Vasopressin attenuates ischemia-reperfusion injury via reduction of oxidative stress and inhibition of mitochondrial permeability transition pore opening in rat hearts

Aim of this study was to investigate the involvement of the mitochondrial permeability transition pore (MPTP) and oxidative stress in the cardioprotective effect of vasopressin (AVP) on ischemia/reperfusion (I/R) injury. Anesthetized male wistar rats were subjected to regional 30 min ischemia and 120 min reperfusion and randomly divided into nine groups: (1) Control; saline was administered intravenously before ischemia, (2) vasopressin was administrated 10 min prior to ischemia, (3, 4) Atractyloside as MPTP opener, was injected 5 min prior to reperfusion without and with vasopressin, (5, 6) Cyclosporine A as a MPTP closer, was injected 5 min prior to reperfusion without and with vasopressin, (7) mitochondria were isolated from control group and CaCl2 was added as MPTP opener and swelling inducer, (8) isolated mitochondria from Control hearts was incubated with Cyclosporine A before adding the CaCl2 (9) CaCl2 was added to isolated mitochondria from vasopressin group. Infusion of vasopressin decreased infarct size (18.6±1.7% vs. control group 37.6±2.4%), biochemical parameters [LDH (Lactate Dehydrogenase), CK-MB (Creatine Kinase-MB) and MDA (Malondialdehyde) plasma levels, PAB (Prooxidant-antioxidant balance)] compared to control group. Atactyloside suppressed the cardioprotective effect of vasopressin (32.5±1.9% vs. 18.6±1.7%) but administration of the Cyclosporine A without and with vasopressin significantly reduced infarct size to 17.7±4% (P<0.001) and 22.7±3% (P<0.01) respectively, vs. 37.6±2.4% in control group. Also, vasopressin, similar to Cyclosporine A, led to decrease in CaCl2-induced swelling. It seems that vasopressin through antioxidant effect and MPTP inhibition has created a cardioprotection against ischemia/reperfusion injuries. © 2015 Elsevier B.V. All rights reserved

A Flexible Framework for Anomaly Detection via Dimensionality Reduction

Anomaly detection is challenging, especially for large datasets in high dimensions. Here we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. We release DRAMA, a general python package that implements the general framework with a wide range of built-in options. We test DRAMA on a wide variety of simulated and real datasets, in up to 3000 dimensions, and find it robust and highly competitive with commonly-used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning and highly unbalanced datasets.Comment: 6 page