Ageing, adipose tissue, fatty acids and inflammation

A common feature of ageing is the alteration in tissue distribution and composition, with a shift in fat away from lower body and subcutaneous depots to visceral and ectopic sites. Redistribution of adipose tissue towards an ectopic site can have dramatic effects on metabolic function. In skeletal muscle, increased ectopic adiposity is linked to insulin resistance through lipid mediators such as ceramide or DAG, inhibiting the insulin receptor signalling pathway. Additionally, the risk of developing cardiovascular disease is increased with elevated visceral adipose distribution. In ageing, adipose tissue becomes dysfunctional, with the pathway of differentiation of preadipocytes to mature adipocytes becoming impaired; this results in dysfunctional adipocytes less able to store fat and subsequent fat redistribution to ectopic sites. Low grade systemic inflammation is commonly observed in ageing, and may drive the adipose tissue dysfunction, as proinflammatory cytokines are capable of inhibiting adipocyte differentiation. Beyond increased ectopic adiposity, the effect of impaired adipose tissue function is an elevation in systemic free fatty acids (FFA), a common feature of many metabolic disorders. Saturated fatty acids can be regarded as the most detrimental of FFA, being capable of inducing insulin resistance and inflammation through lipid mediators such as ceramide, which can increase risk of developing atherosclerosis. Elevated FFA, in particular saturated fatty acids, maybe a driving factor for both the increased insulin resistance, cardiovascular disease risk and inflammation in older adults

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior