Heterogeneity of fractional anisotropy and mean diffusivity measurements by in vivo diffusion tensor imaging in normal human hearts

Background: Cardiac diffusion tensor imaging (cDTI) by cardiovascular magnetic resonance has the potential to assess microstructural changes through measures of fractional anisotropy (FA) and mean diffusivity (MD). However, normal variation in regional and transmural FA and MD is not well described. Methods: Twenty normal subjects were scanned using an optimised cDTI sequence at 3T in systole. FA and MD were quantified in 3 transmural layers and 4 regional myocardial walls. Results: FA was higher in the mesocardium (0.46 ±0.04) than the endocardium (0.40 ±0.04, p≤0.001) and epicardium (0.39 ±0.04, p≤0.001). On regional analysis, the FA in the septum was greater than the lateral wall (0.44 ±0.03 vs 0.40 ±0.05 p = 0.04). There was a transmural gradient in MD increasing towards the endocardium (epicardium 0.87 ±0.07 vs endocardium 0.91 ±0.08×10-3 mm2/s, p = 0.04). With the lateral wall (0.87 ± 0.08×10-3 mm2/s) as the reference, the MD was higher in the anterior wall (0.92 ±0.08×10-3 mm2/s, p = 0.016) and septum (0.92 ±0.07×10-3 mm2/s, p = 0.028). Transmurally the signal to noise ratio (SNR) was greatest in the mesocardium (14.5 ±2.5 vs endocardium 13.1 ±2.2, p<0.001; vs epicardium 12.0 ± 2.4, p<0.001) and regionally in the septum (16.0 ±3.4 vs lateral wall 11.5 ± 1.5, p<0.001). Transmural analysis suggested a relative reduction in the rate of change in helical angle (HA) within the mesocardium. Conclusions: In vivo FA and MD measurements in normal human heart are heterogeneous, varying significantly transmurally and regionally. Contributors to this heterogeneity are many, complex and interactive, but include SNR, variations in cardiac microstructure, partial volume effects and strain. These data indicate that the potential clinical use of FA and MD would require measurement standardisation by myocardial region and layer, unless pathological changes substantially exceed the normal variation identified

Species of Triconia of the conifera-subgroup (Copepoda, Cyclopoida, Oncaeidae) from Korean waters, including a new species

Three species of Triconia belonging to the conifera-subgroup are described from Korean waters (NW Pacific). A new species, Triconia hirsuta, was found that can be distinguished from its sibling T. conifera by a paired patch of long setules on the anterolateral margin of the genital double-somite in the female, and an unusual position of caudal seta II branching off at almost a right angle to the caudal ramus. Males have not been found so far. Both sexes of T. conifera (Giesbrecht, 1891) and T. borealis (Sars, 1918) from Korean waters are redescribed with the addition of morphological details and/or differences not noted in earlier descriptions. Morphological characters of Korean T. conifera are in close agreement with specimens described from the Mediterranean Sea and the Pacific, but differ in several parameters from T. conifera from the Red Sea, which is regarded as a distinct, though closely related, species requiring re-examination. Published data on the community structure and zoogeographical distribution of oncaeids of the conifera-type in the NW Pacific are summarized, and the remaining uncertainties concerning the distribution of species of the conifera-subgroup in these areas are pointed out

Increasing consistency of disease biomarker prediction across datasets

Microarray studies with human subjects often have limited sample sizes which hampers the ability to detect reliable biomarkers associated with disease and motivates the need to aggregate data across studies. However, human gene expression measurements may be influenced by many non-random factors such as genetics, sample preparations, and tissue heterogeneity. These factors can contribute to a lack of agreement among related studies, limiting the utility of their aggregation. We show that it is feasible to carry out an automatic correction of individual datasets to reduce the effect of such 'latent variables' (without prior knowledge of the variables) in such a way that datasets addressing the same condition show better agreement once each is corrected. We build our approach on the method of surrogate variable analysis but we demonstrate that the original algorithm is unsuitable for the analysis of human tissue samples that are mixtures of different cell types. We propose a modification to SVA that is crucial to obtaining the improvement in agreement that we observe. We develop our method on a compendium of multiple sclerosis data and verify it on an independent compendium of Parkinson's disease datasets. In both cases, we show that our method is able to improve agreement across varying study designs, platforms, and tissues. This approach has the potential for wide applicability to any field where lack of inter-study agreement has been a concern. © 2014 Chikina, Sealfon

Determinants of myocardial energetics and efficiency in symptomatic hypertrophic cardiomyopathy

Next to hypertrophy, hypertrophic cardiomyopathy (HCM) is characterized by alterations in myocardial energetics. A small number of studies have shown that myocardial external efficiency (MEE), defined by external work (EW) in relation to myocardial oxidative metabolism (MVO2), is reduced. The present study was conducted to identify determinants of MEE in patients with HCM by use of dynamic positron emission tomography (PET) and cardiovascular magnetic resonance imaging (CMR). Twenty patients with HCM (12 men, mean age: 55.2 +/- 13.9 years) and 11 healthy controls (7 men, mean age: 48.1 +/- 10 years) were studied with [C-11]acetate PET to assess MVO2. CMR was performed to determine left ventricular (LV) volumes and mass (LVM). Univariate and multivariate analyses were employed to determine independent predictors of myocardial efficiency. Between study groups, MVO2 (controls: 0.12 +/- 0.04 ml center dot min(-1)center dot g(-1), HCM: 0.13 +/- 0.05 ml center dot min(-1)center dot g(-1), p = 0.64) and EW (controls: 9,139 +/- 2,484 mmHg center dot ml, HCM: 9,368 +/- 2,907 mmHg center dot ml, p = 0.83) were comparable, whereas LVM was significantly higher (controls: 99 +/- 21 g, HCM: 200 +/- 76 g, p < 0.001) and MEE was decreased in HCM patients (controls: 35 +/- 8%, HCM: 21 +/- 10%, p < 0.001). MEE was related to stroke volume (SV), LV outflow tract gradient, NH2-terminal pro-brain natriuretic peptide (NT-proBNP) and serum free fatty acid levels (all p < 0.05). Multivariate analysis revealed that SV ( = 0.74, p < 0.001) and LVM ( = -0.43, p = 0.013) were independently related to MEE. HCM is characterized by unaltered MVO2, impaired EW generation per gram of myocardial tissue and subsequent deteriorated myocardial efficiency. Mechanical external efficiency could independently be predicted by SV and LVM