Rahko (United Kingdom)

Lipids play importantmodulatory and structural roles for membraneproteins. Molecular dynamics simulations are frequently used to provideinsights into the nature of these protein–lipid interactions.Systematic comparative analysis requires tools that provide algorithmsfor objective assessment of such interactions. We introduce PyLipID,a Python package for the identification and characterization of specificlipid interactions and binding sites on membrane proteins from moleculardynamics simulations. PyLipID uses a community analysis approach forbinding site detection, calculating lipid residence times for boththe individual protein residues and the detected binding sites. Toassist structural analysis, PyLipID produces representative boundlipid poses from simulation data, using a density-based scoring function.To estimate residue contacts robustly, PyLipID uses a dual-cutoffscheme to differentiate between lipid conformational rearrangementswhile bound from full dissociation events. In addition to the characterizationof protein–lipid interactions, PyLipID is applicable to analysisof the interactions of membrane proteins with other ligands. By combiningautomated analysis, efficient algorithms, and open-source distribution,PyLipID facilitates the systematic analysis of lipid interactionsfrom large simulation data sets of multiple species of membrane proteins.

Background: Patients presenting with new onset atrial fibrillation with rapid ventricular response (AFRVR) may present with a reduced left ventricular ejection fraction (LVEF). We hypothesized that these patients have worse outcomes than patients presenting with preserved LVEF. Methods: Retrospective cohort study of 385 consecutive patients with new onset AFRVR, presenting between 01/2006 and 08/2014. Patients with a history of coronary artery disease or known cardiomyopathy were excluded. Patients were divided into those with reduced EF (REF) (LVEF ≤ 55%, n=147), and those with a preserved EF (PEF) (LVEF> 55%, n=238). Echocardiographic parameters, stroke rates, all-cause, and cardiovascular mortality were compared at baseline and a minimum of 1 year follow-up. Results: Mean age was 68 +/- 1.1 in REF versus 60 +/- 7.4 in PEF (p=0.39). There were no significant differences in baseline comorbidities. Mean LVEF was 47.7+/- 0.8% in REF vs 65.5 +/- 0.3% in PEF. The average duration of follow-up was 2.8 years. REF patients had higher all-cause mortality (32.7% REF vs 20.6% PEF, OR 2.17, p= 0.008) but similar cardiovascular mortality (15% REF vs 15.1% PEF, p=0.97). REF patients had higher rates of subsequent clinic or ER visits for AFRVR (32% REF vs 22.7% PEF, p=0.044). The incidence of stroke was similar between both groups (17% REF vs 18.9% PEF, p=0.639). Of REF patients, 29.7% had subsequent EF recovery; this subset had similar outcomes compared to PEF patients. Baseline left ventricular end diastolic diameter (LVEDD) predicted all-cause mortality (OR 1.14, p=0.003) in REF group. None of the echocardiographic parameters predicted EF recovery. Conclusion: In this cohort of patients with new AFRVR, REF was associated with higher long-term all-cause mortality and AFRVR recurrence. Those with subsequent LVEF recovery after medical therapy appear to have outcomes similar to those with PEF.

The American College of Cardiology (ACC)/American Heart Association (AHA) Task Force on Practice Guidelines regularly reviews existing guidelines to determine when an update or full revision is needed. This process gives priority to areas where major changes in text, particularly recommendations,