Search

Graphical abstract Abstract The therapeutic efficacy of a medical product after implantation depends strongly on the host-initiated fibrotic response (foreign body reaction). For novel biomaterials, it is of high relevance to understand this fibrotic process. As an alternative to in vivo studies, in vitro models mimic parts of the whole foreign body reaction. Aim of this study was to develop a wound model with key cells and matrix proteins in coculture. This approach combined blood components such as primary macrophages in a plasma-derived fibrin hydrogel, directly exposed to reference biomaterials (PTFE, glass, titanium). The soft tissue reaction is resembled by integrating fibroblasts in a collagen or a fibrin matrix. Those two experimental setups were conducted to show whether a long-term in vitro culture of 13 days is feasible. The response to reference biomaterials was assessed by multi-parametric analyses, comprising molecular profiling (cytokines, collagen I and ß-actin) and tissue remodeling (cell adherence, histological structure, tissue deposition). Polytetrafluorethylene (PTFE) and titanium were tested as references to correlate the in vitro evaluation to previous in vivo studies. Most striking, both model setups evaluated references' fibrotic characteristics as previously reported by in vivo studies. Statement of Significance We present a test platform applied for assessments on the foreign body reaction to biomaterials. This test system consists of blood components – macrophages and plasma-derived fibrin - as well as fibroblasts and collagen, generating a three-dimensional wound microenvironment. By this modular approach, we achieved a suitable test for long-term studies and overcame the limited short-term stability of whole blood tests. In contrast to previous models, macrophages' viability is maintained during the extended culture period and excels the quality of the model. The potential to evaluate a foreign body reaction in vitro was demonstrated with defined reference materials. This model system might be of high potential as a screening platform to identify novel biomaterial candidates. [ABSTRACT FROM AUTHOR]

• CyGNSSnet is a deep learning model predicting wind speed from CyGNSS data. • Inspired by image processing, convolutional layers extract features from DDMs. • The model yields a significant improvement to the current operational algorithm. • CyGNSSnet outperforms existing methods in terms of RMSE on the test data. • The study shows great potential for deep learning in GNSS remote sensing. GNSS Reflectometry (GNSS-R) is a novel remote sensing technique for the monitoring of geophysical parameters using reflected GNSS signals from the Earth's surface. Ocean wind speed monitoring is the main objective of the recently launched Cyclone GNSS (CyGNSS), a GNSS-R constellation of eight microsatellites, launched in late 2016. In this study, the capability of deep learning, especially, for an operational wind speed data derivation from the measured Delay-Doppler Maps (DDMs) is characterized. CyGNSSnet is based on convolutional layers for the feature extraction from bistatic radar cross section (BRCS) DDMs, along with fully connected layers for processing ancillary technical and higher-level input parameters. The best architecture is determined on a validation set and is evaluated over a completely blind dataset from a different time span than that of the training data to validate the generality of the model for operational usage. After a data quality control, CyGNSSnet results in an RMSE of 1.36 m/s leading to a significant improvement by 28% in comparison to the officially operational retrieval algorithm. The RMSE is the lowest among those seen in the literature for any conventional or machine learning-based algorithm. The benefits of the convolutional layers, the advantages and weaknesses of the model are discussed. CyGNSSnet offers efficient processing of GNSS-R measurements for high-quality global ocean winds. [ABSTRACT FROM AUTHOR]