No abstract

Nocturnal gastro-oesophageal reflux may be important in the pathogenesis of reflux oesophagitis. This study aimed to determine whether: (1) gastro-oesophageal reflux occurs during sleep in patients with reflux oesophagitis and, if so, to explore the mechanism, and (2)

We performed the present studies to determine whether a proximal renal tubular dopamine D1-like receptor defect exists in human essential hypertension. Twenty-four subjects were studied (13 normotensive and 11 hypertensive) in a randomized, double-blind, vehicle-controlled study using fenoldopam, a selective D1-like receptor agonist. Subjects were studied in sodium metabolic balance at 300 mEq/d, after which the salt sensitivity of their blood pressure was determined. Fenoldopam at peak doses of 0.1 to 0.2 microgram/kg per minute decreased mean arterial pressure in hypertensive subjects but did not change mean pressure in normotensive subjects. Fenoldopam increased renal plasma flow to a greater extent in hypertensive than normotensive subjects. Fenoldopam increased both urinary and fractional sodium excretions in the hypertensive and normotensive groups. In normotensive but not hypertensive subjects, fenoldopam increased the fractional excretion of lithium and distal sodium delivery. In contrast, both distal fractional sodium reabsorption and sodium-potassium exchange fell significantly in hypertensive subjects. We conclude that human essential hypertension is associated with a reduction in the proximal tubular response to D1-like receptor stimulation compared with normotensive subjects. Hypertensive subjects appear to have a compensatory upregulation of renal vascular and distal tubular D1-like receptor function that offsets the proximal tubular defect, resulting in an enhanced natriuretic response to D1-like receptor stimulation.

Hormone signaling is often pulsatile, and multi-parameter deconvolution procedures have long been utilized to identify and characterize secretory events. However, the existing programs have serious limitations, including the subjective nature of initial peak selection, lack of statistical verification of presumed bursts, and user-unfriendliness of the application. Here, we describe a novel deconvolution program, AutoDecon, which addresses these concerns. We validate AutoDecon for application to serum luteinizing hormone (LH) concentration time series using synthetic data mimicking real data from normal women and then comparing the performance of AutoDecon to the performance of the widely-employed hormone pulsatility analysis program Cluster. The sensitivity of AutoDecon is higher than Cluster: ~96% vs. ~80% (p = 0.001). However, Cluster had a lower false-positive detection rate than AutoDecon: 6% vs 1%, p = 0.001. Further analysis demonstrated that the pulsatility parameters recovered by AutoDecon were indistinguishable from those characterizing the synthetic data and sampling at 5-or 10-minute intervals was optimal for maximizing the sensitivity rates for LH. Accordingly, AutoDecon presents a viable non-subjective alternative to previous pulse detection algorithms for the analysis of LH data. It is applicable to other pulsatile hormone-concentration time series and many other pulsatile phenomena. The software is free and downloadable at