Search

Coronary artery thrombosis is the major risk associated with Kawasaki disease (KD). Long-term management of KD patients with persistent aneurysms requires a thrombotic risk assessment and clinical decisions regarding the administration of anticoagulation therapy. Computational fluid dynamics has demonstrated that abnormal KD coronary artery hemodynamics can be associated with thrombosis. However, the underlying mechanisms of clot formation are not yet fully understood. Here we present a new model incorporating data from patient-specific simulated velocity fields to track platelet activation and accumulation. We use a system of Reaction-Advection-Diffusion equations solved with a stabilized finite element method to describe the evolution of non-activated platelets and activated platelet concentrations [AP], local concentrations of adenosine diphosphate (ADP) and poly-phosphate (PolyP). The activation of platelets is modeled as a function of shear-rate exposure and local concentration of agonists. We compared the distribution of activated platelets in a healthy coronary case and six cases with coronary artery aneurysms caused by KD, including three with confirmed thrombosis. Results show spatial correlation between regions of higher concentration of activated platelets and the reported location of the clot, suggesting predictive capabilities of this model towards identifying regions at high risk for thrombosis. Also, the concentration levels of ADP and PolyP in cases with confirmed thrombosis are higher than the reported critical values associated with platelet aggregation (ADP) and activation of the intrinsic coagulation pathway (PolyP). These findings suggest the potential initiation of a coagulation pathway even in the absence of an extrinsic factor. Finally, computational simulations show that in regions of flow stagnation, biochemical activation, as a result of local agonist concentration, is dominant. Identifying the leading factors to a pro-coagulant environment in each case-mechanical or biochemical-could help define improved strategies for thrombosis prevention tailored for each patient.

Coronary artery thrombosis is the major risk associated with Kawasaki disease (KD). Long-term management of KD patients with persistent aneurysms requires a thrombotic risk assessment and clinical decisions regarding the administration of anticoagulation therapy. Computational fluid dynamics has demonstrated that abnormal KD coronary artery hemodynamics can be associated with thrombosis. However, the underlying mechanisms of clot formation are not yet fully understood. Here we present a new model incorporating data from patient-specific simulated velocity fields to track platelet activation and accumulation. We use a system of Reaction-Advection-Diffusion equations solved with a stabilized finite element method to describe the evolution of non-activated platelets and activated platelet concentrations [AP], local concentrations of adenosine diphosphate (ADP) and poly-phosphate (PolyP). The activation of platelets is modeled as a function of shear-rate exposure and local concentration of agonists. We compared the distribution of activated platelets in a healthy coronary case and six cases with coronary artery aneurysms caused by KD, including three with confirmed thrombosis. Results show spatial correlation between regions of higher concentration of activated platelets and the reported location of the clot, suggesting predictive capabilities of this model towards identifying regions at high risk for thrombosis. Also, the concentration levels of ADP and PolyP in cases with confirmed thrombosis are higher than the reported critical values associated with platelet aggregation (ADP) and activation of the intrinsic coagulation pathway (PolyP). These findings suggest the potential initiation of a coagulation pathway even in the absence of an extrinsic factor. Finally, computational simulations show that in regions of flow stagnation, biochemical activation, as a result of local agonist concentration, is dominant. Identifying the leading factors to a pro-coagulant environment in each case-mechanical or biochemical-could help define improved strategies for thrombosis prevention tailored for each patient.

Background Coronary artery aneurysms (CAAs) may occur after Kawasaki disease (KD) and lead to important morbidity and mortality. As CAA in patients with KD are rare and heterogeneous lesions, prognostication and risk stratification are difficult. We sought to derive the cumulative risk and associated factors for cardiovascular complications in patients with CAAs after KD. Methods and Results A 34-institution international registry of 1651 patients with KD who had CAAs (maximum CAA Z score ≥2.5) was used. Time-to-event analyses were performed using the Kaplan-Meier method and Cox proportional hazard models for risk factor analysis. In patients with CAA Z scores ≥10, the cumulative incidence of luminal narrowing (>50% of lumen diameter), coronary artery thrombosis, and composite major adverse cardiovascular complications at 10 years was 20±3%, 18±2%, and 14±2%, respectively. No complications were observed in patients with a CAA Z score

