adding liothyronine to the replacement therapy could prove beneficial. However, with a few exceptions, several clinical trials failed to identify any beneficial effects of combined therapy. More recently, the results of a large clinical trial revealed a better neurocognitive outcome with combined therapy only in hypothyroid patients carrying a polymorphism in the DIO2 gene. This obviously needs to be confirmed by other groups but it is tempting to speculate that combined levothyroxine and liothyronine has a place in the treatment of hypothyroidism, for some.
Project description:Multiple sclerosis is a complex, autoimmune-mediated disease of the central nervous system characterized by inflammatory demyelination and axonal/neuronal damage. The approval of various disease-modifying therapies and our increased understanding of disease mechanisms and evolution in recent years have significantly changed the prognosis and course of the disease. This update of the Multiple Sclerosis Therapy Consensus Group treatment recommendation focuses on the most important recommendations for disease-modifying therapies of multiple sclerosis in 2021. Our recommendations are based on current scientific evidence and apply to those medications approved in wide parts of Europe, particularly German-speaking countries (Germany, Austria, Switzerland).
Project description:A healthy lifestyle, myocardial revascularisation and medical therapy constitute the three pillars for the treatment of ischaemic heart disease. Lifestyle and optimal medical therapy should be used in all cases. However, the selection of cases for revascularisation among stable patients remains controversial. The ISCHEMIA trial compared an early invasive strategy with revascularisation plus optimal medical therapy against initial optimal medical therapy alone with revascularisation reserved for cases in which symptom control was insufficient. The study included over 5,000 patients with stable coronary artery disease and moderate to severe myocardial ischaemia. No differences were found in relevant clinical outcomes, including all-cause mortality, cardiovascular death, MI, heart failure and stroke, over a follow-up of 3.2 years. Conversely, angina control was better in patients with severe symptomatic angina. Following the tradition of all trials comparing medical therapy alone with revascularisation, the ISCHEMIA trial results are controversial, but an analysis of the design and results of the trial offers important information to better understand, evaluate and treat the growing number of patients with stable chronic ischaemic heart disease and moderate to severe myocardial ischaemia.
Project description:Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on www.ofcaus.org, where SAS and Stata code for analysis is also provided.
Project description:COVID-19 has caused extensive human casualties with significant economic impacts around the globe, and has imposed new challenges on health systems worldwide. Over the past decade, SARS, Ebola, and Zika also led to significant concerns among the scientific community. Interestingly, the SARS and Zika epidemics ended before vaccine development; however, the scholarly community and the pharmaceutical companies responded very quickly at that time. Similarly, when the genetic sequence of SARSCoV-2 was revealed, global vaccine companies and scientists have stepped forward to develop a vaccine, triggering a race toward vaccine development that the whole world is relying on. Similarly, an effective and safe vaccine could play a pivotal role in eradicating COVID-19. However, few important questions regarding SARS-CoV-2 vaccine development are explored in this review.
Project description:Chromosomally integrated human herpesvirus 6 (ciHHV-6) is a condition in which the complete HHV-6 genome is integrated into the host germ line genome and is vertically transmitted in a Mendelian manner. The condition is found in less than 1% of controls in the USA and UK, but has been found at a somewhat higher prevalence in transplant recipients and other patient populations in several small studies. HHV-6 levels in whole blood that exceed 5.5 log10 copies/ml are strongly suggestive of ciHHV-6. Monitoring DNA load in plasma and serum is unreliable, both for identifying and for monitoring subjects with ciHHV-6 due to cell lysis and release of cellular DNA. High HHV-6 DNA loads associated with ciHHV-6 can lead to erroneous diagnosis of active infection. Transplant recipients with ciHHV-6 may be at increased risk for bacterial infection and graft rejection. ciHHV-6 can be induced to a state of active viral replication in vitro. It is not known whether ciHHV-6 individuals are put at clinical risk by the use of drugs that have been associated with HHV-6 reactivation in vivo or in vitro. Nonetheless, we urge careful observation when use of such drugs is indicated in individuals known to have ciHHV-6. Little is known about whether individuals with ciHHV-6 develop immune tolerance for viral proteins. Further research is needed to determine the role of ciHHV-6 in disease.
Project description:Automatic summarization of natural language is a widely studied area in computer science, one that is broadly applicable to anyone who needs to understand large quantities of information. In the medical domain, automatic summarization has the potential to make health information more accessible to people without medical expertise. However, to evaluate the quality of summaries generated by summarization algorithms, researchers first require gold standard, human generated summaries. Unfortunately there is no available data for the purpose of assessing summaries that help consumers of health information answer their questions. To address this issue, we present the MEDIQA-Answer Summarization dataset, the first dataset designed for question-driven, consumer-focused summarization. It contains 156 health questions asked by consumers, answers to these questions, and manually generated summaries of these answers. The dataset's unique structure allows it to be used for at least eight different types of summarization evaluations. We also benchmark the performance of baseline and state-of-the-art deep learning approaches on the dataset, demonstrating how it can be used to evaluate automatically generated summaries.