Therapeutic embolization of the dural arteriovenous malformation involving the jugular bulb.

Pulsatile tinnitus is a rarely occurring symptom of vascular origin. Most frequently, the symptoms are due to an arteriovenous malformation, to a tumor of the jugular glomus or to a local arterial stenosis. A 39-yr-old Korean male suffering from pulsatile tinnitus of the left ear was diagnosed to have dural arteriovenous malformation of the jugular bulb. Magnetic resonance imaging and angiography revealed a high-velocity vascular lesion encroaching the internal jugular vein and sigmoid sinuses. Digital subtraction angiography demonstrated a dural arteriovenous malformation involving the jugular bulb. The arterial supply was from the neuromeningeal branch of the left ascending pharyngeal artery and inferior tympanic artery. Stenosis of the left jugular vein caused retrograde venous drainage through the contralateral transverse sinus. Superselective embolization of these feeding arteries was successfully performed using 25% mixture of N-butylcyanoacrylate and lipiodol. In postembolization period, his complaints of pulsatile tinnitus and buzzing noise behind his left ear disappeared

Hepatitis B and C virus prevalence in a rural area of South Korea: the role of acupuncture

A cross-sectional study evaluated the prevalence of and the risk factors for hepatitis C and B viruses among 700 adults above the age of 40 years in a rural area of South Korea. Seropositivity for hepatitis C virus antibody (11.0%, 95% confidence interval: 8.7–13.6) was higher than that for hepatitis B surface antigen (4.4%, 95% confidence interval: 3.0–6.2). Anti-hepatitis C virus seropositivity was associated with a history of repeated acupuncture (odds ratio=2.1, 95% confidence interval: 1.1–4.0), and blood transfusion (odds ratio=5.5, 95% confidence interval: 1.6–19.3) before 1992 when hepatitis C virus screening in blood donors became mandatory. Hepatitis C virus 2a was the most prevalent genotype, followed by 1b. Hepatitis C virus risk attributable to acupuncture was 38% (9% for men and 55% for women). Safer acupuncture practice has become a priority for hepatitis C virus prevention in South Korea

Risk Factors of Peritoneal Recurrence and Outcome of Resected Peritoneal Recurrence After Liver Resection in Hepatocellular Carcinoma: Review of 1222 Cases of Hepatectomy in a Tertiary Institution

10.1245/s10434-012-2260-3Annals of Surgical Oncology1972246-225

PLPD: reliable protein localization prediction from imbalanced and overlapped datasets

Subcellular localization is one of the key functional characteristics of proteins. An automatic and efficient prediction method for the protein subcellular localization is highly required owing to the need for large-scale genome analysis. From a machine learning point of view, a dataset of protein localization has several characteristics: the dataset has too many classes (there are more than 10 localizations in a cell), it is a multi-label dataset (a protein may occur in several different subcellular locations), and it is too imbalanced (the number of proteins in each localization is remarkably different). Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackles effectively these characteristics at the same time. Thus, a new computational method for protein localization is eventually needed for more reliable outcomes. To address the issue, we present a protein localization predictor based on D-SVDD (PLPD) for the prediction of protein localization, which can find the likelihood of a specific localization of a protein more easily and more correctly. Moreover, we introduce three measurements for the more precise evaluation of a protein localization predictor. As the results of various datasets which are made from the experiments of Huh et al. (2003), the proposed PLPD method represents a different approach that might play a complimentary role to the existing methods, such as Nearest Neighbor method and discriminate covariant method. Finally, after finding a good boundary for each localization using the 5184 classified proteins as training data, we predicted 138 proteins whose subcellular localizations could not be clearly observed by the experiments of Huh et al. (2003)