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Abstract Osteotomy can correct kyphosis, restore the spinal sequence, and restore the healthy appearance of a patient. However, the aorta is stretched during pedicle subtraction osteotomy (PSO), and some surgeons are concerned about aortic injury. We used finite element analysis to construct an aortic model to simulate hemodynamic changes during osteotomy. 16 patients with ankylosing spondylitis kyphosis who had undergone a two-level osteotomy at the L1 and L3 levels was included in this study. Aortic computed tomography angiography (CTA) was performed, and a 3D image model was constructed. The length, transverse diameter, and curvature of the aorta were used to evaluate morphological changes. Finite element analysis was used to analyze the changes in aortic fluid dynamics. Blood pressure, wall shear stress, and blood flow velocity were compared pre- and postoperatively. The overall length of the aorta before surgery was 424.3 ± 42.9 mm, and the overall length of the aorta after surgery was 436.2 ± 54.8 mm. The aortic curvature decreased from 0.27 ± 0.13 to 0.17 ± 0.09. The mean transverse diameter of the aorta did not change (19.3 ± 6.6 vs. 19.2 ± 7.4 mm, P > 0.05). The blood flow velocity (2.8 ± 1.1 vs. 1.5 ± 0.8 m/s, P

Abstract Diabetes mellitus (DM) is a well-documented risk factor of intervertebral disc degeneration (IVDD). The current study was aimed to clarify the effects and mechanisms of NADH: ubiquinone oxidoreductase subunit A3 (NDUFA3) in human nucleus pulposus cells (HNPCs) exposed to high glucose. NDUFA3 was overexpressed in HNPCs via lenti-virus transduction, which were co-treated with high glucose and rotenone (a mitochondrial complex I inhibitor) for 48 h. Cell activities were assessed for cell viability, cell apoptosis, reactive oxygen species (ROS) production, mitochondrial membrane potential (MMP) ratio, oxygen consumption rate (OCR) and mitochondrial complexes I activities. High glucose decreased cell viability, increased apoptotic cells, increased ROS production, decreased MMP levels and OCR values in HNPCs in a dose-dependent manner. Rotenone co-treatment augmented the high glucose-induced injuries on cell viability, apoptosis, ROS production and mitochondrial function. NDUFA3 overexpression counteracted the high glucose-induced injuries in HNPCs. HDAC/H3K27ac mechanism was involved in regulating NDUFA3 transcription. NDUFA3 knockdown decreased cell viability and increased apoptotic cells, which were reversed by ROS scavenger N-acetylcysteine. HDAC/H3K27ac-mediated transcription of NDUFA3 protects HNPCs against high glucose-induced injuries through suppressing cell apoptosis, eliminating ROS, improving mitochondrial function and oxidative phosphorylation. This study sheds light on candidate therapeutic targets and deepens the understanding of molecular mechanisms behind DM-induced IVDD.

Abstract Background Acute respiratory distress syndrome (ARDS) after cardiac surgery is a severe respiratory complication with high mortality and morbidity. Traditional clinical approaches may lead to under recognition of this heterogeneous syndrome, potentially resulting in diagnosis delay. This study aims to develop and external validate seven machine learning (ML) models, trained on electronic health records data, for predicting ARDS after cardiac surgery. Methods This multicenter, observational cohort study included patients who underwent cardiac surgery in the training and testing cohorts (data from Nanjing First Hospital), as well as those patients who had cardiac surgery in a validation cohort (data from Shanghai General Hospital). The number of important features was determined using the sliding windows sequential forward feature selection method (SWSFS). We developed a set of tree-based ML models, including Decision Tree, GBDT, AdaBoost, XGBoost, LightGBM, Random Forest, and Deep Forest. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and Brier score. The SHapley Additive exPlanation (SHAP) techinque was employed to interpret the ML model. Furthermore, a comparison was made between the ML models and traditional scoring systems. ARDS is defined according to the Berlin definition. Results A total of 1996 patients who had cardiac surgery were included in the study. The top five important features identified by the SWSFS were chronic obstructive pulmonary disease, preoperative albumin, central venous pressure_T4, cardiopulmonary bypass time, and left ventricular ejection fraction. Among the seven ML models, Deep Forest demonstrated the best performance, with an AUC of 0.882 and a Brier score of 0.809 in the validation cohort. Notably, the SHAP values effectively illustrated the contribution of the 13 features attributed to the model output and the individual feature's effect on model prediction. In addition, the ensemble ML models demonstrated better performance than the other six traditional scoring systems. Conclusions Our study identified 13 important features and provided multiple ML models to enhance the risk stratification for ARDS after cardiac surgery. Using these predictors and ML models might provide a basis for early diagnostic and preventive strategies in the perioperative management of ARDS patients.