Aim:Numerous studies have shown an increase in NT-pro BNP, troponin and D-dimer levels with right ventricular dysfunction on echocardiography in patients with acute pulmonary thromboembolism (PTE). We found no data about the relation between tenascin-C and acute PTE in the literature. The aim of this study was to evaluate tenascin-C levels in acute PTE and correlate them with NT-pro BNP, troponin and D-dimer. Method: Thirty-four patients who have massive or submassive PTE on spiral thorax CT (PTE group) and twenty healthy volunteers (non-PTE group) were evaluated. In all patients, right ventricular functions were obtained on transthoracic echocardiography and plasma tenascin-C, NT-pro BNP, troponin , and D-dimer levels were measured. Results: The left ventricular systolic diameter, left ventricular diastolic diameter and left ventricular ejection fraction were similar in the two groups. The right heart chamber sizes and main pulmonary artery diameter were significantly larger in the PTE group and systolic pulmonary artery pressures were also significantly higher in this group. Tenascin-C, NT-pro BNP, and D-dimer levels were also significantly higher in the PTE group than in the non-PTE group ( p 0.001). The troponin I levels did not differ between the two groups ( p 0.4). Tenascin-C was found to be highly correlated with sPAP and NT-pro BNP and correlated with D-dimer; however, troponin I was not correlated with tenascin-C. Conclusion: This study demonstrates that tenascin-C may be an indicator of acute PTE. J Atheroscler Thromb, 2011; 18:487-493.
Elbow fractures are one of the most common fracture types. Diagnoses on elbow fractures often need the help of radiographic imaging to be read and analyzed by a specialized radiologist with years of training. Thanks to the recent advances of deep learning, a model that can classify and detect different types of bone fractures needs only hours of training and has shown promising results. However, most existing deep learning models are purely data-driven, lacking incorporation of known domain knowledge from human experts. In this work, we propose a novel deep learning method to diagnose elbow fracture from elbow X-ray images by integrating domain-specific medical knowledge into a curriculum learning framework. In our method, the training data are permutated by sampling without replacement at the beginning of each training epoch. The sampling probability of each training sample is guided by a scoring criterion constructed based on clinically known knowledge from human experts, where the scoring indicates the diagnosis difficultness of different elbow fracture subtypes. We also propose an algorithm that updates the sampling probabilities at each epoch, which is applicable to other sampling-based curriculum learning frameworks. We design an experiment with 1865 elbow X-ray images for a fracture/normal binary classification task and compare our proposed method to a baseline method and a previous method using multiple metrics. Our results show that the proposed method achieves the highest classification performance. Also, our proposed probability update algorithm boosts the performance of the previous method.
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