Alpha shapes: Determining 3D shape complexity across morphologically diverse structures

Background. Following recent advances in bioimaging, high-resolution 3D models of biological structures are now generated rapidly and at low-cost. To utilise this data to address evolutionary and ecological questions, an array of tools has been developed to conduct 3D shape analysis and quantify topographic complexity. Here we focus particularly on shape techniques applied to irregular-shaped objects lacking clear homologous landmarks, and propose the new ‘alpha-shapes’ method for quantifying 3D shape complexity. Methods. We apply alpha-shapes to quantify shape complexity in the mammalian baculum as an example of a morphologically disparate structure. Micro- computed-tomography (μCT) scans of bacula were conducted. Bacula were binarised and converted into point clouds. Following application of a scaling factor to account for absolute differences in size, a suite of alpha-shapes was fitted to each specimen. An alpha shape is a formed from a subcomplex of the Delaunay triangulation of a given set of points, and ranges in refinement from a very coarse mesh (approximating convex hulls) to a very fine fit. ‘Optimal’ alpha was defined as the degree of refinement necessary in order for alpha-shape volume to equal CT voxel volume, and was taken as a metric of overall shape ‘complexity’. Results Our results show that alpha-shapes can be used to quantify interspecific variation in shape ‘complexity’ within biological structures of disparate geometry. The ‘stepped’ nature of alpha curves is informative with regards to the contribution of specific morphological features to overall shape ‘complexity’. Alpha-shapes agrees with other measures of topographic complexity (dissection index, Dirichlet normal energy) in identifying ursid bacula as having low shape complexity. However, alpha-shapes estimates mustelid bacula as possessing the highest topographic complexity, contrasting with other shape metrics. 3D fractal dimension is found to be an inappropriate metric of complexity when applied to bacula. Conclusions. The alpha-shapes methodology can be used to calculate ‘optimal’ alpha refinement as a proxy for shape ‘complexity’ without identifying landmarks. The implementation of alpha-shapes is straightforward, and is automated to process large datasets quickly. Beyond genital shape, we consider the alpha-shapes technique to hold considerable promise for new applications across evolutionary, ecological and palaeoecological disciplines

Treatment costs for acute myocardial infarction in the Netherlands

Background This study aimed to calculate the treatment costs of acute myocardial infarction (AMI) in the Netherlands for 2012. Also, the degree of association between treatment costs of AMI and some patient and hospital characteristics was examined. Methods For this retrospective cost analysis, patients were drawn from the database of the Diagnosis Treatment Combination (Diagnose Behandeling Combinatie, DBC) casemix system, which contains data on the resource use of all hospitalisations in the Netherlands. All costs were based on Euro 2012 cost data. Results The analysis was based on data of 25,657 patients. Mean treatment costs were estimated at € 5021, with significant cost increases for patients with percutaneous coronary intervention (PCI) treatment. ST-segment elevation myocardial infarction (STEMI) patients receiving thrombolysis incurred the lowest (€ 4286), while non-STEMI patients receiving PCI the highest costs (€ 6060). Length of stay and hosp

Lung morphometry after repetitive antenatal glucocorticoid treatment in preterm sheep

Antenatal glucocorticoids are thought to be less effective when delivery occurs more than 7 d after initiation of treatment; therefore, repeat courses are often administered. We examined lung structure after single or repetitive antenatal glucocorticoid injections in fetal sheep. Pregnant ewes received single or repetitive doses of 0.5 mg/kg betamethasone at 7-d intervals by maternal or fetal injection, beginning at D104 or D114 with delivery at D125, D135, or D146 gestation (term = 150 d). Changes in lung structure were more pronounced after repetitive versus single injections. Repetitive fetal or maternal injections beginning at D104 (delivery at D125) resulted in comparable structural changes: alveolar volume increased by 50 to 80%, alveolar numerical density decreased by 30 to 40%, and pleural and interlobular septal volumes decreased by as much as 70%. Similar changes were seen in animals delivered at D135 after repetitive maternal injections beginning at D114. There were no structural differences between control and repetitive betamethasone animals when delivery was delayed until D146, indicating that betamethasone induced structural changes were reversible

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered