Effect of different decellularization protocols on reendothelialization with human cells for a perfused renal bioscaffold of the rat.

BACKGROUND: Scaffolds for tissue engineering can be received by whole organ decellularization while maintaining the site-specific extracellular matrix and the vascular tree. One among other decellularization techniques is the perfusion-based method using specific agents e.g. SDS for the elimination of cellular components. While SDS can disrupt the composition of the extracellular matrix and impair the adherence and growth of site-specific cells there are indications that xenogeneic cell types may benefit from protein denaturation by using higher detergent concentrations. The aim of this work is to investigate the effect of two different SDS-concentrations (i.e. 0.66% and 3%) on the ability of human endothelial cells to adhere and proliferate in an acellular rat kidney scaffold. MATERIAL AND METHODS: Acellular rat kidney scaffold was obtained by perfusion-based decellularization through the renal artery using a standardized protocol including SDS at concentrations of 0.66% or 3%. Subsequently cell seeding was performed with human immortalized endothelial cells EA.hy 926 via the renal artery. Recellularized kidneys were harvested after five days of pressure-controlled dynamic culture followed sectioning, histochemical and immunohistochemical staining as well as semiquantitative analysis. RESULTS: Efficacy of decellularization was verified by absence of cellular components as well as preservation of ultrastructure and adhesive proteins of the extracellular matrix. In semiquantitative analysis of recellularization, cell count after five days of dynamic culture more than doubled when using the gentle decellularization protocol with a concentration of SDS at 0.66% compared to 3%. Detectable cells maintained their endothelial phenotype and presented proliferative behavior while only a negligible fraction underwent apoptosis. CONCLUSION: Recellularization of acellular kidney scaffold with endothelial cells EA.hy 926 seeded through the renal artery benefits from gentle decellularization procedure. Because of that, decellularization with a SDS concentration at 0.66% should be preferred in further studies and coculture experiments.

Prediction of complications and surgery duration in primary TKA with high accuracy using machine learning with arthroplasty-specific data.

PURPOSE: The number of primary total knee arthroplasties (TKA) is expected to rise constantly. For patients and healthcare providers, the early identification of risk factors therefore becomes increasingly fundamental in the context of precision medicine. Others have already investigated the detection of risk factors by conducting literature reviews and applying conventional statistical methods. Since the prediction of events has been moderately accurate, a more comprehensive approach is needed. Machine learning (ML) algorithms have had ample success in many disciplines. However, these methods have not yet had a significant impact in orthopaedic research. The selection of a data source as well as the inclusion of relevant parameters is of utmost importance in this context. In this study, a standardized approach for ML in TKA to predict complications during surgery and an irregular surgery duration using data from two German arthroplasty-specific registries was evaluated. METHODS: The dataset is based on two initiatives of the German Society for Orthopaedics and Orthopaedic Surgery. A problem statement and initial parameters were defined. After screening, cleaning and preparation of these datasets, 864 cases of primary TKA (2016-2019) were gathered. The XGBoost algorithm was chosen and applied with a hyperparameter search, a cross validation and a loss weighting to cope with class imbalance. For final evaluation, several metrics (accuracy, sensitivity, specificity, AUC) were calculated. RESULTS: An accuracy of 92.0%, sensitivity of 34.8%, specificity of 95.8%, and AUC of 78.0% were achieved for predicting complications in primary TKA and 93.4%, 74.0%, 96.3%, and 91.6% for predicting irregular surgery duration, respectively. While traditional statistics (correlation coefficient) could not find any relevant correlation between any two parameters, the feature importance revealed several non-linear outcomes. CONCLUSION: In this study, a feasible ML model to predict outcomes of primary TKA with very promising results was built. Complex correlations between parameters were detected, which could not be recognized by conventional statistical analysis. Arthroplasty-specific data were identified as relevant by the ML model and should be included in future clinical applications. Furthermore, an interdisciplinary interpretation as well as evaluation of the results by a data scientist and an orthopaedic surgeon are of paramount importance. LEVEL OF EVIDENCE: Level IV.