CT-proET-1-, MR-proADM, and the calculated ratio in surviving as compared to nonsurviving patients

Data from all patients on admission are shown. Squares denote median values, boxes represent 25th to 75th percentiles and whiskers indicate the range.<p><b>Copyright information:</b></p><p>Taken from "Circulating Precursor Levels of Endothelin-1 and Adrenomedullin, Two Endothelium-Derived, Counteracting Substances, in Sepsis"</p><p></p><p>Endothelium 2007;14(6):345-351.</p><p>Published online 13 Dec 2007</p><p>PMCID:PMC2430170.</p><p></p

Receiver operating curve (ROC) analysis of CT-proET1, MRproADM, the CT-proET1/MR-proADM ratio and the APACHE II score with respect to outcome prediction of critically ill patients

Receiver operating characteristic (ROC) plots are graphical plots illustrating the sensitivity (-axis) and the specificity ( -axis) for all cut-off points of a diagnostic or prognostic test. The overall performance and a summary measure of the diagnostic accuracy of a test can be expressed as the area under the ROC curve (AUC). Note that an AUCof 0.50 means that the diagnostic accuracy in question is equivalent to that which would be obtained by flipping a coin (i.e., random chance). () Data of all patients ( = 95) with SIRS and sepsis on admission to the ICU. Sensitivity was calculated with nonsurvivors ( = 21), specificity with survivors ( = 74) during their hospital stay. () Data of patients with sepsis (including septic shock) in need for blood pressure support with vasoactiva ( = 30) on admission to the ICU. Sensitivity was calculated with nonsurvivors ( = 9), specificity with survivors ( = 21) during their hospital stay.<p><b>Copyright information:</b></p><p>Taken from "Circulating Precursor Levels of Endothelin-1 and Adrenomedullin, Two Endothelium-Derived, Counteracting Substances, in Sepsis"</p><p></p><p>Endothelium 2007;14(6):345-351.</p><p>Published online 13 Dec 2007</p><p>PMCID:PMC2430170.</p><p></p

CT-proET-1 and MR-proADM values in all patients according to the severity of disease

Patients' data on admission to the ICU were grouped according to the severity of the disease following consensus criteria in groups with “SIRS, but no sepsis,” “sepsis,” and “septic shock.” Squares denote median values and whiskers indicate 25th and 75th percentiles.<p><b>Copyright information:</b></p><p>Taken from "Circulating Precursor Levels of Endothelin-1 and Adrenomedullin, Two Endothelium-Derived, Counteracting Substances, in Sepsis"</p><p></p><p>Endothelium 2007;14(6):345-351.</p><p>Published online 13 Dec 2007</p><p>PMCID:PMC2430170.</p><p></p

Free Energy Surfaces from Single-Distance Information

We propose a network-based method for determining basins and barriers of complex free energy surfaces (e.g., the protein folding landscape) from the time series of a single intramolecular distance. First, a network of transitions is constructed by clustering the points of the time series according to the short-time distribution of the signal. The transition network, which reflects the short-time kinetics, is then used for the iterative determination of individual basins by the minimum-cut-based free energy profile, a barrier-preserving one-dimensional projection of the free energy surface. The method is tested using the time series of a single Cβ−Cβ distance extracted from equilibrium molecular dynamics (MD) simulations of a structured peptide (20 residue three-stranded antiparallel β-sheet). Although the information of only one distance is employed to describe a system with 645 degrees of freedom, both the native state and the unfolding barrier of about 10 kJ/mol are determined with remarkable accuracy. Moreover, non-native conformers are identified by comparing long-time distributions of the same distance. To examine the applicability to single-molecule Förster resonance energy transfer (FRET) experiments, a time series of donor and acceptor photons is generated using the MD trajectory. The native state of the β-sheet peptide is determined accurately from the emulated FRET signal. Applied to real single-molecule FRET measurements on a monomeric variant of λ-repressor, the network-based method correctly identifies the folded and unfolded populations, which are clearly separated in the minimum-cut-based free energy profile

Amyloid Fibril Polymorphism Is under Kinetic Control

Self-assembly of proteins into amyloid aggregates displays a broad diversity of morphologies, both at the protofibrillar and final fibrillar species. These polymorphic species can coexist at fixed experimental conditions, and their relative abundance can be controlled by changing the solvent composition, or stirring the solution. However, the extent to which external conditions regulate the equilibrium of morphologically distinct species is still unknown. Here we investigate the nucleation of distinct fibril morphologies using computer simulations of a simplified model of an amyloid polypeptide. Counterintuitively, the energetically less favorable fibril morphologies nucleate more frequently than the morphologies of higher stability for models with low aggregation propensity. The free-energy profiles of the aggregation process indicate that the nucleation barrier determines the population fractions of different fibril morphologies, i.e., amyloid polymorphism is under kinetic control

Frequencies of primary and secondary endpoints according to eGFR groups and associations of eGFR stratified by groups with adverse clinical outcome in univariate and multivariate regression analyses.

Frequencies of primary and secondary endpoints according to eGFR groups and associations of eGFR stratified by groups with adverse clinical outcome in univariate and multivariate regression analyses.</p

Discriminative performance of eGFR and serum creatinine for the prediction of the different outcomes.

Discriminative performance of eGFR and serum creatinine for the prediction of the different outcomes.</p

Hazard ratio of 30-day mortality as a function of eGFR.

(x-axis, eGFR (ml/min/1.73m2), y-axis, odd ratio for 30-day mortality, CI, confidence interval).</p

Univariate and multivariate logistic regression analyses according to continuous eGFR values.

Univariate and multivariate logistic regression analyses according to continuous eGFR values.</p

Baseline characteristics of the total cohort and stratified by eGFR categories.

Baseline characteristics of the total cohort and stratified by eGFR categories.</p