Assessment of the Geometric Interaction Between the Lotus Transcatheter Aortic Valve Prosthesis and the Native Ventricular Aortic Interface by 320-Multidetector Computed Tomography

AbstractObjectivesThis study sought to assess the geometric interaction between the Lotus Valve System transcatheter aortic prosthesis (Boston Scientific, Natick, Massachusetts) and the native aortoventricular interface using multidetector computed tomography (MDCT).BackgroundThe interaction between transcatheter aortic valve prostheses and native anatomy is variable, although potentially predictable. The Lotus transcatheter device uses a novel mechanical means of expansion, the effect of which on native anatomic geometry has not previously been described.MethodsForty patients treated with the Lotus prosthesis were enrolled. The patients underwent 320-MDCT imaging before and after implantation. Prosthesis dimensions and relevant interaction parameters, including circularity and expansion, were assessed. The degree of paraprosthetic regurgitation (PAR) and prosthesis gradient were measured by transthoracic echocardiography at the same time points.ResultsThe mean baseline annular eccentricity index (EI) was 0.21 ± 0.06 and left ventricular outflow tract EI was 0.31 ± 0.09. The deployed prostheses had high rates of circularity with a mean EI across all device segments of 0.06 ± 0.04. In noncircular device deployment, an EI >0.1 was identified in 25% of prostheses and was associated with greater native annular eccentricity at baseline compared with circular devices (0.24 ± 0.04 vs. 0.19 ± 0.06; p = 0.01). The median percent of expansion was 97.5 ± 3.8% in the inflow portion of the prosthesis. Twenty-five percent of prostheses were <90% expanded in at least 1 segment with a numerical, but not statistically significant, association between oversizing and underexpansion. No correlation was found between device underexpansion and the mean transprosthesis gradient or between noncircularity and PAR.ConclusionsThe Lotus prosthesis results in nearly full device expansion and circularization of the native basal plane. Awareness of the anatomic interaction between this unique device and the native architecture may help in the formulation of appropriate device-specific sizing algorithms

Natural variation in linalool metabolites: One genetic locus, many functions?

The ubiquitous volatile linalool is metabolized in plants to nonvolatile derivatives. We studied Nicotiana attenuata plants which naturally vary in (S)‐(+)‐linalool contents, and lines engineered to produce either (R)‐(‐)‐ or (S)‐(+)‐linalool. Only (S)‐(+)‐linalool production was associated with slower growth of a generalist herbivore, and a large fraction was present as nonvolatile derivatives. We found that variation in volatile linalool and its nonvolatile glycosides mapped to the same genetic locus which harbored the biosynthetic gene, NaLIS, but that free linalool varied more in environmental responses. This study reveals how (S)‐(+)‐linalool and conjugates differ in their regulation and possible functions in resistance

Prevalence, Detection, and Management of the Metabolic Syndrome in Patients with Acute Myocardial Infarction: Role of an Obesity-Centric Definition

Background. We sought to determine and compare the prevalence of the Metabolic Syndrome (MS) in patients with acute myocardial infarction (AMI) utilizing the new International Diabetes Federation (IDF) definition with the older National Cholesterol Education Program (NCEP) definition. We also examined the clinical utility of MS in this context. Methods. A total of 107 consecutive patients with AMI were prospectively evaluated for MS. Fasting lipids obtained at admission and fasting glucose at discharge were used. A postdischarge folder audit verified rates of discharge coding and implementation of specific management strategies for MS. Results. Baseline patient characteristics included: mean age 59 ± 13 years; males 80%; diabetes 19%; mean BMI 29.7 ± 8.4 kg/m2. MS prevalence was 54% by the IDF definition and 49% by the NCEP definition, with good agreement between definitions: κ = 0.664, P < .001. Factors predictive of MS after multivariate analysis included: hypertension, fasting glucose, waist circumference, and serum HDL (all P < .05). Despite the high prevalence, MS was recognized at discharge in only 1 patient, and referral for exercise and/or weight-loss programs was undertaken in 5 patients. Conclusion. There is a high prevalence of MS utilizing contemporary definitions in patients with AMI: 54% by the IDF definition and 49% by NCEP criteria. Despite the high prevalence, MS was under-recognized and under-treated in this population

Evaluating potential of leaf reflectance spectra to monitor plant genetic variation

Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution ("hyperspectral") spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation-information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400-2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations