Glucosinolates Extracts from Brassica juncea Ameliorate HFD-Induced Non-Alcoholic Steatohepatitis.

Non-alcoholic fatty liver disease (NAFLD) is mainly characterized by excessive fat accumulation in the liver. It spans a spectrum of diseases from hepatic steatosis to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Brassica juncea is rich in glucosinolates and has been proven to possess many potential pharmacological properties, including hypoglycemic, anti-oxidation, anti-inflammatory, and anti-carcinogenic activities. This study aims to investigate whether whole-plant Brassica juncea (WBJ) and its glucosinolates extracts (BGE) have hepatoprotective effects against a high-fat diet (HFD)-induced NAFLD and further explore the mechanism underlying this process in vivo and in vitro. WBJ treatment significantly reduced body fat, dyslipidemia, hepatic steatosis, liver injury, and inflammation; WBJ treatment also reversed the antioxidant enzyme activity to attenuate oxidative stress in HFD-fed rat liver. Moreover, WBJ and BGE enhanced the activation of AMPK to reduce SREBPs, fatty acid synthase, and HMG-CoA reductase but increased the expression of CPT-I and PPARα to improve hepatic steatosis. In addition, WBJ and BGE could ameliorate NAFLD by inhibiting TNF-α and NF-κB. Based on the above results, this study demonstrates that WBJ and BGE ameliorate HFD-induced hepatic steatosis and liver injury. Therefore, these treatments could represent an unprecedented hope toward improved strategies for NAFLD.

Multilabel Deep Visual-Semantic Embedding.

Inspired by the great success from deep convolutional neural networks (CNNs) for single-label visual-semantic embedding, we exploit extending these models for multilabel images. We propose a new learning paradigm for multilabel image classification, in which labels are ranked according to its relevance to the input image. In contrast to conventional CNN models that learn a latent vector representation (i.e., the image embedding vector), the developed visual model learns a mapping (i.e., a transformation matrix) from an image in an attempt to differentiate between its relevant and irrelevant labels. Despite the conceptual simplicity of our approach, the proposed model achieves state-of-the-art results on three public benchmark datasets.

Increased Risk of Sudden Sensorineural Hearing Loss in Patients With Osteoporosis: A Population-based, Propensity Score-matched, Longitudinal Follow-Up Study.

CONTEXT: Previous studies have reported an increased prevalence of sudden sensorineural hearing loss (SSNHL) in osteoporotic patients. However, the risk of SSNHL in this population remains unclear. OBJECTIVE: This study investigated the risk of SSNHL in osteoporotic patients. SETTING: Taiwan launched a single-payer National Health Insurance (NHI) program on March 1, 1995. NHI covers nearly all of Taiwan's residents. DESIGN: Using randomized representative sample of one million individuals from Taiwan's National Health Insurance claims database, we compared the data of 10,660 patients with newly diagnosed osteoporosis from 1998-2008 and with 31,980 patients without osteoporosis. All patients were tracked until SSNHL was diagnosed, death, or the end of 2011. Osteoporosis was identified based on a primary diagnosis of osteoporosis (ICD-9-CM code 7330) by dual-energy x-ray absorptiometry. INTERVENTION: Identified the diagnosis of osteoporosis and SSNHL by ICD-9CM code. MAIN OUTCOME MEASURE: The identification of patients with newly diagnosed SSNHL by ICD-9CM code. RESULTS: The incidence rates of SSNHL in the osteoporosis cohort and comparison group were 10.43 and 5.93 per 10,000 person years. Patients with osteoporosis were at 1.76 times the risk of developing SSNHL than patients without osteoporosis. The incidence rate ratio (IRR) for SSNHL was significantly greater in older (50-64 y and ≥ 65 y), and female patients, and borderline greater in hypertensive patients with osteoporosis than the controls, IRRs being 1.50, 2.33, 1.87, and 1.59. CONCLUSIONS: Patients with osteoporosis are at significantly greater risk of developing SSNHL.