Suprofen, A New Peripheral Analgesic1

ABSTRACT Capetola, Rober

A multidimensional metabolomics workflow to image biodistribution and evaluate pharmacodynamics in adult zebrafish

An integrated evaluation of the tissue distribution and pharmacodynamic properties of a therapeutic is essential for successful translation to the clinic. To date, however, cost-effective methods to measure these parameters at the systems level in model organisms are lacking. Here, we introduce a multidimensional workflow to evaluate drug activity that combines mass spectrometry-based imaging, absolute drug quantitation across different biological matrices, in vivo isotope tracing and global metabolome analysis in the adult zebrafish. As a proof of concept, we quantitatively determined the whole-body distribution of the anti-rheumatic agent hydroxychloroquine sulfate (HCQ) and measured the systemic metabolic impacts of drug treatment. We found that HCQ distributed to most organs in the adult zebrafish 24 h after addition of the drug to water, with the highest accumulation of both the drug and its metabolites being in the liver, intestine and kidney. Interestingly, HCQ treatment induced organ-specific alterations in metabolism. In the brain, for example, HCQ uniquely elevated pyruvate carboxylase activity to support increased synthesis of the neuronal metabolite, N-acetylaspartate. Taken together, this work validates a multidimensional metabolomics platform for evaluating the mode of action of a drug and its potential off-target effects in the adult zebrafish. This article has an associated First Person interview with the first author of the paper

Inferring dynamics from the wavenumber spectra of an eddying global ocean model with embedded tides

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94531/1/jgr_richmanetal_wavenumberspectra_2012.pd

Detection of local-scale population declines through optimized tidal marsh bird monitoring design

Evaluating the efficacy of monitoring designs is crucial for the successful monitoring and conservation of populations. For tidal marsh bird species of conservation concern, detecting population declines at local spatial scales within actionable time frames is a top priority. We examined and compared the effectiveness of alternative monitoring strategies for detecting local-scale population declines using count data from 1176 spatially-independent salt marsh sampling points throughout the northeastern United States (Maine to Virginia). We used abundance estimates that accounted for imperfect detection as initial conditions to simulate annual population declines of 5%, 10%, 30%, and 50% over a 5-year sampling period. Under an optimal monitoring design with biennial sampling, we were able to successfully detect annual population declines of ≥30% for each species and for all species combined. However, this required a minimum of 15–20 points per site being sampled. Power to detect declines, although low for detecting smaller annual declines (i.e., \u3c10%), improved substantially when points were visited twice per season, yet a third visit provided a reduced benefit. When testing factors that could potentially influence power to detect declines, we found that the power within sites was positively related to species abundance. Power was similar between biennial sampling (3 of 5 years) and annual sampling (5 of 5 years), suggesting a more cost-effective approach would be to sample every other year. We found that within most sites, detecting annual declines of 10% or less over a relatively short 5-year duration would be difficult. Hence, we recommend that salt marsh bird monitoring programs in the northeastern United States conduct two visits to each site per sampling year, include 15 or more sampling points per site (without confounding spatial independence), and conduct monitoring efforts every other year. This approach will maximize the efficacy of site-level monitoring of tidal marsh birds, which can aid in assessments of coastal wetland conservation and related habitat management efforts

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and Computin

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19

ALFRED: an allele frequency resource for research and teaching

ALFRED (http://alfred.med.yale.edu) is a free, web accessible, curated compilation of allele frequency data on DNA sequence polymorphisms in anthropologically defined human populations. Currently, ALFRED has allele frequency tables on over 663 400 polymorphic sites; 170 of them have frequency tables for more than 100 different population samples. In ALFRED, a population may have multiple samples with each ‘sample’ consisting of many individuals on which an allele frequency is based. There are 3566 population samples from 710 different populations with allele frequency tables on at least one polymorphism. Fifty of those population samples have allele frequency data for over 650 000 polymorphisms. Records also have active links to relevant resources (dbSNP, PharmGKB, OMIM, Ethnologue, etc.). The flexible search options and data display and download capabilities available through the web interface allow easy access to the large quantity of high-quality data in ALFRED

Transmission and Pathogenesis of Swine-Origin 2009 A(H1N1) Influenza Viruses in Ferrets and Mice

available in PMC 2010 October 12Recent reports of mild to severe influenza-like illness in humans caused by a novel swine-origin 2009 A(H1N1) influenza virus underscore the need to better understand the pathogenesis and transmission of these viruses in mammals. In this study, selected 2009 A(H1N1) influenza isolates were assessed for their ability to cause disease in mice and ferrets and compared with a contemporary seasonal H1N1 virus for their ability to transmit to naïve ferrets through respiratory droplets. In contrast to seasonal influenza H1N1 virus, 2009 A(H1N1) influenza viruses caused increased morbidity, replicated to higher titers in lung tissue, and were recovered from the intestinal tract of intranasally inoculated ferrets. The 2009 A(H1N1) influenza viruses exhibited less efficient respiratory droplet transmission in ferrets in comparison with the highly transmissible phenotype of a seasonal H1N1 virus. Transmission of the 2009 A(H1N1) influenza viruses was further corroborated by characterizing the binding specificity of the viral hemagglutinin to the sialylated glycan receptors (in the human host) by use of dose-dependent direct receptor-binding and human lung tissue–binding assays