Tidal volumes and respiratory related impedance changes.

<p>A) Tidal volumes in liters. B) Amplitude changes due to respiration were normalized to tidal volume [AU/L]. The example shows data reconstructed using R_G. The red lines denote median values, the blue box encompasses values between the 25^th and 75^th percentile. Whiskers cover 95% of the data. Positions that are significantly different (Friedman’s test) are labeled with their appropriate number.</p

The effect of filtering on the amplitude.

<p>A) Comparing ΔZ_CA (red) and ΔZ_CR (blue) using reconstruction R_G. Note the significantly lower amplitude obtained with ΔZ_CA in upright position. B) Only in prone position a significant difference was found using GREIT reconstructions R_L and R_LH. Here, as an example the values of R_L reconstruction are shown. Black circles denote median values, the body box encompasses values between the 25^th and 75^th percentile. Whiskers cover 95% of the data. * p<0.01.</p

Cardiac related impedance changes.

<p>Left side: Boxplots of amplitude levels of cardiac related impedance changes: Image reconstruction referenced to upright position (“Global reference”). Middle: Boxplots of amplitude levels of cardiac related impedance changes: Image reconstruction referenced to the corresponding body position (“Local reference”). Right: Depiction of the corresponding reconstruction algorithm. The cylindrical uniform shape describes the back-projection used with the software provided with the GoeMF II tomograph (A) whereas thorax shaped meshes were used with GREIT reconstruction (B-D). The red lines denote median values, the blue box encompasses values between the 25^th and 75^th percentile. Whiskers cover 95% of the data. Positions that are significantly different (Friedman’s test) are labeled with their appropriate number.</p

Heart mask.

<p>A) The standard deviation over the length of the recording was calculated for each pixel. Regions of high impedance change are in red, regions of low impedance change in blue. The region with the highest impedance change corresponds with a high probability to lung tissue and was used as a reference for cross-correlation. B) Phase image depicting the obtained phase after cross-correlating all pixels with the reference region. Red corresponds to a phase shift of about 150° whereas blue denotes -150°. C) Binary heart mask obtained after thresholding the phase image at ±30° and applying morphological operators. The dashed lines illustrate the different anatomical regions: lung (black), heart (green) and great vessels (red).</p

Molecular Fractionation of Dissolved Organic Matter with Metal Salts

Coagulation of dissolved organic matter (DOM) by hydrolyzing metals is an important environmental process with particular relevance, e.g., for the cycling of organic matter in metal-rich aquatic systems or the flocculation of organic matter in wastewater treatment plants. Often, a nonremovable fraction of DOM remains in solution even at low DOM/metal ratios. Because coagulation by metals results from interactions with functional groups, we hypothesize that noncoagulating fractions have a distinct molecular composition. To test the hypothesis, we analyzed peat-derived dissolved organic matter remaining in solution after mixing with salts of Ca, Al, and Fe using 15 T Electrospray Ionization Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (ESI-FT-ICR-MS). Addition of metals resulted in a net removal of DOM. Also a reduction of molecular diversity was observed, as the number of peaks from the ESI-FT-ICR-MS spectra decreased. At DOM/metal ratios of ∼9 Ca did not show any preference for distinct molecular fractions, while Fe and Al removed preferentially the most oxidized compounds (O/C ratio >0.4) of the peat leachate. Lowering DOM/metal ratios to ∼1 resulted in further removal of less oxidized as well as more aromatic compounds (“black carbon”). Molecular composition in the residual solution after coagulation was more saturated, less polar, and less oxidized compared to the original peat leachate and exhibited a surprising similarity with DOM of marine origin. By identifying more than 9200 molecular formulas we can show that structural properties (saturation and aromaticity) and oxygen content of individual DOM molecules play an important role in coagulation with metals. We conclude that polyvalent cations not only alter the net mobility but also the very molecular composition of DOM in aquatic environments

Spatial ventilation distribution with different breathing aids.

<p>Mean (95% CI) of the percentage of ventilation directed to the right lung in right lateral position for healthy and CF. For comparison spontaneous breathing in upright position is shown. Values >0.5 indicate more ventilation of the right lung, values <0.5 more ventilation of the left lung.</p

Change in end-expiratory level (EEL) to spontaneous breathing in the corresponding body position expressed as percentage of the average tidal volume during spontaneous breathing in upright position.

<p>Results are given as mean (95% confidence interval).</p><p>All RPT devices showed significant differences in EEL compared to spontaneous breathing in both body positions in healthy (*p<0.001) but not CF.</p><p>CF cystic fibrosis; CPAP continuous positive airway pressure; PEP positive end-expiratory pressure.</p><p>Change in end-expiratory level (EEL) to spontaneous breathing in the corresponding body position expressed as percentage of the average tidal volume during spontaneous breathing in upright position.</p

Temporal ventilation distribution: Filling index of the right lung in different body positions.

<p>Results are given as mean (standard deviation). An index >1 indicating a lag (slower filling than the rest of the lung) and an index <1 a lead (faster filling than the rest of the lung). Mean difference (95% confidence interval) to spontaneous breathing is noted in square brackets^‡.</p><p>p-values provided for comparison with the upright position* and for comparison with spontaneous breathing within the respective body position^#.</p><p>n.s. not significant; CF cystic fibrosis; CPAP continuous positive airway pressure; PEP positive end-expiratory pressure.</p><p>Temporal ventilation distribution: Filling index of the right lung in different body positions.</p

The Markov model for competitive antagonism consists of 3 different receptor states, closed (C; yellow), open (O; purple) and desensitized (D; green), which are connected by the specific transition rates for each state.

<p>Because every state can bind up to 3 ligands, which are either agonists (red spheres) or antagonists (blue cones), there are 23 states in this model. Starting at C1, an additional agonist is bound rightwards and an additional antagonist upwards. Contrary to this, the unbinding of agonists and antagonists proceeds in opposite directions. k₁, k_-1, association and dissociation rates of the antagonist; a₁, a_-1, association and dissociation rates of the agonist; d₁, d_-1, transition rates of the desensitized state. Insets: structures of the antagonists used in this study (Tocris).</p

Agonist Antagonist Interactions at the Rapidly Desensitizing P2X3 Receptor

<div><p>P2X3 receptors (P2XRs), as members of the purine receptor family, are deeply involved in chronic pain sensation and therefore, specific, competitive antagonists are of great interest for perspective pain management. Heretofore, Schild plot analysis has been commonly used for studying the interaction of competitive antagonists and the corresponding receptor. Unfortunately, the steady-state between antagonist and agonist, as a precondition for this kind of analysis, cannot be reached at fast desensitizing receptors like P2X3R making Schild plot analysis inappropriate. The aim of this study was to establish a new method to analyze the interaction of antagonists with their binding sites at the rapidly desensitizing human P2X3R. The patch-clamp technique was used to investigate the structurally divergent, preferential antagonists A317491, TNP-ATP and PPADS. The P2X1,3-selective α,β-methylene ATP (α,β-meATP) was used as an agonist to induce current responses at the wild-type (wt) P2X3R and several agonist binding site mutants. Afterwards a Markov model combining sequential transitions of the receptor from the closed to the open and desensitized mode in the presence or absence of associated antagonist molecules was developed according to the measured data. The P2X3R-induced currents could be fitted correctly with the help of this Markov model allowing identification of amino acids within the binding site which are important for antagonist binding. In conclusion, Markov models are suitable to simulate agonist antagonist interactions at fast desensitizing receptors such as the P2X3R. Among the antagonists investigated, TNP-ATP and A317491 acted in a competitive manner, while PPADS was identified as a (pseudo)irreversible blocker.</p> </div