Glucocorticoid-endocannabinoid interaction in cardiac surgical patients: relationship to early cognitive dysfunction and late depression

Background: Endocannabinoids (ECs) are rapidly acting immune-modulatory lipid-signaling molecules that are important for adaptation to stressful and aversive situations. They are known to interact with glucocorticoids and other stress-responsive systems. Maladaptation to acute or chronic stress represents a major risk factor for the development of psychiatric disorders. In the present study, we administered stress doses of hydrocortisone in a prospective, randomized, placebo-controlled double-blind study in patients undergoing cardiac surgery (CS) to examine the relationship between the use of glucocorticoids, plasma EC levels, and the occurrence of early postoperative cognitive dysfunction (delirium) and of later development of depression. Methods: We determined plasma levels of the ECs anandamide and 2-arachidonoylglycerol (2-AG) in CS patients of the hydrocortisone (n=56) and the placebo group (n=55) preoperatively, at postoperative day (POD) 1, at intensive care unit discharge, and at 6 months after CS (n=68). Postoperative delirium was diagnosed according to Diagnostic and Statistical Manual of the American Psychiatric Association IVth Edition (DSM-IV) criteria, and depression was determined by validated questionnaires and a standardized psychological interview (Structured Clinical Interview for DSM-IV). Results: Stress doses of hydrocortisone did not affect plasma EC levels and the occurrence of delirium or depression. However, patients who developed delirium on POD 1 had significantly lower preoperative 2-AG levels of the neuroprotective EC 2-AG (median values, 3.8 vs. 11.3 ng/ml; p=0.03). Preoperative 2-AG concentrations were predictive of postoperative delirium (sensitivity=0.70; specificity=0.69; cutoff value=4.9 ng/ml; receiver operating characteristic curve area=0.70; 95% confidence interval=0.54-0.85). Patients with depression at 6 months after CS (n=16) had significantly lower anandamide and 2-AG levels during the perioperative period. Conclusions: A low perioperative EC response may indicate an increased risk for early cognitive dysfunction and long-term depression in patients after CS. Glucocorticoids do not seem to influence this relationship

Development of a hydro-morphodynamic Model for Sediment Management in the Rosenheim Reservoir

Hydrodynamic

Optimization of computer-generated transmission holograms using different LED wavefront approximations

The reconstruction of computer-generated transmission volume holograms with light-emitting diodes (LEDs) requires the adaptation of the holograms to the specific diodes' wavefronts. We show first results of the analysis of different LED wavefront approximations, including a new interferometry-based approach to measure an LED wavefront

VieWBay – Wasserwirtschaftliche Modellierung der Fließgewässer Bayerns

Aufsatz veröffentlicht in: "Wasserbau-Symposium 2021: Wasserbau in Zeiten von Energiewende, Gewässerschutz und Klimawandel, Zurich, Switzerland, September 15-17, 2021, Band 2" veröffentlicht unter: https://doi.org/10.3929/ethz-b-00049975

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost