The major structural elements of retroviruses are contained in a single polyprotein, Gag, which in human immunodeficiency virus type 1 (HIV-1) comprises the MA, CA, spacer peptide 1 (SP1), NC, SP2, and p6 polypeptides. In the immature HIV-1 virion, the domains of Gag are arranged radially with the N-terminal MA domain at the membrane and C-terminal NC-SP2-p6 region nearest to the center. Here, we report the three-dimensional structures of individual immature HIV-1 virions, as obtained by electron cryotomography. The concentric shells of the Gag polyprotein are clearly visible, and radial projections of the different Gag layers reveal patches of hexagonal order within the CA and SP1 shells. Averaging well-ordered unit cells leads to a model in which each CA hexamer is stabilized by a bundle of six SP1 helices. This model suggests why the SP1 spacer is essential for assembly of the Gag lattice and how cleavage between SP1 and CA acts as a structural switch controlling maturation.

BackgroundHealthcare costs are increasing rapidly and at an unsustainable rate in many countries, and inpatient hospitalizations are a significant driver of these costs. Clinical decision support (CDS) represents a promising approach to not only improve care but to reduce costs in the inpatient setting. The purpose of this study was to systematically review trials of CDS interventions with the potential to reduce inpatient costs, so as to identify promising interventions for more widespread implementation and to inform future research in this area.MethodsTo identify relevant studies, MEDLINE was searched up to July 2013. CDS intervention studies with the potential to reduce inpatient healthcare costs were identified through titles and abstracts, and full text articles were reviewed to make a final determination on inclusion. Relevant characteristics of the studies were extracted and summarized.ResultsFollowing a screening of 7,663 articles, 78 manuscripts were included. 78.2% of studies were controlled before-after studies, and 15.4% were randomized controlled trials. 53.8% of the studies were focused on pharmacotherapy. The majority of manuscripts were published during or after 2008. 70.5% of the studies resulted in statistically and clinically significant improvements in an explicit financial measure or a proxy financial measure. Only 12.8% of the studies directly measured the financial impact of an intervention, whereas the financial impact was inferred in the remainder of studies. Data on cost effectiveness was available for only one study.ConclusionsSignificantly more research is required on the impact of clinical decision support on inpatient costs. In particular, there is a remarkable gap in the availability of cost effectiveness studies required by policy makers and decision makers in healthcare systems.

Background: Relapsing-remitting multiple sclerosis (RRMS) has a major impact on affected patients; therefore, improved understanding of RRMS is important, particularly in the context of real-world evidence. Objectives: To develop and validate algorithms for identifying patients with RRMS in both unstructured clinical notes found in electronic health records (EHRs) and structured/coded health care claims data. Methods: US Integrated Delivery Network data (2010 e2014) were queried for study inclusion criteria (possible multiple sclerosis [MS] base cohort): one or more MS diagnosis code, patients aged 18 years or older, 1 year or more baseline history, and no other demyelinating diseases. Sets of algorithms were developed to search narrative text of unstructured clinical notes (EHR clinical notesebased algorithms) and structured/coded data (claims-based algorithms) to identify adult patients with RRMS, excluding patients with evidence of progressive MS. Medical records were reviewed manually for algorithm validation. Positive predictive value was calculated for both EHR clinical notesebased and claims-based algorithms. Results: From a sample of 5308 patients with possible MS, 837 patients with RRMS were identified using only the EHR clinical notesebased algorithms and 2271 patients were identified using only the claims-based algorithms; 779 patients were identified using both algorithms. The positive predictive value was 99.1% (95% confidence interval [CI], 94.2% e100%) for the EHR clinical notesebased algorithms and 94.6% (95% CI, 89.1%e97.8%) to 94.9% (95% CI, 89.8%e97.9%) for the claims-based algorithms. Conclusions: The algorithms evaluated in this study identified a real-world cohort of patients with RRMS without evidence of progressive MS that can be studied in clinical research with confidence.

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Clinical decision support interventions are typically heterogeneous in nature, making it difficult to identify why some interventions succeed while others do not. One approach to identify factors important to the success of health information systems is the use of meta-regression techniques, in which potential explanatory factors are correlated with the outcome of interest. This approach, however, can result in misleading conclusions due to several issues. In this manuscript, we present a cautionary case study in the context of clinical decision support systems to illustrate the limitations of this type of analysis. We then discuss implications and recommendations for future work aimed at identifying success factors of medical informatics interventions. In particular, we identify the need for head-to-head trials in which the importance of system features is directly evaluated in a prospective manner.