Merging molecular catalysts and metal-organic frameworks for photocatalytic fuel production.

In the effort to generate sustainable clean energy from abundant resources such as water and carbon dioxide, solar fuel production-the combination of solar-light harvesting and the generation of efficient chemical energy carriers-by artificial molecular photosystems is very attractive. Molecular constituents that display attractive features for chemical energy conversion (such as high product selectivity and atom economy) have been developed, and their interfacing with host materials has enabled recyclability, controlled site positioning, as well as access to fundamental insights into the catalytic mechanism and environment-governed selectivity. Among the wide variety of supports, metal-organic frameworks (MOFs) possess valuable characteristics (such as their porosity and versatility) that can influence the reaction environment and material architecture in a unique fashion. Here we highlight the various existing synthetic strategies to graft molecular complexes such as catalysts and photosensitizers onto MOFs for solar fuel production. The opportunities and limitations of one-pot and stepwise approaches are critically assessed, and the resulting materials are discussed based on their photocatalytic performances and the practical applicability of selected examples.

The use of megavoltage CT (MVCT) images for dose recomputations.

Megavoltage CT (MVCT) images of patients are acquired daily on a helical tomotherapy unit (TomoTherapy, Inc., Madison, WI). While these images are used primarily for patient alignment, they can also be used to recalculate the treatment plan for the patient anatomy of the day. The use of MVCT images for dose computations requires a reliable CT number to electron density calibration curve. In this work, we tested the stability of the MVCT numbers by determining the variation of this calibration with spatial arrangement of the phantom, time and MVCT acquisition parameters. The two calibration curves that represent the largest variations were applied to six clinical MVCT images for recalculations to test for dosimetric uncertainties. Among the six cases tested, the largest difference in any of the dosimetric endpoints was 3.1% but more typically the dosimetric endpoints varied by less than 2%. Using an average CT to electron density calibration and a thorax phantom, a series of end-to-end tests were run. Using a rigid phantom, recalculated dose volume histograms (DVHs) were compared with plan DVHs. Using a deformed phantom, recalculated point dose variations were compared with measurements. The MVCT field of view is limited and the image space outside this field of view can be filled in with information from the planning kVCT. This merging technique was tested for a rigid phantom. Finally, the influence of the MVCT slice thickness on the dose recalculation was investigated. The dosimetric differences observed in all phantom tests were within the range of dosimetric uncertainties observed due to variations in the calibration curve. The use of MVCT images allows the assessment of daily dose distributions with an accuracy that is similar to that of the initial kVCT dose calculation.

Assessment of the anatomic regurgitant orifice in aortic regurgitation: a clinical magnetic resonance imaging study.

BACKGROUND: The aim of our study was to determine whether planimetry of the anatomic regurgitant orifice (ARO) in patients with aortic regurgitation (AR) by magnetic resonance imaging (MRI) is feasible and whether ARO by MRI correlates with the severity of AR. METHODS AND RESULTS: Planimetry of ARO by MRI was performed on a clinical magnetic resonance system (1.5 T Sonata, Siemens Medical Solutions) in 45 patients and correlated with the regurgitant fraction (RgF) and regurgitant volume (RgV) determined by MRI phase velocity mapping (PVM; MRI-RgF, MRI-RgV, n = 45) and with invasively quantified AR by supravalvular aortography (n = 32) and RgF upon cardiac catheterisation (CATH-RgF, n = 15). Determination of ARO was possible in 98% (44/45) of the patients with adequate image quality. MRI-RgF and CATH-RgF were modestly correlated (n = 15, r = 0.71, p<0.01). ARO was closely correlated with MRI-RgF (n = 44, r = 0.88, p<0.001) and was modestly correlated with CATH-RgF (n = 14, r = 0.66, p = 0.01). Sensitivity and specificity of ARO to detect moderately severe and severe aortic regurgitation (defined as MRI-RgF > or =40%) were 96% and 95% at a threshold of 0.28 cm2 (AUC = 0.99). Of note, sensitivity and specificity of ARO to detect moderately severe and severe AR at catheterisation (defined as CATH-RgF > or =40% or supravalvular aortography > or =3+) were 90% and 91% at a similar threshold of 0.28 cm2 (AUC = 0.95). Lastly, sensitivity and specificity of ARO to detect severe aortic regurgitation (defined as MRI-RgF > or =50% and/or regurgitant volume > or =60 ml) were 83% and 97% at a threshold of 0.48 cm2 (AUC = 0.97). CONCLUSIONS: Visualisation and planimetry of the ARO in patients with AR are feasible by MRI. There is a strong correlation of ARO with RgV and RgF assessed by PVM and with invasively graded AR at catheterisation. Therefore, determination of ARO by MRI is a new non-invasive measure for assessing the severity of AR.

Quantification of aortic valve area and left ventricular muscle mass in healthy subjects and patients with symptomatic aortic valve stenosis by MRI.

MRI allows visualization and planimetry of the aortic valve orifice and accurate determination of left ventricular muscle mass, which are important parameters in aortic stenosis. In contrast to invasive methods, MRI planimetry of the aortic valve area (AVA) is flow independent. AVA is usually indexed to body surface area. Left ventricular muscle mass is dependent on weight and height in healthy individuals. We studied AVA, left ventricular muscle mass (LMM) and ejection fraction (EF) in 100 healthy individuals and in patients with symptomatic aortic valve stenosis (AS). All were examined by MRI (1.5 Tesla Siemens Sonate) and the AVA was visualized in segmented 2D flash sequences and planimetry of the performed AVA was manually. The aortic valve area in healthy individuals was 3.9+/-0.7 cm(2), and the LMM was 99+/-27 g. In a correlation analysis, the strongest correlation of AVA was to height (r=0.75, p<0.001) and for LMM to weight (r=0.64, p<0.001). In a multiple regression analysis, the expected AVA for healthy subjects can be predicted using body height: AVA=-2.64+0.04 x(height in cm) -0.47 x w (w=0 for man, w=1 for female).In patients with aortic valve stenosis, AVA was 1.0+/-0.35 cm(2), in correlation to cath lab r=0.72, and LMM was 172+/-56 g. We compared the AS patients results with the data of the healthy subjects, where the reduction of the AVA was 28+/-10% of the expected normal value, while LMM was 42% higher in patients with AS. There was no correlation to height, weight or BSA in patients with AS. With cardiac MRI, planimetry of AVA for normal subjects and patients with AS offered a simple, fast and non-invasive method to quantify AVA. In addition LMM and EF could be determined. The strong correlation between height and AVA documented in normal subjects offered the opportunity to integrate this relation between expected valve area and definitive orifice in determining the disease of the aortic valve for the individual patient. With diagnostic MRI in patients with AS, invasive measurements of the systolic transvalvular gradient does not seem to be necessary.