Publications by authors named "Melissa Naylor"

Background: Orexin neuropeptides help regulate sleep/wake states, respiration, and pain. However, their potential role in regulating breathing, particularly in perioperative settings, is not well understood. TAK-925 (danavorexton), a novel, orexin receptor 2-selective agonist, directly activates neurons associated with respiratory control in the brain and improves respiratory parameters in rodents undergoing fentanyl-induced sedation.

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Study Objectives: Idiopathic hypersomnia (IH) is a chronic disorder characterized by excessive daytime sleepiness unexplained by another disorder or drug/medication use. Although the orexin system plays a role in sleep-wake regulation, orexin A levels in the cerebrospinal fluid are normal in people with IH. This phase 1b, randomized, placebo-controlled, crossover study aimed to investigate the safety, pharmacokinetics, and pharmacodynamics of danavorexton, a small-molecule orexin-2 receptor agonist, in adults with IH.

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Background: The aim of this study is to use classification methods to predict future onset of Alzheimer's disease in cognitively normal subjects through automated linguistic analysis.

Methods: To study linguistic performance as an early biomarker of AD, we performed predictive modeling of future diagnosis of AD from a cognitively normal baseline of Framingham Heart Study participants. The linguistic variables were derived from written responses to the cookie-theft picture-description task.

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Background: Dopamine D1 receptor signaling plays key roles in core domains of neural function, including cognition and reward processing; however, many questions remain about the functions of circuits modulated by dopamine D1 receptor, largely because clinically viable, selective agonists have yet to be tested in humans.

Methods: Using a novel, exploratory neurofunctional domains study design, we assessed the safety, tolerability, pharmacodynamics, and pharmacokinetics of PF-06412562, a selective D1/D5R partial agonist, in healthy male volunteers who met prespecified criteria for low working memory capacity. Functional magnetic resonance imaging, electrophysiologic endpoints, and behavioral paradigms were used to assess working memory, executive function, and motivation/reward processing following multiple-dose administration of PF-06412562.

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Multiple imputation by chained equations (MICE) has emerged as a leading strategy for imputing missing epidemiological data due to its ease of implementation and ability to maintain unbiased effect estimates and valid inference. Within the MICE algorithm, imputation can be performed using a variety of parametric or nonparametric methods. Literature has suggested that nonparametric tree-based imputation methods outperform parametric methods in terms of bias and coverage when there are interactions or other nonlinear effects among the variables.

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Background: PF-06412562 is an orally bioavailable, selective dopamine D1/D5 receptor partial agonist with a non-catechol structure under evaluation for treatment of cognitive impairment in schizophrenia.

Aims: This randomized, double-blind, placebo-controlled, parallel-group, Phase 1b study examined the pharmacokinetics and pharmacodynamics of three doses of PF-06412562 (3 mg, 9 mg, and 45 mg twice daily) over 15 days in patients with schizophrenia receiving antipsychotics.

Methods: Primary endpoints included adjunctive safety/tolerability and effects on MATRICS Consensus Cognitive Battery Working Memory domain and reward processing (Monetary Incentive Delay) tasks.

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Stable neuropsychological deficits may provide a reliable basis for identifying etiological subtypes of schizophrenia. The aim of this study was to identify clusters of individuals with schizophrenia based on dimensions of neuropsychological performance, and to characterize their neural correlates. We acquired neuropsychological data as well as structural and functional magnetic resonance imaging from 129 patients with schizophrenia and 165 healthy controls.

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Background: Genetic association studies of longitudinal cognitive phenotypes are an alternate approach to discovering genetic risk factors for Alzheimer's disease (AD). However, the standard linear mixed model approach is limited in the face of multidimensional longitudinal data and multiple genotypes. In this setting, the principal components of heritability (PCH) approach may increase efficiency by deriving a linear combination of phenotypes to maximize the heritability attributable to a particular genetic locus.

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Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model.

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For genome-wide association studies with family-based designs, we propose a Bayesian approach. We show that standard transmission disequilibrium test and family-based association test statistics can naturally be implemented in a Bayesian framework, allowing flexible specification of the likelihood and prior odds. We construct a Bayes factor conditional on the offspring phenotype and parental genotype data and then use the data we conditioned on to inform the prior odds for each marker.

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Arabidopsis thaliana glyoxalase 2-1 (GLX2-1) exhibits extensive sequence similarity with GLX2 enzymes but is catalytically inactive with SLG, the GLX2 substrate. In an effort to identify residues essential for GLX2 activity, amino acid residues were altered at positions 219, 246, 248, 325, and 328 in GLX2-1 to be the same as those in catalytically active human GLX2. The resulting enzymes were overexpressed, purified, and characterized using metal analyses, fluorescence spectroscopy, and steady-state kinetics to evaluate how these residues affect metal binding, structure, and catalysis.

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Background: Discovering genetic associations between genetic markers and gene expression levels can provide insight into gene regulation and, potentially, mechanisms of disease. Such analyses typically involve a linkage or association analysis in which expression data are used as phenotypes. This approach leads to a large number of multiple comparisons and may therefore lack power.

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Abnormalities of the medial temporal lobe have been consistently demonstrated in schizophrenia. A common functional polymorphism, Val108/158Met, in the putative schizophrenia susceptibility gene, catechol-O-methyltransferase (COMT), has been shown to influence medial temporal lobe function. However, the effects of this polymorphism on volumes of medial temporal lobe structures, particularly in patients with schizophrenia, are less clear.

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Genetic association studies of complex traits often rely on standardised quantitative phenotypes, such as percentage of predicted forced expiratory volume and body mass index to measure an underlying trait of interest (eg lung function, obesity). These phenotypes are appealing because they provide an easy mechanism for comparing subjects, although such standardisations may not be the best way to control for confounders and other covariates. We recommend adjusting raw or standardised phenotypes within the study population via regression.

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Glyoxalase II belongs to the metallo-beta-lactamase superfamily of proteins, possessing the characteristic dinuclear active site. Within this protein family, glyoxalase II from Arabidopsis thaliana is the first member to be isolated with significant amounts of iron, manganese, and zinc when being recombinantly produced in Escherichia coli. Enzyme preparations with different ratios of these three metals all yield k(cat)/K(M) values in the range of 1.

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