Noninvasive monitoring of serial changes in pulmonary vascular resistance and acute vasodilator testing using cardiac magnetic resonance

Objectives The study sought to evaluate the ability of cardiac magnetic resonance (CMR) to monitor acute and long-term changes in pulmonary vascular resistance (PVR) noninvasively. Background PVR monitoring during the follow-up of patients with pulmonary hypertension (PH) and the response to vasodilator testing require invasive right heart catheterization. Methods An experimental study in pigs was designed to evaluate the ability of CMR to monitor: 1) an acute increase in PVR generated by acute pulmonary embolization (n = 10); 2) serial changes in PVR in chronic PH (n = 22); and 3) changes in PVR during vasodilator testing in chronic PH (n = 10). CMR studies were performed with simultaneous hemodynamic assessment using a CMR-compatible Swan-Ganz catheter. Average flow velocity in the main pulmonary artery (PA) was quantified with phase contrast imaging. Pearson correlation and mixed model analysis were used to correlate changes in PVR with changes in CMR-quantified PA velocity. Additionally, PVR was estimated from CMR data (PA velocity and right ventricular ejection fraction) using a formula previously validated. Results Changes in PA velocity strongly and inversely correlated with acute increases in PVR induced by pulmonary embolization (r = –0.92), serial PVR fluctuations in chronic PH (r = –0.89), and acute reductions during vasodilator testing (r = –0.89, p ≤ 0.01 for all). CMR-estimated PVR showed adequate agreement with invasive PVR (mean bias –1.1 Wood units,; 95% confidence interval: –5.9 to 3.7) and changes in both indices correlated strongly (r = 0.86, p < 0.01). Conclusions CMR allows for noninvasive monitoring of acute and chronic changes in PVR in PH. This capability may be valuable in the evaluation and follow-up of patients with PH.

Nrf2 is essential for protection against acute pulmonary injury in mice

Nrf2 is a member of the “cap ‘n’ collar” family of transcription factors. These transcription factors bind to the NF-E2 binding sites (GCTGAGTCA) that are essential for the regulation of erythroid-specific genes. Nrf2 is expressed in a wide range of tissues, many of which are sites of expression for phase 2 detoxification genes. Nrf2−/− mice are viable and have a normal phenotype under normal laboratory conditions. The NF-E2 binding site is a subset of the antioxidant response elements that have the sequence GCNNNGTCA. The antioxidant response elements are regulatory sequences found on promoters of several phase 2 detoxification genes that are inducible by xenobiotics and antioxidants. We report here that Nrf2−/− mice are extremely susceptible to the administration of the antioxidant butylated hydroxytoluene. With doses of butylated hydroxytoluene that are tolerated by wild-type mice, the Nrf2−/− mice succumb from acute respiratory distress syndrome. Gene expression studies show that the expression of several detoxification enzymes is altered in the Nrf2−/− mice. The Nrf2−/− mice may prove to be a good in vivo model for toxicological studies. As oxidative damage causes DNA breakage, these mice may also be useful for testing carcinogenic agents.

Multi-component assessment of chronic obstructive pulmonary disease : an evaluation of the ADO and DOSE indices and the global obstructive lung disease categories in international primary care data sets

Funding The International Primary Care Respiratory Group (IPCRG) provided funding for this research project as an UNLOCK group study for which the funding was obtained through an unrestricted grant by Novartis AG, Basel, Switzerland. The latter funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Database access for the OPCRD was provided by the Respiratory Effectiveness Group (REG) and Research in Real Life; the OPCRD statistical analysis was funded by REG. The Bocholtz Study was funded by PICASSO for COPD, an initiative of Boehringer Ingelheim, Pfizer and the Caphri Research Institute, Maastricht University, The Netherlands.

Respiratory microbiota resistance and resilience to pulmonary exacerbation and subsequent antimicrobial intervention

This study was supported by the UK Natural Environment Research Council (NE/H019456/1) to CJvdG, by the Wellcome Trust (WT 098051) to AWW and JP for sequencing costs, and by The Anna Trust (KB2008) to KDB. AWW and The Rowett Institute of Nutrition and Health, University of Aberdeen, receive core funding support from the Scottish Government Rural and Environmental Science and Analysis Service (RESAS). We thank Paul Scott, Richard Rance and the Wellcome Trust Sanger Institute’s sequencing team for generating 16S rRNA gene sequence data.

Use of intrinsic modes in biology: Examples of indicial response of pulmonary blood pressure to ± step hypoxia

Recently, a new method to analyze biological nonstationary stochastic variables has been presented. The method is especially suitable to analyze the variation of one biological variable with respect to changes of another variable. Here, it is illustrated by the change of the pulmonary blood pressure in response to a step change of oxygen concentration in the gas that an animal breathes. The pressure signal is resolved into the sum of a set of oscillatory intrinsic mode functions, which have zero “local mean,” and a final nonoscillatory mode. With this device, we obtain a set of “mean trends,” each of which represents a “mean” in a definitive sense, and together they represent the mean trend systematically with different degrees of oscillatory content. Correspondingly, the oscillatory content of the signal about any mean trend can be represented by a set of partial sums of intrinsic mode functions. When the concept of “indicial response function” is used to describe the change of one variable in response to a step change of another variable, we now have a set of indicial response functions of the mean trends and another set of indicial response functions to describe the energy or intensity of oscillations about each mean trend. Each of these can be represented by an analytic function whose coefficients can be determined by a least-squares curve-fitting procedure. In this way, experimental results are stated sharply by analytic functions.