Noninvasive assessment of diffusion hypoxia following administration of nitrous oxide-oxygen.

The phenomenon of diffusion hypoxia is commonly believed to occur unless nitrous oxide-oxygen inhalation sedation is followed by "washout" with 100% oxygen for 5 minutes upon termination of the flow of nitrous oxide. When systematically studied, however, this phenomenon generally appears to be unfounded. The present study evaluated the effect of breathing room air instead of 100% oxygen in healthy (ASA 1) human volunteers following administration of sedative concentrations of nitrous oxide. The occurrence of hypoxia was determined objectively, using pulse oximetry and a standardized psychomotor skills test (Trieger test). Diffusion hypoxia was not observed using these criteria.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Comparative efficacy of 2 topical anesthetics for the placement of orthodontic temporary anchorage devices.

This study compared the effectiveness of topical benzocaine 20% versus a combination of lidocaine, tetracaine, and phenylephrine in providing sufficient analgesia for the placement of orthodontic temporary anchorage devices (TADs). The 2 topical anesthetics were tested against each other bilaterally using a randomized, double-blind, crossover design. The agents were left in place for the amount of time prescribed by the manufacturer. The TAD was then placed, and each subject rated the degree of pain on a Heft-Parker visual analogue scale. A pulse oximeter was used to record the preoperative and postoperative pulse rates. Statistically significant differences in perceived pain (P < .05) and success rate (P < .01) between drugs were seen, but no significant difference in pulse rate change between the topical anesthetics was observed (P > .05). It was concluded that when the efficacy of topical benzocaine and of a combination product was compared as the sole anesthetic to facilitate acceptable pain control for placement of orthodontic temporary anchorage devices, the combination product was considerably more efficacious.

Identification and characterization of distinct classes of cAMP-pulse-induced genes in Dictyostelium discoideum development

The unicellular amoeba Dictyostelium discoideum embarks on a developmental program upon starvation. During development, extracellular oscillatory cAMP signaling orchestrates the chemotaxis-mediated aggregation of ∼105 amoebae and is required for optimal induction of so-called pulse-induced genes. This requirement for pulsatile CAMP reflects adaptation of the cAMP-receptor-mediated pathways that regulate these genes. Through examination of a collection of pulse-induced genes, we defined two distinct gene classes based on their induction kinetics and the impact of mutations that impair PKA signaling. The first class (represented by D2 and prtA) is highly dependent on PKA signaling, whereas the second class (represented by carA, gpaB, and acaA) is not. Analysis of expression kinetics revealed that these classes are sequentially expressed with the PKA-independent genes peaking in expression before the PKA-dependent class. Experiments with cycloheximide, an inhibitor of translation, demonstrated that the pulse induction of both classes depends on new protein synthesis early in development. carA and gpaB also exhibit pulse-independent, starvation-induced expression which, unlike their pulse induction, was found to be insensitive to cycloheximide added at the outset of starvation. This result indicates that the mechanism of starvation induction pre-exists in growing cells and is distinct from the pulse induction mechanism for these genes. In order to identify cis-acting elements that are critical for induction of carA, we constructed a GFP reporter controlled by a 914-base-pair portion of its promoter and verified that its expression was PKA-independent, pulse-inducible, and developmentally regulated like the endogenous carA gene. By a combination of truncation, internal deletion, and site-directed mutation, we defined several distinct functional elements within the carA promoter, including a 39-bp region required for pulse induction between base pairs -321 and -282 (relative to the transcription start site), a 131-bp region proximal to the start site that is sufficient for starvation induction, and two separate enhancer domains. Identification of factors that interact with these promoter elements and genetic approaches exploiting the GFP reporter described here should help complete our understanding of the mechanisms regulating these genes, including adaptation mechanisms that likely also govern chemotaxis of Dictyostelium and mammalian cells. ^