Standard intracranial in vivo animal models of delayed cerebral vasospasm

Animal models provide a basis for clarifying the complex pathogenesis of delayed cerebral vasospasm (DCVS) and for screening of potential therapeutic approaches. Arbitrary use of experimental parameters in current models can lead to results of uncertain relevance. The aim of this work was to identify and analyze the most consistent and feasible models and their parameters for each animal.

Cartilage, bone and synovial histomorphometry in animal models of osteoarthritis

http://www.ncbi.nlm.nih.gov/pubmed/20864016

Automated detection of the osseous acetabular rim using three-dimensional models of the pelvis

An automated algorithm for detection of the acetabular rim was developed. Accuracy of the algorithm was validated in a sawbone study and compared against manually conducted digitization attempts, which were established as the ground truth. The latter proved to be reliable and reproducible, demonstrated by almost perfect intra- and interobserver reliability. Validation of the automated algorithm showed no significant difference compared to the manually acquired data in terms of detected version and inclination. Automated detection of the acetabular rim contour and the spatial orientation of the acetabular opening plane can be accurately achieved with this algorithm.

Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na(+)-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.