2D high resolution vs. 3D whole heart myocardial perfusion cardiovascular magnetic resonance.

AIMS: Developments in myocardial perfusion cardiovascular magnetic resonance (CMR) allow improvements in spatial resolution and/or myocardial coverage. Whole heart coverage may provide the most accurate assessment of myocardial ischaemic burden, while high spatial resolution is expected to improve detection of subendocardial ischaemia. The objective of this study was to compare myocardial ischaemic burden as depicted by 2D high resolution and 3D whole heart stress myocardial perfusion in patients with coronary artery disease. METHODS AND RESULTS: Thirty-eight patients [age 61 ± 8 (21% female)] underwent 2D high resolution (spatial resolution 1.2 mm2) and 3D whole heart (in-plane spatial resolution 2.3 mm2) stress CMR at 3-T in randomized order. Myocardial ischaemic burden (%) was visually quantified as perfusion defect at peak stress perfusion subtracted from subendocardial myocardial scar and expressed as a percentage of the myocardium. Median myocardial ischaemic burden was significantly higher with 2D high resolution compared with 3D whole heart [16.1 (2.0-30.6) vs. 13.4 (5.2-23.2), P = 0.004]. There was excellent agreement between myocardial ischaemic burden (intraclass correlation coefficient 0.81; P < 0.0001), with mean ratio difference between 2D high resolution vs. 3D whole heart 1.28 ± 0.67 (95% limits of agreement -0.03 to 2.59). When using a 10% threshold for a dichotomous result for presence or absence of significant ischaemia, there was moderate agreement between the methods (κ = 0.58, P < 0.0001). CONCLUSION: 2D high resolution and 3D whole heart myocardial perfusion stress CMR are comparable for detection of ischaemia. 2D high resolution gives higher values for myocardial ischaemic burden compared with 3D whole heart, suggesting that 2D high resolution is more sensitive for detection of ischaemia.

Associating mutations causing cystinuria with disease severity with the aim of providing precision medicine.

BACKGROUND: Cystinuria is an inherited disease that results in the formation of cystine stones in the kidney, which can have serious health complications. Two genes (SLC7A9 and SLC3A1) that form an amino acid transporter are known to be responsible for the disease. Variants that cause the disease disrupt amino acid transport across the cell membrane, leading to the build-up of relatively insoluble cystine, resulting in formation of stones. Assessing the effects of each mutation is critical in order to provide tailored treatment options for patients. We used various computational methods to assess the effects of cystinuria associated mutations, utilising information on protein function, evolutionary conservation and natural population variation of the two genes. We also analysed the ability of some methods to predict the phenotypes of individuals with cystinuria, based on their genotypes, and compared this to clinical data. RESULTS: Using a literature search, we collated a set of 94 SLC3A1 and 58 SLC7A9 point mutations known to be associated with cystinuria. There are differences in sequence location, evolutionary conservation, allele frequency, and predicted effect on protein function between these mutations and other genetic variants of the same genes that occur in a large population. Structural analysis considered how these mutations might lead to cystinuria. For SLC7A9, many mutations swap hydrophobic amino acids for charged amino acids or vice versa, while others affect known functional sites. For SLC3A1, functional information is currently insufficient to make confident predictions but mutations often result in the loss of hydrogen bonds and largely appear to affect protein stability. Finally, we showed that computational predictions of mutation severity were significantly correlated with the disease phenotypes of patients from a clinical study, despite different methods disagreeing for some of their predictions. CONCLUSIONS: The results of this study are promising and highlight the areas of research which must now be pursued to better understand how mutations in SLC3A1 and SLC7A9 cause cystinuria. The application of our approach to a larger data set is essential, but we have shown that computational methods could play an important role in designing more effective personalised treatment options for patients with cystinuria.