BackgroundThrombosis is a major adverse outcome associated with coronary artery aneurysms (CAAs) resulting from Kawasaki disease (KD). Clinical guidelines recommend initiation of anticoagulation therapy with maximum CAA diameter (Dmax) ≥8 mm or Z-score ≥ 10. Here, we investigate the role of aneurysm hemodynamics as a superior method for thrombotic risk stratification in KD patients.Methods and resultsWe retrospectively studied ten KD patients with CAAs, including five patients who developed thrombosis. We constructed patient-specific anatomic models from cardiac magnetic resonance images and performed computational hemodynamic simulations using SimVascular. Our simulations incorporated pulsatile flow, deformable arterial walls and boundary conditions automatically tuned to match patient-specific arterial pressure and cardiac output. From simulation results, we derived local hemodynamic variables including time-averaged wall shear stress (TAWSS), low wall shear stress exposure, and oscillatory shear index (OSI). Local TAWSS was significantly lower in CAAs that developed thrombosis (1.2 ± 0.94 vs. 7.28 ± 9.77 dynes/cm2, p = 0.006) and the fraction of CAA surface area exposed to low wall shear stress was larger (0.69 ± 0.17 vs. 0.25 ± 0.26%, p = 0.005). Similarly, longer residence times were obtained in branches where thrombosis was confirmed (9.07 ± 6.26 vs. 2.05 ± 2.91 cycles, p = 0.004). No significant differences were found for OSI or anatomical measurements such us Dmax and Z-score. Assessment of thrombotic risk according to hemodynamic variables had higher sensitivity and specificity compared to standard clinical metrics (Dmax, Z-score).ConclusionsHemodynamic variables can be obtained non-invasively via simulation and may provide improved thrombotic risk stratification compared to current diameter-based metrics, facilitating long-term clinical management of KD patients with persistent CAAs.

Toxicity associated with elevated levels of cobalt-chromium-molybdenum (CoCrMo) nanoparticles in total hip replacement (THR) patients has been a rising concern. Recent investigations demonstrated that these particles can induce polyneuropathy in THR patients. The current study aims to address a detailed molecular investigation of CoCrMo nanoparticle-mediated mitochondrial dynamics using induced pluripotent stem cell-derived neurons (iPSC neurons). Telencephalic neurons from iPSCs were used in this study. A statistically significant dose-dependent reduction in membrane potential and mitochondrial superoxide generation was observed after CoCrMo nanoparticle treatment. The gene expression analysis confirmed that the oxidative-specific genes were significantly upregulated in particle-treated cells compared to untreated cells. When iPSCs were exposed to CoCrMo nanoparticles, there was a significant reduction in the area, perimeter, and length of mitochondria. Live cell imaging (mitochondrial tracking) revealed a significant reduction in mitochondrial movements in the presence of CoCrMo nanoparticles. Further protein expression confirmed increased mitochondrial fission in CoCrMo particle-treated cells by significantly upregulating Drp-1 protein and downregulating Mfn-2. In conclusion, the results show that CoCrMo nanoparticles can significantly alter neuronal mitochondrial dynamics. The disturbance in balance restricts mitochondrial movement, reduces energy production, increases oxidative stress, and can cause subsequent neurodegeneration. [ABSTRACT FROM AUTHOR]

Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery, utilizing preoperative X-ray images. The automated pipeline comprised two primary stages. First, the pretrained BiLuNet deep learning model was employed to extract essential features from X-ray images. Subsequently, five machine learning algorithms were trained using a five-fold cross-validation technique on a dataset of 311 patients to identify the optimal models to predict interbody cage height and postoperative PI-LL. LASSO regression and support vector regression demonstrated superior performance in predicting interbody cage height and postoperative PI-LL, respectively. For cage height prediction, the root mean square error (RMSE) was calculated as 1.01, and the model achieved the highest accuracy at a height of 12 mm, with exact prediction achieved in 54.43% (43/79) of cases. In most of the remaining cases, the prediction error of the model was within 1 mm. Additionally, the model demonstrated satisfactory performance in predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, our results indicate that machine learning models can reliably predict interbody cage height and postoperative PI-LL.