Publications by authors named "David Keith Williams"

Introduction: Infancy is a stage characterized by multiple brain and cognitive changes. In a short time, infants must consolidate a new brain network and develop two important properties for speech comprehension: phonemic normalization and categorical perception. Recent studies have described diet as an essential factor in normal language development, reporting that breastfed infants show an earlier brain maturity and thus a faster cognitive development.

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Aim: Noninvasive electrical stimulation at acupuncture points (NESAP) for analgesia is used in children, but has not been widely studied in neonates. The purpose of this study was to determine whether NESAP alone or in combination with sucrose relieved heelstick pain in neonates.

Methods: Term neonates (n = 162) receiving routine heelsticks for newborn screening were enrolled following parental consent.

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Background: Commonly when designing studies, researchers propose to measure several independent variables in a regression model, a subset of which are identified as the main variables of interest while the rest are retained in a model as covariates or confounders. Power for linear regression in this setting can be calculated using SAS PROC POWER. There exists a void in estimating power for the logistic regression models in the same setting.

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Chronic (+)-methamphetamine (METH) use during pregnancy increases the health risk for both mother and fetus. To provide insights into these risks, the relationship between changes in METH disposition and METH-induced pharmacological effects were studied in Sprague-Dawley rat dams and litters. Timed-pregnant rats (n = 5-6) were given saline or METH (5.

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Background: The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence.

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Background: Mathematical models describing growth kinetics are very important for predicting many biological phenomena such as tumor volume, speed of disease progression, and determination of an optimal radiation and/or chemotherapy schedule. Growth models such as logistic, Gompertz, Richards, and Weibull have been extensively studied and applied to a wide range of medical and biological studies. We introduce a class of three and four parameter models called "hyperbolastic models" for accurately predicting and analyzing self-limited growth behavior that occurs e.

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