Pheochromocytoma Diagnosis After an Abnormal Stress Test: Case Report and Review of the Literature

Silent Hypertensive Crisis in an Adolescent: First Case Report of Pediatric Pheochromocytoma from Indonesia

Secondary hypertension in children, to the rare extent, can be caused by endocrine factors such as pheochromocytoma, an adrenal tumor that secretes catecholamine. Only a few cases have been reported in the past 3 decades. To the best of our knowledge, this is the first case report of pediatric pheochromocytoma from Indonesia. We reviewed a case of a 16-year-old Indonesian boy with history of silent hypertensive crisis who was referred from a remote area in an island to the pediatric nephrology clinic at Cipto Mangunkusumo Hospital, Jakarta, Indonesia. Despite medications, his symptoms persisted for 14 months. At the pediatric nephrology clinic, pheochromocytoma was suspected due to symptoms of catecholamine secretion presented, which was palpitation, diaphoresis, and weight loss. However, as the urine catecholamine test was unavailable in Indonesia, the urine sample was sent to a laboratory outside the country. The elevated level of urine metanephrine, focal pathological uptake in the right adrenal mass seen on ¹³¹I-MIBG, and histopathology examination confirmed the suspicion of pheochromocytoma. Following the tumor resection, he has been living with normal blood pressure without antihypertensive medications. This case highlights that pheochromocytoma should always be included in the differential diagnoses of any atypical presentation of hypertension. In limited resources setting, high clinical awareness of pheochromocytoma is required to facilitate prompt referral. Suspicion of pheochromocytoma should be followed by measurement of urine metanephrine levels. Early diagnosis of pheochromocytoma would fasten the optimal cure, alleviate the symptoms of catecholamine release, and reverse hypertension.

DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels

BACKGROUND

A drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs.

RESULT

In this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation.

CONLUSION

In 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.