The Effect of Naloxone Access Laws on Fatal Synthetic Opioid Overdose Fatality Rates

Background: Increases in fatal synthetic opioid overdoses over the past 8 years have left states scrambling for effective means to curtail these deaths. Many states have implemented policies and increased service capacity to address this rise. To better understand the effectiveness of policy level interventions we estimated the impact of the presence of naloxone access laws (NALs) on synthetic opioid fatalities at the state level. Methods: A multivariable longitudinal linear mixed model with a random intercept was used to determine the relationship between the presence of NALs and synthetic opioid overdose death rates, while controlling for, Good Samaritan laws, opioid prescription rate, and capacity for medication for opioid use disorder (MOUD), utilizing a quadratic time trajectory. Data for the study was collected from the National Vital Statistics System using multiple cause-of-death mortality files linked to drug overdose deaths. Results: The presence of an NAL had a significant (univariate P-value = .013; multivariable p-value = .010) negative relationship to fentanyl overdose death rates. Other significant controlling variables were quadratic time (univariate and multivariable P-value \u3c .001), MOUD (univariate P-value \u3c .001; multivariable P-value = .009), and Good Samaritan Law (univariate P-value = .033; multivariable P-value = .018). Conclusion: Naloxone standing orders are strongly related to fatal synthetic opioid overdose reduction. The effect of NALs, MOUD treatment capacity, and Good Samaritan laws all significantly influenced the synthetic opioid overdose death rate. The use of naloxone should be a central part of any state strategy to reduce overdose death rate

Reproducibility in the absence of selective reporting : An illustration from large-scale brain asymmetry research

Altres ajuts: Max Planck Society (Germany).The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes

Solution-Phase Nuclear Magnetic Resonance Studies of a Nonribosomal Peptide Synthetase Adenylation Domain, of a Bacterial Glycosyltransferase, and the Rational Design of Inhibitors and Mutants of Glycosyltransferases

<p>A molecule's biological function is determined by its chemical structure and its three dimensional (3D) shape. While a molecule's chemical structure is fairly static its physical 3D structure is typically very dynamic and thus more difficult to determine. A protein's 3D structure is actually an ensemble of shapes that it can assume depending on its immediate surroundings. The two main methods of determining a protein's 3D structure at high resolution are X-ray crystallography and nuclear magnetic resonance (NMR). These two methods complement each other by allowing for a protein's 3D shape to be studied in a wider variety of environments than either one alone can do. We are working to develop new methods for determining the 3D structures of proteins in solution by NMR, with and without ligands present that may bind to them. In particular we are developing NMR methods for studying the solution-phase 3D structures of large, biologically important, enzymes. </p><p>We are interested in determining the solution-phase 3D structures of enzymes at the atomic level so that we can understand their biological functions and how they accomplish them, and thus how to control them in order to treat diseases and improve human health. We are also interested in using high resolution structures of enzymes to do structure-based reengineering of them. Redesigning enzymes enhances our understanding of how they function in their native environment and leads to redesigned</p><p>versions of them that can be used to chemoenzymatically synthesize clinically important drugs. </p><p>This dissertation begins with our studies, by NMR, of the solution-phase structures of two bacterial enzymes involved in the biosynthesis of antibiotics. In particular we studied the solution-phase structures of the adenylation domain responsible for selectively activating the amino acid phenylalanine in the biosynthetic pathway for the antibiotic gramicidin S. Next, we present our studies of two glycosylation enzymes involved in the final phase of biosynthesis of the antibiotic vancomycin. We compared two approaches to determine the amino acids involved in substrate binding by these two enzymes, a solution-phase NMR approach and an in silico protein modeling, with ligand docking, approach. These enzymes are each quite large for current NMR solution-phase techniques and we present the lessons we learned from studying them and our plans for future work. Finally, we present a review of the use of small-molecule inhibitors and enzyme redesign in the study of the function of glycosyltransferases, with applications in the treatment of glycosylation disorders in humans and the chemoenzymatic synthesis of homogeneously glycosylated molecules.</p>Dissertatio