A Cannabinoid CB 1 Receptor-Positive Allosteric Modulator Reduces Neuropathic Pain in the Mouse with No Psychoactive Effects

Peer reviewedPostprin

Title page Regulation of Inflammatory Pain by Inhibition of Fatty Acid Amide Hydrolase (FAAH)

Abstract Although cannabinoids are efficacious in laboratory animal models of inflammatory pain, their established cannabimimetic actions diminish enthusiasm for their therapeutic development. Conversely, fatty acid amide hydrolase (FAAH), the chief catabolic enzyme regulating the endogenous cannabinoid N-arachidonoylethanolamine (anandamide), has emerged as an attractive target to treat pain and other conditions. Here, we tested WIN55,212-2, a cannabinoid receptor agonist, as well as genetic deletion or pharmacological inhibition of FAAH in the lipopolysaccharide (LPS) mouse model of inflammatory pain. WIN55,212 significantly reduced edema and hotplate hyperalgesia caused by LPS infusion into the hind paws, though the mice also displayed analgesia and other CNS effects. FAAH (-/-) mice exhibited reduced paw edema and hyperalgesia in this model, without apparent cannabimimetic effects. Transgenic mice expressing FAAH exclusively on neurons continued to display the anti-edematous, but not the anti-hyperalgesic, phenotype. The CB 2 receptor antagonist, SR144528, blocked this non-neuronal, anti-inflammatory phenotype, and the CB 1 receptor antagonist, rimonabant, blocked the anti-hyperalgesic phenotype. The FAAH inhibitor, URB597 attenuated the development of LPS-induced paw edema and reversed LPS-induced hyperalgesia through respective CB 2 and CB 1 receptor mechanisms of action. However, the TRPV1 receptor antagonist, capsazepine, did not affect either the anti-hyperalgesic or antiedematous effects of URB597. Finally, URB597 attenuated levels of the pro-inflammatory cytokines IL-1β and TNF-α in LPS-treated paws. These findings demonstrate that simultaneous elevations in non-neuronal and neuronal endocannabinoid signaling are possible through inhibition of a single enzymatic target, thereby offering a potentially powerful strategy to treat chronic inflammatory pain syndromes that operate at multiple levels of anatomical integration

The Biology and Economics of Coral Growth

To protect natural coral reefs, it is of utmost importance to understand how the growth of the main reef-building organisms—the zooxanthellate scleractinian corals—is controlled. Understanding coral growth is also relevant for coral aquaculture, which is a rapidly developing business. This review paper provides a comprehensive overview of factors that can influence the growth of zooxanthellate scleractinian corals, with particular emphasis on interactions between these factors. Furthermore, the kinetic principles underlying coral growth are discussed. The reviewed information is put into an economic perspective by making an estimation of the costs of coral aquaculture

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages