Browsing by Author "Brenner, Thorsten"

The adsorption of differently charged nanoparticles at liquid-solid interfaces was investigated by in situ X-ray reflectivity measurements. The layer formation of positively charged maghemite (γ-Fe(2)O(3)) nanoparticles at the aqueous solution-SiO(2) interface was observed while negatively charged gold nanoparticles show no adsorption at this interface. Thus, the electrostatic interaction between the particles and the charged surface was determined as the driving force for the adsorption process. The data analysis shows that a logarithmic particle size distribution describes the density profile of the thin adsorbed maghemite layer. The size distribution in the nanoparticle solution determined by small angle X-ray scattering shows an average particle size which is similar to that found for the adsorbed film. The formed magehemite film exhibits a rather high stability.

Abstract Background Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.

The formation of a layer of hydrophobic magnetite (Fe(3)O(4)) nanoparticles stabilized by lauric acid is analyzed by in situ X-ray reflectivity measurements. The data analysis shows that the nanoparticles partially disperse their hydrophobic coating. Consequently, a Langmuir layer was formed by lauric acid molecules that can be compressed into an untilted condensed phase. A majority of the nanoparticles are attached to the Langmuir film integrating lauric acid residue on their surface into the Langmuir film. Hence, the particles at the liquid-gas interface can be identified as so-called Janus beads, which are amphiphilic solids having two sides with different functionality.

The adsorption process of proteins to surfaces is governed by the mutual interactions among proteins, the solution, and the substrate. Interactions arising from the substrate are usually attributed to the uppermost atomic layer. This actual surface defines the surface chemistry and hence steric and electrostatic interactions. For a comprehensive understanding, however, the interactions arising from the bulk material also have to be considered. Our protein adsorption experiments with globular proteins (α-amylase, bovine serum albumin, and lysozyme) clearly reveal the influence of the subsurface material via van der Waals forces. Here, a set of functionalized silicon wafers enables a distinction between the effects of surface chemistry and the subsurface composition of the substrate. Whereas the surface chemistry controls whether the individual proteins are denatured, the strength of the van der Waals forces affects the final layer density and hence the adsorbed amount of proteins. The results imply that van der Waals forces mainly influence surface processes, which govern the structure formation of the protein adsorbates, such as surface diffusion and spreading.