Oxaliplatin-induced acquired long QT syndrome with torsades de pointes and myocardial injury in a patient with dilated cardiomyopathy and rectal cancer

AbstractA 67-year-old woman presented with a history of dilated cardiomyopathy with congestive heart failure since 2003, who subsequently developed lower rectal cancer (adenocarcinoma) with liver, bone, and lymph node metastasis. Abdominoperineal resection and hepatectomy were performed. The patient received two rounds of intravenous chemotherapy, including 12 and six courses of FOLFOX4 (5-fluorouracil, leucovorin, and oxaliplatin; 85 mg/m2 per cycle). She underwent a third round of intravenous FOLFOX4 because of tumor progression. During the 21st course of FOLFOX4 regimen, the patient developed ST segment depression in lead II and prolongation of QT interval with polymorphic ventricular tachycardia, torsades de pointes right after the start of oxaliplatin infusion. Immediate defibrillation and cardiopulmonary resuscitation were administered, and the patient regained spontaneous circulation and consciousness. Twelve-lead electrocardiogram showed ST segment elevation in III, aVF, and ST segment depression in V4–6 after resuscitation. To our knowledge, prolongation of QT interval with torsades de pointes and coronary spasm with myocardial injury that were stabilized in one patient following oxaliplatin infusion has not been reported. We present a patient with these rare complications

Learning Structural Kernels for Natural Language Processing

Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods