The burden of undertreatment and non-treatment among patients with non-valvular atrial fibrillation and elevated stroke risk: a systematic review.

Objective: Global treatment guidelines recommend treatment with oral anticoagulants (OACs) for patients with non-valvular atrial fibrillation (NVAF) and an elevated stroke risk. However, not all patients with NVAF and an elevated stroke risk receive guideline-recommended therapy. A literature review and synthesis of observational studies were undertaken to identify the body of evidence on untreated and undertreated NVAF and the association with clinical and economic outcomes.

The IMPact of untReated nOn-Valvular atrial fibrillation on short-tErm clinical and economic outcomes in the US Medicare population: the IMPROVE-AF model.

Background: Despite treatment guidelines recommending the use of oral anticoagulants (OACs) for patients with non-valvular atrial fibrillation (NVAF) and moderate to high risk of stroke (CHADS-VASc score ≥1), many patients remain untreated. A study conducted among Medicare beneficiaries with AF and a CHADS-VASc score of ≥2 found that 51% of patients were not prescribed an OAC despite being eligible for treatment. When left untreated, NVAF poses an enormous burden to society, as stroke events are estimated to cost the US healthcare system about $34 billion each year in both direct medical costs and indirect productivity losses.

Cost-utility analyses of targeted immunomodulators in rheumatoid arthritis: systematic review.

Cost-utility (CU) modeling is a common technique used to determine whether new treatments represent good value for money. As with any modeling exercise, findings are a direct result of methodology choices, which may vary widely. Several targeted immuno-modulators have been launched in recent years to treat moderate-to-severe rheumatoid arthritis (RA) which have been evaluated using CU methods.

Integrating high-content screening and ligand-target prediction to identify mechanism of action.

High-content screening is transforming drug discovery by enabling simultaneous measurement of multiple features of cellular phenotype that are relevant to therapeutic and toxic activities of compounds. High-content screening studies typically generate immense datasets of image-based phenotypic information, and how best to mine relevant phenotypic data is an unsolved challenge. Here, we introduce factor analysis as a data-driven tool for defining cell phenotypes and profiling compound activities.

A support vector machine classifier for recognizing mitotic subphases using high-content screening data.

High-content screening studies of mitotic checkpoints are important for identifying cancer targets and developing novel cancer-specific therapies. A crucial step in such a study is to determine the stage of cell cycle. Due to the overwhelming number of cells assayed in a high-content screening experiment and the multiple factors that need to be taken into consideration for accurate determination of mitotic subphases, an automated classifier is necessary.