Corrigendum: Ex vivo normothermic preservation of a kidney graft from uncontrolled donation after circulatory death over 73 hours.

[This corrects the article DOI: 10.3389/fbioe.2023.1330043.].

Development of a Machine-Learning Model for Anterior Knee Pain After Total Knee Arthroplasty With Patellar Preservation Using Radiological Variables.

BACKGROUND: Anterior knee pain (AKP) following total knee arthroplasty (TKA) with patellar preservation is a common complication that significantly affects patients' quality of life. This study aimed to develop a machine-learning model to predict the likelihood of developing AKP after TKA using radiological variables. METHODS: A cohort of 131 anterior stabilized TKA cases (105 patients) without patellar resurfacing was included. Patients underwent a follow-up evaluation with a minimum 1-year follow-up. The primary outcome was AKP, and radiological measurements were used as predictor variables. There were 2 observers who made the radiological measurement, which included lower limb dysmetria, joint space, and coronal, sagittal, and axial alignment. Machine-learning models were applied to predict AKP. The best-performing model was selected based on accuracy, precision, sensitivity, specificity, and Kappa statistics. Python 3.11 with Pandas and PyCaret libraries were used for analysis. RESULTS: A total of 35 TKA had AKP (26.7%). Patient-reported outcomes were significantly better in the patients who did not have AKP. The Gradient Boosting Classifier performed best for both observers, achieving an area under the curve of 0.9261 and 0.9164, respectively. The mechanical tibial slope was the most important variable for predicting AKP. The Shapley test indicated that high/low mechanical tibial slope, a shorter operated leg, a valgus coronal alignment, and excessive patellar tilt increased AKP risk. CONCLUSIONS: The results suggest that global alignment, including sagittal, coronal, and axial alignment, is relevant in predicting AKP after TKA. These findings provide valuable insights for optimizing TKA outcomes and reducing the incidence of AKP.

Ex vivo normothermic preservation of a kidney graft from uncontrolled donation after circulatory death over 73 hours.

The transplant community is focused on prolonging the ex vivo preservation time of kidney grafts to allow for long-distance kidney graft transportation, assess the viability of marginal grafts, and optimize a platform for the translation of innovative therapeutics to clinical practice, especially those focused on cell and vector delivery to organ conditioning and reprogramming. We describe the first case of feasible preservation of a kidney from a donor after uncontrolled circulatory death over a 73-h period using normothermic perfusion and analyze hemodynamic, biochemical, histological, and transcriptomic parameters for inflammation and kidney injury. The mean pressure and flow values were 71.24 ± 9.62 mmHg and 99.65 ± 18.54 mL/min, respectively. The temperature range was 36.7°C-37.2°C. The renal resistance index was 0.75 ± 0.15 mmHg/mL/min. The mean pH was 7.29 ± 0.15. The lactate concentration peak increased until 213 mg/dL at 6 h, reaching normal values after 34 h of perfusion (8.92 mg/dL). The total urine output at the end of perfusion was 1.185 mL. Histological analysis revealed no significant increase in acute tubular necrosis (ATN) severity as perfusion progressed. The expression of KIM-1, VEGF, and TGFß decreased after 6-18 h of perfusion until 60 h in which the expression of these genes increased again together with the expression of ß-catenin, Ki67, and TIMP1. We show that normothermic perfusion can maintain a kidney graft viable ex vivo for 3 days, thus allowing a rapid translation of pre-clinical therapeutics to clinical practice.