Comparison of Sucrose and Trehalose for Protein Stabilization Using Differential Scanning Calorimetry.

The disaccharide trehalose is generally acknowledged as a superior stabilizer of proteins and other biomolecules in aqueous environments. Despite many theories aiming to explain this, the stabilization mechanism is still far from being fully understood. This study compares the stabilizing properties of trehalose with those of the structurally similar disaccharide sucrose. The stability has been evaluated for the two proteins, lysozyme and myoglobin, at both low and high temperatures by determining the glass transition temperature, Tg, and the denaturation temperature, Tden. The results show that the sucrose-containing samples exhibit higher Tden than the corresponding trehalose-containing samples, particularly at low water contents. The better stabilizing effect of sucrose at high temperatures may be explained by the fact that sucrose, to a greater extent, binds directly to the protein surface compared to trehalose. Both sugars show Tden elevation with an increasing sugar-to-protein ratio, which allows for a more complete sugar shell around the protein molecules. Finally, no synergistic effects were found by combining trehalose and sucrose. Conclusively, the exact mechanism of protein stabilization may vary with the temperature, as influenced by temperature-dependent interactions between the protein, sugar, and water. This variability can make trehalose to a superior stabilizer under some conditions and sucrose under others.

May-Thurner syndrome in patients with postural orthostatic tachycardia syndrome and Ehlers-Danlos syndrome: a case series.

Background: Postural orthostatic tachycardia syndrome (POTS), Ehlers-Danlos syndrome (EDS), and May-Thurner syndrome (MTS) are three syndromes that are often misdiagnosed or underdiagnosed. The true prevalence of these syndromes may be higher than currently reported. The following case series is the first to report a three-way association between POTS, EDS, and MTS. Case summary: We describe three patients with concomitant POTS, EDS, and MTS. Although abdominopelvic vasculature evaluation can be difficult via conventional imaging techniques, we present the use of novel dynamic contrast-enhanced magnetic resonance angiography with Differential Subsampling with Cartesian Ordering (DISCO) and four-dimensional flow magnetic resonance imaging to aid vasculature evaluation and the diagnosis of MTS. Two patients underwent left common iliac vein stenting to treat MTS, experiencing significant improvement in their POTS symptoms and quality of life. Discussion: Ehlers-Danlos syndrome, POTS, and MTS may interact synergistically to exacerbate symptoms. Patients with EDS should be evaluated for possible POTS and pelvic venous complications. Left common iliac vein stenting for MTS can mitigate POTS symptoms by decreasing lower extremity venous pooling and should be considered in this patient population. Further research is needed to understand the exact mechanism and intricacies of this syndrome triad.

Deep Learning Automated Background Phase Error Correction for Abdominopelvic 4D Flow MRI.

Background Four-dimensional (4D) flow MRI has the potential to provide hemodynamic insights for a variety of abdominopelvic vascular diseases, but its clinical utility is currently impaired by background phase error, which can be challenging to correct. Purpose To assess the feasibility of using deep learning to automatically perform image-based background phase error correction in 4D flow MRI and to compare its effectiveness relative to manual image-based correction. Materials and Methods A convenience sample of 139 abdominopelvic 4D flow MRI acquisitions performed between January 2016 and July 2020 was retrospectively collected. Manual phase error correction was performed using dedicated imaging software and served as the reference standard. After reserving 40 examinations for testing, the remaining examinations were randomly divided into training (86% [85 of 99]) and validation (14% [14 of 99]) data sets to train a multichannel three-dimensional U-Net convolutional neural network. Flow measurements were obtained for the infrarenal aorta, common iliac arteries, common iliac veins, and inferior vena cava. Statistical analyses included Pearson correlation, Bland-Altman analysis, and F tests with Bonferroni correction. Results A total of 139 patients (mean age, 47 years ± 14 [standard deviation]; 108 women) were included. Inflow-outflow correlation improved after manual correction (ρ = 0.94, P < .001) compared with that before correction (ρ = 0.50, P < .001). Automated correction showed similar results (ρ = 0.91, P < .001) and demonstrated very strong correlation with manual correction (ρ = 0.98, P < .001). Both correction methods reduced inflow-outflow variance, improving mean difference from -0.14 L/min (95% limits of agreement: -1.61, 1.32) (uncorrected) to 0.05 L/min (95% limits of agreement: -0.32, 0.42) (manually corrected) and 0.05 L/min (95% limits of agreement: -0.38, 0.49) (automatically corrected). There was no significant difference in inflow-outflow variance between manual and automated correction methods (P = .10). Conclusion Deep learning automated phase error correction reduced inflow-outflow bias and variance of volumetric flow measurements in four-dimensional flow MRI, achieving results comparable with manual image-based phase error correction. © RSNA, 2021 See also the editorial by Roldán-Alzate and Grist in this issue.