RESPIRATORY RESPONSES TO ACUTE INTERMITTENT HYPOXIA AND HYPERCAPNIA IN AWAKE RATS

This article deals with the recognition of early changes in the breathing pattern, in response to acute intermittent stimuli in awake rats. Two different types of stimuli were given: 9% hypoxia in N 2 and 10% hypercapnia in O 2 . Animals were exposed to 3 consecutive cycles consisting of 3-min stimulus period separated by 8-min normoxic recovery intervals. Features of the breathing pattern, such as respiratory frequency, tidal volume, minute ventilation, inspiration and expiration times, peak inspiratory and expiratory flows, were measured by whole body plethysmography. The data were analyzed with the use of pattern recognition methods. We conclude that the overall respiratory changes were rather slight. However, computerized analysis using a k-nearest neighbor decision rule (k-NN) allowed for a good recognition of the respiratory responses to the stimuli. The misclassification rate (E r ) varied from 5 to 10%. After feature selection, E r decreased below 1%. The k-NN classifier differentiated correctly also the type of intermittent stimulus. Our experimental results demonstrate usefulness of pattern recognition algorithms in studying respiratory effects in biological models

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis

IntroductionAdvances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome.MethodsMultivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality.ResultsWe identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters.ConclusionsHere we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients

Hippocampal Subfield Volumes: Age, Vascular Risk, and Correlation with Associative Memory

Aging and age-related diseases have negative impact on the hippocampus (HC), which is crucial for such age-sensitive functions as memory formation, maintenance, and retrieval. We examined age differences in hippocampal subfield volumes in 10 younger and 19 older adults, and association of those volumes with memory performance in the older participants. We manually measured volumes of HC regions CA1 and CA2 (CA1–2), sectors CA3 and CA4 plus dentate gyrus (CA3–4/DG), subiculum, and the entorhinal cortex using a contrast-optimized high-resolution PD-weighted MRI sequence. Although, as in previous reports, the volume of one region (CA1–2) was larger in the young, the difference was due to the presence of hypertensive subjects among the older adults. Among older participants, increased false alarm rate in an associative recognition memory task was linked to reduced CA3–4/DG volume. We discuss the role of the DG in pattern separation and the formation of discrete memory representations