Glibenclamide for the Treatment of Ischemic and Hemorrhagic Stroke

Ischemic and hemorrhagic strokes are associated with severe functional disability and high mortality. Except for recombinant tissue plasminogen activator, therapies targeting the underlying pathophysiology of central nervous system (CNS) ischemia and hemorrhage are strikingly lacking. Sur1-regulated channels play essential roles in necrotic cell death and cerebral edema following ischemic insults, and in neuroinflammation after hemorrhagic injuries. Inhibiting endothelial, neuronal, astrocytic and oligodendroglial sulfonylurea receptor 1–transient receptor potential melastatin 4 (Sur1–Trpm4) channels and, in some cases, microglial K_ATP (Sur1–Kir6.2) channels, with glibenclamide is protective in a variety of contexts. Robust preclinical studies have shown that glibenclamide and other sulfonylurea agents reduce infarct volumes, edema and hemorrhagic conversion, and improve outcomes in rodent models of ischemic stroke. Retrospective studies suggest that diabetic patients on sulfonylurea drugs at stroke presentation fare better if they continue on drug. Additional laboratory investigations have implicated Sur1 in the pathophysiology of hemorrhagic CNS insults. In clinically relevant models of subarachnoid hemorrhage, glibenclamide reduces adverse neuroinflammatory and behavioral outcomes. Here, we provide an overview of the preclinical studies of glibenclamide therapy for CNS ischemia and hemorrhage, discuss the available data from clinical investigations, and conclude with promising preclinical results that suggest glibenclamide may be an effective therapeutic option for ischemic and hemorrhagic stroke.     

State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

Steels for Bridge Structures

Metallurgist - We consider domestic and foreign standards for rolled metals used in bridge building. Domestic standards contain elevated requirements to the reliability of rolled metals in terms of...     

Investigation into the Influence of Sodium Lignosulfonate,Anionic Surfactants,and Their Mixtures on the Copper Cementation Rate by Zinc

Russian Journal of Non-Ferrous Metals - This article is devoted to the influence of sodium lignosulfonate (SL), anionic surfactants (sodium dodecylsulfate (SDS), sodium dodecylbenzene sulfonate...