Procedural and Anatomical Determinants of Multielectrode Renal Denervation Efficacy: Insights From Preclinical Models

Radiofrequency renal denervation is under investigation for treatment of hypertension with variable success. We developed preclinical models to examine the dependence of ablation biomarkers on renal denervation treatment parameters and anatomic variables. One hundred twenty-nine porcine renal arteries were denervated with an irrigated radiofrequency catheter with multiple helically arrayed electrodes. Nerve effects and ablation geometries at 7 days were characterized histomorphometrically and correlated with associated renal norepinephrine levels. Norepinephrine exhibited a threshold dependence on the percentage of affected nerves across the range of treatment durations (30-60 s) and power set points (6-20 W). For 15 W/30 s treatments, norepinephrine reduction and percentage of affected nerves tracked with number of electrode treatments, confirming additive effects of helically staggered ablations. Threshold effects were only attained when ≥4 electrodes were powered. Histomorphometry and computational modeling both illustrated that radiofrequency treatments directed at large neighboring veins resulted in subaverage ablation areas and, therefore, contributed suboptimally to efficacy. Account for measured nerve distribution patterns and the annular geometry of the artery revealed that, regardless of treatment variables, total ablation area and circumferential coverage were the prime determinants of renal denervation efficacy, with increased efficacy at smaller diameters.National Institutes of Health (Grant R01-GM-49039

Inferring whole-genome histories in large population datasets

Inferring the full genealogical history of a set of DNA sequences is a core problem in evolutionary biology, because this history encodes information about the events and forces that have influenced a species. However, current methods are limited, and the most accurate techniques are able to process no more than a hundred samples. As datasets that consist of millions of genomes are now being collected, there is a need for scalable and efficient inference methods to fully utilize these resources. Here we introduce an algorithm that is able to not only infer whole-genome histories with comparable accuracy to the state-of-the-art but also process four orders of magnitude more sequences. The approach also provides an ‘evolutionary encoding’ of the data, enabling efficient calculation of relevant statistics. We apply the method to human data from the 1000 Genomes Project, Simons Genome Diversity Project and UK Biobank, showing that the inferred genealogies are rich in biological signal and efficient to process