Postcardiac Injury Syndrome after Percutaneous Coronary Intervention

The post cardiac injury syndrome is characterized by the development of a fever, pleuropericarditis, and parenchymal pulmonary infiltrates in the weeks following trauma to the pericardium or myocardium. According to previous reports, almost all cases develop after major cardiac surgery or a myocardial infarction. Recently, a few reports have described post cardiac injury syndrome as a complication of endovascular procedures such as percutaneous cardiac intervention. Here we describe an unusual case of post cardiac injury syndrome after a percutaneous coronary intervention

Complete Atrioventricular Block Secondary to Bortezomib Use in Multiple Myeloma

Bortezomib is an inhibitor of 26S proteasome, which is an effective treatment for multiple myeloma. The common adverse effects of bortezomib are asthenic conditions, gastrointestinal disturbances, and peripheral neuropathy. Here we describe a patient with dyspnea and general weakness because of complete atrioventricular block while receiving bortezomib. We immediately stopped bortezomib, and after inserting a permanent VDD pacemaker, the patients' symptoms disappeared

A Case of Malignant Pericardial Mesothelioma With Constrictive Pericarditis Physiology Misdiagnosed as Pericardial Metastatic Cancer

Malignant pericardial mesothelioma is a rare and progressive cardiac tumor. There is no established standard treatment and the prognosis is poor. Most patients were retrospectively diagnosed from surgery or autopsy due to absence of specific clinical manifestation. Most patients with pericardial mesothelioma have demonstrated constrictive physiology on echocardiography or cardiac catheterization. Therefore, pericardial mesothelioma was often misdiagnosed as other causes of constrictive pericarditis. We report a case of primary pericardial mesothelioma misdiagnosed as pericardial metastasis of unknown origin

Cadmium resistance in tobacco plants expressing the MuSI gene

MuSI, a gene that corresponds to a domain that contains the rubber elongation factor (REF), is highly homologous to many stress-related proteins in plants. Since MuSI is up-regulated in the roots of plants treated with cadmium or copper, the involvement of MuSI in cadmium tolerance was investigated in this study. Escherichia coli cells overexpressing MuSI were more resistant to Cd than wild-type cells transfected with vector alone. MuSI transgenic plants were also more resistant to Cd. MuSI transgenic tobacco plants absorbed less Cd than wild-type plants. Cd translocation from roots to shoots was reduced in the transgenic plants, thereby avoiding Cd toxicity. The number of short trichomes in the leaves of wild-type tobacco plants was increased by Cd treatment, while this was unchanged in MuSI transgenic tobacco. These results suggest that MuSI transgenic tobacco plants have enhanced tolerance to Cd via reduced Cd uptake and/or increased Cd immobilization in the roots, resulting in less Cd translocation to the shoots

Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction

Abstract Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs). This study developed ML-based models (logistic regression with regularization, random forest, support vector machine, and extreme gradient boosting) and compared their performance in predicting the short- and long-term mortality of patients with AMI with those of TMs with comparable predictors. The endpoints were the in-hospital mortality of 14,183 participants and the three- and 12-month mortality in patients who survived at discharge. The performance of the ML models in predicting the mortality of patients with an ST-segment elevation myocardial infarction (STEMI) was comparable to the TMs. In contrast, the areas under the curves (AUC) of the ML models for non-STEMI (NSTEMI) in predicting the in-hospital, 3-month, and 12-month mortality were 0.889, 0.849, and 0.860, respectively, which were superior to the TMs, which had corresponding AUCs of 0.873, 0.795, and 0.808. Overall, the performance of the predictive model could be improved, particularly for long-term mortality in NSTEMI, from the ML algorithm rather than using more clinical predictors