Protein kinase Cθ is required for cardiomyocyte survival and cardiac remodeling.

Protein kinase Cs (PKCs) constitute a family of serine/threonine kinases, which has distinguished and specific roles in regulating cardiac responses, including those associated with heart failure. We found that the PKCθ isoform is expressed at considerable levels in the cardiac muscle in mouse, and that it is rapidly activated after pressure overload. To investigate the role of PKCθ in cardiac remodeling, we used PKCθ(-/-) mice. In vivo analyses of PKCθ(-/-) hearts showed that the lack of PKCθ expression leads to left ventricular dilation and reduced function. Histological analyses showed a reduction in the number of cardiomyocytes, combined with hypertrophy of the remaining cardiomyocytes, cardiac fibrosis, myofibroblast hyper-proliferation and matrix deposition. We also observed p38 and JunK activation, known to promote cell death in response to stress, combined with upregulation of the fetal pattern of gene expression, considered to be a feature of the hemodynamically or metabolically stressed heart. In keeping with these observations, cultured PKCθ(-/-) cardiomyocytes were less viable than wild-type cardiomyocytes, and, unlike wild-type cardiomyocytes, underwent programmed cell death upon stimulation with α1-adrenergic agonists and hypoxia. Taken together, these results show that PKCθ maintains the correct structure and function of the heart by preventing cardiomyocyte cell death in response to work demand and to neuro-hormonal signals, to which heart cells are continuously exposed.

The use of artificial neural networks methodology in the assessment of "vulnerability" to heroin use among army corps soldiers: a preliminary study of 170 cases inside the Military Hospital of Legal Medicine of Verona.

This article describes a preliminary study of screening/diagnostic instruments for prediction for large-scale application in the military field at the Neuropsychiatric Department of the Military Hospital of Legal Medicine of Verona and for the prevention of self-destructive behaviors, particularly through the use of drugs. 170 subjects divided into three subsamples were examined. The first subsample was characterized by a strong tendency towards normalcy, the second by a strong tendency towards pathology, and the third by a great variety of expressions of psychological and social problems, which were not necessarily related to drug use. These subjects were administered a questionnaire designed according to Squashing Theory principles (Buscema, 1994a). Answers were processed by an Artificial Neural Network created by Semeion in Rome (Buscema, 1996) and were compared with a standard clinical psychiatric assessment report and with the results of psychodiagnostic tests. Results document ANNs' remarkable ability to recognize subjects with declared, in exordium and "at risk" pathological behaviors. Blind results on learning and trial samples show a very high predictive capacity (over 90%). A comparison with the examined subjects' clinical report and the results of the first follow-up also document very high agreements. The broad variation of answers obtained in the third subsample allows further methodological reflections on the contribution of Artificial Neural Networks and Squashing Theory to the study of deviance, for both sociologists and clinicians, and not only for those in the field of drug addiction.