Effects of fatty acid amide hydrolase inhibitor URB597 in a rat model of trauma-induced long-term anxiety.

Rationale: The endocannabinoid neurotransmitter, anandamide, has been implicated in the central modulation of stress responses. Previous animal experiments have shown that inhibitors of the anandamide-degrading enzyme, fatty acid amide hydrolase (FAAH), enhance the ability to cope with acute and chronic stress.

Objectives: Here, we investigated the effects of the globally active FAAH inhibitor URB597 in a rat model of predator stress-induced long-term anxiety.

Increased Renal 2-Arachidonoylglycerol Level Is Associated with Improved Renal Function in a Mouse Model of Acute Kidney Injury.

Acute kidney injury (AKI) is associated with a significantly increased risk of morbidity and mortality. Ischemia-reperfusion injury (IRI) is a major cause of AKI. In this study, we investigated the role of the endocannabinoid (EC) system in renal IRI using a well-established mouse model.

Dysfunctional oleoylethanolamide signaling in a mouse model of Prader-Willi syndrome.

Prader-Willi syndrome (PWS), the leading genetic cause of obesity, is characterized by a striking hyperphagic behavior that can lead to obesity, type-2 diabetes, cardiovascular disease and death. The molecular mechanism underlying impaired satiety in PWS is unknown. Oleoylethanolamide (OEA) is a lipid mediator involved in the control of feeding, body weight and energy metabolism.

Brown adipose tissue quantification in human neonates using water-fat separated MRI.

There is a major resurgence of interest in brown adipose tissue (BAT) biology, particularly regarding its determinants and consequences in newborns and infants. Reliable methods for non-invasive BAT measurement in human infants have yet to be demonstrated. The current study first validates methods for quantitative BAT imaging of rodents post mortem followed by BAT excision and re-imaging of excised tissues.

Tree-structured feature extraction using mutual information.

One of the most informative measures for feature extraction (FE) is mutual information (MI). In terms of MI, the optimal FE creates new features that jointly have the largest dependency on the target class. However, obtaining an accurate estimate of a high-dimensional MI as well as optimizing with respect to it is not always easy, especially when only small training sets are available.