Relative contributions of connexins 40 and 43 to atrial impulse propagation in synthetic strands of neonatal and fetal murine cardiomyocytes

Atrial tissue expresses both connexin 40 (Cx40) and 43 (Cx43) proteins. To assess the relative roles of Cx40 and Cx43 in atrial electrical propagation, we synthesized cultured strands of atrial myocytes derived from mice with genetic deficiency in Cx40 or Cx43 expression and measured propagation velocity (PV) by high-resolution optical mapping of voltage-sensitive dye fluorescence. The amount of Cx40 and/or Cx43 in gap junctions was measured by immunohistochemistry and total or sarcolemmal Cx43 or Cx40 protein by immunoblotting. Progressive genetic reduction in Cx43 expression decreased PV from 34+/-6 cm/sec in Cx43(+/+) to 30+/-8 cm/sec in Cx43(+/-) and 19+/-11 cm/sec in Cx43(-/-) cultures. Concomitantly, the cell area occupied by Cx40 immunosignal in gap junctions decreased from 2.0+/-1.6% in Cx43(+/+) to 1.7+/-0.5% in Cx43(+/-) and 1.0+/-0.2% in Cx43(-/-) strands. In contrast, progressive genetic reduction in Cx40 expression increased PV from 30+/-2 cm/sec in Cx40(+/+) to 40+/-7 cm/sec in Cx40(+/-) and 45+/-10 cm/sec in Cx40(-/-) cultures. Concomitantly, the cell area occupied by Cx43 immunosignal in gap junctions increased from 1.2+/-0.9% in Cx40(+/+) to 2.8+/-1.4% in Cx40(+/-) and 3.1+/-0.6% in Cx40(-/-) cultures. In accordance with the immunostaining results, immunoblots of the Triton X-100-insoluble fraction revealed an increase of Cx43 in gap junctions in extracts from Cx40-ablated atria, whereas total cellular Cx43 remained unchanged. Our results suggest that the relative abundance of Cx43 and Cx40 is an important determinant of atrial impulse propagation in neonatal hearts, whereby dominance of Cx40 decreases and dominance of Cx43 increases local propagation velocity.

Practical sources of error in measuring pulmonary artery occlusion pressure: a study in participants of a special intensivist training program of The Scandinavian Society of Anaesthesiology and Intensive Care Medicine (SSAI)

BACKGROUND: Physiological data obtained with the pulmonary artery catheter (PAC) are susceptible to errors in measurement and interpretation. Little attention has been paid to the relevance of errors in hemodynamic measurements performed in the intensive care unit (ICU). The aim of this study was to assess the errors related to the technical aspects (zeroing and reference level) and actual measurement (curve interpretation) of the pulmonary artery occlusion pressure (PAOP). METHODS: Forty-seven participants in a special ICU training program and 22 ICU nurses were tested without pre-announcement. All participants had previously been exposed to the clinical use of the method. The first task was to set up a pressure measurement system for PAC (zeroing and reference level) and the second to measure the PAOP. RESULTS: The median difference from the reference mid-axillary zero level was - 3 cm (-8 to + 9 cm) for physicians and -1 cm (-5 to + 1 cm) for nurses. The median difference from the reference PAOP was 0 mmHg (-3 to 5 mmHg) for physicians and 1 mmHg (-1 to 15 mmHg) for nurses. When PAOP values were adjusted for the differences from the reference transducer level, the median differences from the reference PAOP values were 2 mmHg (-6 to 9 mmHg) for physicians and 2 mmHg (-6 to 16 mmHg) for nurses. CONCLUSIONS: Measurement of the PAOP is susceptible to substantial error as a result of practical mistakes. Comparison of results between ICUs or practitioners is therefore not possible.

Error models for microarray intensities

We derive the additive-multiplicative error model for microarray intensities, and describe two applications. For the detection of differentially expressed genes, we obtain a statistic whose variance is approximately independent of the mean intensity. For the post hoc calibration (normalization) of data with respect to experimental factors, we describe a method for parameter estimation.

Model Evaluation Based on the Distribution of Estimated Absolute Prediction Error

The construction of a reliable, practically useful prediction rule for future response is heavily dependent on the "adequacy" of the fitted regression model. In this article, we consider the absolute prediction error, the expected value of the absolute difference between the future and predicted responses, as the model evaluation criterion. This prediction error is easier to interpret than the average squared error and is equivalent to the mis-classification error for the binary outcome. We show that the distributions of the apparent error and its cross-validation counterparts are approximately normal even under a misspecified fitted model. When the prediction rule is "unsmooth", the variance of the above normal distribution can be estimated well via a perturbation-resampling method. We also show how to approximate the distribution of the difference of the estimated prediction errors from two competing models. With two real examples, we demonstrate that the resulting interval estimates for prediction errors provide much more information about model adequacy than the point estimates alone.

Inference on Survival Data with Covariate Measurement Error - An Imputation-based Approach

We propose a new method for fitting proportional hazards models with error-prone covariates. Regression coefficients are estimated by solving an estimating equation that is the average of the partial likelihood scores based on imputed true covariates. For the purpose of imputation, a linear spline model is assumed on the baseline hazard. We discuss consistency and asymptotic normality of the resulting estimators, and propose a stochastic approximation scheme to obtain the estimates. The algorithm is easy to implement, and reduces to the ordinary Cox partial likelihood approach when the measurement error has a degenerative distribution. Simulations indicate high efficiency and robustness. We consider the special case where error-prone replicates are available on the unobserved true covariates. As expected, increasing the number of replicate for the unobserved covariates increases efficiency and reduces bias. We illustrate the practical utility of the proposed method with an Eastern Cooperative Oncology Group clinical trial where a genetic marker, c-myc expression level, is subject to measurement error.

Gap junction channels and cardiac impulse propagation

The role of gap junction channels on cardiac impulse propagation is complex. This review focuses on the differential expression of connexins in the heart and the biophysical properties of gap junction channels under normal and disease conditions. Structural determinants of impulse propagation have been gained from biochemical and immunocytochemical studies performed on tissue extracts and intact cardiac tissue. These have defined the distinctive connexin coexpression patterns and relative levels in different cardiac tissues. Functional determinants of impulse propagation have emerged from electrophysiological experiments carried out on cell pairs. The static properties (channel number and conductance) limit the current flow between adjacent cardiomyocytes and thus set the basic conduction velocity. The dynamic properties (voltage-sensitive gating and kinetics of channels) are responsible for a modulation of the conduction velocity during propagated action potentials. The effect is moderate and depends on the type of Cx and channel. For homomeric-homotypic channels, the influence is small to medium; for homomeric-heterotypic channels, it is medium to strong. Since no data are currently available on heteromeric channels, their influence on impulse propagation is speculative. The modulation by gap junction channels is most prominent in tissues at the boundaries between cardiac tissues such as sinoatrial node-atrial muscle, atrioventricular node-His bundle, His bundle-bundle branch and Purkinje fibers-ventricular muscle. The data predict facilitation of orthodromic propagation.