Comparison of Dixon Sequences for Estimation of Percent Breast Fibroglandular Tissue

Objectives

To evaluate sources of error in the Magnetic Resonance Imaging (MRI) measurement of percent fibroglandular tissue (%FGT) using two-point Dixon sequences for fat-water separation.

Methods

Ten female volunteers (median age: 31 yrs, range: 23–50 yrs) gave informed consent following Research Ethics Committee approval. Each volunteer was scanned twice following repositioning to enable an estimation of measurement repeatability from high-resolution gradient-echo (GRE) proton-density (PD)-weighted Dixon sequences. Differences in measures of %FGT attributable to resolution, T₁ weighting and sequence type were assessed by comparison of this Dixon sequence with low-resolution GRE PD-weighted Dixon data, and against gradient-echo (GRE) or spin-echo (SE) based T₁-weighted Dixon datasets, respectively.

Results

%FGT measurement from high-resolution PD-weighted Dixon sequences had a coefficient of repeatability of ±4.3%. There was no significant difference in %FGT between high-resolution and low-resolution PD-weighted data. Values of %FGT from GRE and SE T₁-weighted data were strongly correlated with that derived from PD-weighted data (r = 0.995 and 0.96, respectively). However, both sequences exhibited higher mean %FGT by 2.9% (p < 0.0001) and 12.6% (p < 0.0001), respectively, in comparison with PD-weighted data; the increase in %FGT from the SE T₁-weighted sequence was significantly larger at lower breast densities.

Conclusion

Although measurement of %FGT at low resolution is feasible, T₁ weighting and sequence type impact on the accuracy of Dixon-based %FGT measurements; Dixon MRI protocols for %FGT measurement should be carefully considered, particularly for longitudinal or multi-centre studies.     

Inhibition of antigen and calcium ionophore induced secretion from RBL-2H3 cells by phosphatase inhibitors

The role of serine/threonine protein phosphatases PP1 and PP2A in mast cell secretion was investigated using the phosphatase inhibitors okadaic acid and calyculin A. Calyculin A (5-25 nm) inhibited antigen-induced secretion from a rat mucosal mast cell line (RBL-2H3) when added in conjunction with the activator. Okadaic acid (250-1000 nm) inhibited secretion only when added before activation and did so in a time- and concentration-dependent manner. Both inhibitors caused the cells to become rounder, but only calyculin A induced membrane blebbing and a loss of adherence. Okadaic acid also inhibited secretion induced by the calcium ionophore A23187, in the presence or absence of PMA, indicating that the phosphatase inhibitors act on a component of the secretory pathway downstream of calcium mobilization. Okadaic acid increased the phosphorylation of a number of proteins, as did an analogue methyl okadaate, which also inhibited secretion, but less effectively. Okadaic acid induced the phosphorylation of triton-insoluble proteins of 55, 18 and 16 kDa. The 55 kDa protein was identified as vimentin and okadaic acid induced its partial translocation to the triton-soluble fraction. Our data indicate that full secretory function in mucosal mast cells requires phosphatase activity.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.