Open Access

Respiratory aerosol is generated in the airways among others by shear induced stripping of particles from the mucus. One approach to model the number and size distribution of stripped particles is a combination of a Eulerian Wall Film (EWF) to model the generation of particles with a Discrete Phase Model (DPM) to compute the trajectory of the stripped particles. However, the EWF model lacks two main aspects which are necessary for representing shear-induced aerosol generation. Firstly, the local thickness of the film is only modelled as a property of the surface with no impact on the flow. Thus, the formation of surface waves and their effect on the local wall shear stress is neglected. However, as we found in experimental analyses, surface waves occur in mucus mimetics exposed to shear flow, which induced wall shear stresses in an equivalent range to those occurring in the human body during breathing. In first computational analyses we represented these wave structures with generic surfaces with superimposed symmetrical, sinusoidal waves. These generic waves caused differences between the minimum and maximum wall shear stress of up to 81 % of the peak wall shear stress. Thus, because the main criterion for the occurrence of particle stripping in the EWF is the local wall shear stress, the thickness of the film and the formation of surface waves are not negligible. Secondly, the parameters, which govern particle stripping in the current implementation of the EWF, neglect the highly complex viscoelastic behavior of the mucus. Especially the limited range of the linear viscoelastic region (LVR) likely affects the occurrence of particle stripping, as strain loads exceeding the LVR disrupt internal structures in the mucus. Resulting weaker internal bonds within the fluid might decrease its resistance to particle stripping and affect the size of generated particles. Thus, constant parameters for the critical stress for particle stripping, and the mass and diameter of the particles are likely not applicable. In this work, we measure the geometry and propagation of surface waves in the mucus in simplified flow experiments using laser light techniques. Further, we measure the number and size distribution of generated particles using an aerosol spectrometer to analyze the effect of the mucus viscoelasticity. Based on the experimental study, we formulate a model, which includes both the effects of surface waves and of the mucus viscoelasticity.

In recent years, respiratory aerosol has gained much attention as a carrier of infectious diseases. While the flow mechanics of aerosol spreading and containment are mostly well researched, less is known about the flow mechanics of how the aerosol forms inside the respiratory system. In our work, we focus on the aerosol formation during coughing, which is mostly triggered by large shear air flow velocities in the larger airways. The associated large velocity differences between the wall-lining mucus film and the air trigger Kelvin-Helmholtz waves in the mucus film, from which particles detach

Introduction: Despite the promising application of extracorporeal membrane oxygenation (ECMO) in the treatment of critically ill patients, coagulation-associated technical complications, primarily clot formation and critical bleeding, remain a major challenge during ECMO therapy. The deposition of nucleated cells on the surface has been shown, yet the role of these cells towards complication development is still matter of ongoing research. In particular, the membrane lung (MemL) is prone to clot formation. Therefore, the investigation of nuclear deposits on its hollow-fibers may provide insights for a better understanding of the cellular mechanisms involved in the development of ECMO complications. Methods: To support current research, this study aimed to develop a deep learning–based tool for the automated detection and quantitative analysis of nuclear depositions on MemL hollow-fiber mats. A customized fluorescence microscopy workflow, combined with a semi-automated iterative labeling strategy, was used to generate a high-quality dataset for model training. Results: Six configurations of instance segmentation models were evaluated, with a Mask R-CNN with ResNet 101 backbone using dilated convolution providing the most balanced performance in both nuclei count and area accuracy. Compared with U-Net–based approaches such as Cellpose or StarDist, the proposed model demonstrated superior segmentation of overlapping and low-intensity nuclei, maintaining accuracy even in densely packed cellular regions. Discussion: We present an automated image analysis tool for clinically used MemLs, which exhibit complex three-dimensional hollow-fiber architectures and irregular cellular deposits that challenge conventional tools. A dedicated graphical user interface enables streamlined detection, morphometric analysis, and spatial clustering of nuclei, establishing a reproducible workflow for high-throughput analysis of fluorescence microscopy images. This approach eliminates labor-intensive manual counting and facilitates large-scale studies on cell-fiber interactions and disease-related correlations.

Contact of blood with artificial surfaces triggers platelet activation. The aim was to compare platelet kinetics after venovenous extracorporeal membrane oxygenation (V-V ECMO) start and after system exchange in different etiologies of acute lung failure. Platelet counts and coagulation parameters were analyzed from adult patients with long and exchange-free (≥8 days) ECMO runs (n = 330) caused by bacterial (n = 142), viral (n = 76), or coronavirus disease 2019 (COVID-19) (n = 112) pneumonia. A subpopulation requiring a system exchange and with long, exchange-free runs of the second oxygenator (≥7 days) (n = 110) was analyzed analogously. Patients with COVID-19 showed the highest platelet levels before ECMO implantation. Independent of the underlying disease and ECMO type, platelet counts decreased significantly within 24 hours and reached a steady state after 5 days. In the subpopulation, at the day of a system exchange, platelet counts were lower compared with ECMO start, but without differences between underlying diseases. Subsequently, platelets remained unchanged in the bacterial pneumonia group, but increased in the COVID-19 and viral pneumonia groups within 2–4 days, whereas D-dimers decreased and fibrinogen levels increased. Thus, overall platelet counts on V-V ECMO show disease-specific initial dynamics followed by an ongoing consumption by the ECMO device, which is not boosted by new artificial surfaces after a system exchange.