Cardiorespiratory responses to systemic administration of a protein kinase C inhibitor in conscious rats.

Although protein kinase C (PKC) is an essential component of multiple neurally mediated events, its role in respiratory control remains undefined. The ventilatory effects of a systemically active PKC inhibitor (Ro-32-0432; 100 mg/kg i.p.) were assessed by whole body plethysmography during normoxia, hypoxia (10% O2), and hyperoxia (100% O2) in unrestrained Sprague-Dawley rats. A sustained expiratory time increase occurred within 8-10 min of injection in room air[mean 44.8 +/- 5.2 (SE) % ], was similar to expiratory time prolongations after Ro-32-0432 administration during 100% O2 (45.5 +/- 8.1%; not significant), and was associated with mild minute ventilation (VE) decreases. Hypercapnic ventilatory responses (5% CO2) remained unchanged after Ro-32-0432. During 10% O2, VE increased from 122.6 +/- 15.6 to 195.7 +/- 10.1 ml/min in vehicle-treated rats (P < 0.001). In contrast, marked attenuation of VE hypoxic responses occurred after Ro-32-0432 [86.2 +/- 6.2 ml/min in room air to 104.1 +/- 7.1 ml/min in 10% O2; pre- vs. post-Ro32-0432, P < 0.001 (analysis of variance)]. Overall, PKC activity was reduced and increases with hypoxia were abolished in the particulate subcellular fraction of brain tissue after Ro-32-0432 treatment, indicating that this compound readily crosses the blood-brain barrier. We conclude that systemic PKC inhibition elicits significant centrally mediated expiratory prolongations and ventilatory reductions as well as blunted ventilatory responses to hypoxia but not to hypercapnia. We postulate that PKC plays an important role in signal transduction pathways within brain regions underlying respiratory control.

Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra: classification of normal premalignant and malignant pathological conditions.

Pulsed laser-induced autofluorescence spectroscopic studies of pathologically certified normal, premalignant, and malignant oral tissues were carried out at 325 nm excitation. The spectral analysis and classification for discrimination among normal, premalignant, and malignant conditions were performed using principal component analysis (PCA) and artificial neural network (ANN) separately on the same set of spectral data. In case of PCA, spectral residuals, Mahalanobis distance, and scores of factors were used for discrimination among normal, premalignant, and malignant cases. In ANN, parameters like mean, spectral residual, standard deviation, and total energy were used to train the network. The ANN used in this study is a classical multiplayer feed-forward type with a back-propagation algorithm for the training of the network. The specificity and sensitivity were determined in both classification schemes. In the case of PCA, they are 100 and 92.9%, respectively, whereas for ANN they are 100 and 96.5% for the data set considered.