No abstract

Thus, our modification considerably shortened the time taken for creating the maze in comparison to the Cox's maze procedure and was effective in restoring normal sinus rhythm in 80% of the patients.

Behavioral neurogenetics research is a new method of scientific inquiry that focuses on investigation of neurodevelopmental dysfunction associated with specific genetic conditions. This research method provides a powerful tool for scientific inquiry into human gene-brain-behavior linkages that complements more traditional research approaches. In particular, the use of specific genetic conditions as models of common behavioral and cognitive disorders occurring in the general population can reveal insights into neurodevelopmental pathways that might otherwise be obscured or diluted when investigating more heterogeneous, behaviorally defined subject groups. In this paper, we review five genetic conditions that commonly give rise to identifiable neurodevelopmental and neuropsychiatric disability in children: fragile X syndrome, velo-cardio-facial syndrome, Williams syndrome, Turner syndrome, and Klinefelter syndrome. While emphasis is placed on describing the brain morphology associated with these conditions as revealed by neuroimaging studies, we also include information pertaining to molecular genetic, postmortem, and neurobehavioral investigations to illustrate how behavioral neurogenetics research can contribute to an improved understanding of brain disorders in childhood.

S everal predictive models are currently being used for risk stratification and clinical decision-making in cardiovascular medicine and primary healthcare.1,2 Most models are based on the traditional cardiovascular risk factors (TRF), that is, age, sex, blood pressure, total cholesterol, high-density lipoprotein cholesterol, and smoking status, and with the estimated 10-year risk of either cardiovascular mortality or event rate as outcome. However, it is evident that the traditional risk factors do not adequately reflect all cardiovascular risk because the majority of individuals who experience a first time cardiovascular event have adverse levels in <2 traditional risk factors and are misidentified as being at low risk. 3 Both the successes and shortcomings of the traditional risk factors have stimulated research into identifying additional biomarkers, that is, biological signals, which can be used to improve on current cardiovascular disease risk models, or are indicators of progressive subclinical disease and, as such, would have utility in predicting cardiovascular event risk, improve on traditional predictive models, and lead to more accurate treatment decisions. Blood-based biomarkers that can be easily integrated into patient management in the primary care setting are particularly desirable. Of the <60 different proteins screened to date, only 3, C-reactive protein (CRP), N-terminal prohormone of brain natriuretic peptide, and cardiac troponin I, have been shown, in combination only, to add incremental value to TRF-based predictive models of first-time CVD. 4 However, their clinical utility in preventive cardiology has not been clearly established. CRP, like other acute phase proteins, such as fibrinogen, is widely recognized to be a marker of a general inflammatory state that contributes to cardiovascular Background-Identification of individuals with high risk for first-ever myocardial infarction (MI) can be improved. The objectives of the study were to survey multiple protein biomarkers for association with the 10-year risk of incident MI and identify a clinically significant risk model that adds information to current common risk models. Methods and Results-We used an immunoassay platform that uses a sensitive, sample-efficient molecular counting technology to measure 51 proteins in samples from the fourth survey (1994) in the Tromsø Study, a longitudinal study of men and women in Tromsø, Norway. A case control design was used with 419 first-ever MI cases (169 females/250 males) and 398 controls (244 females/154 males). Of the proteins measured, 17 were predictors of MI when considered individually after adjustment for traditional risk factors either in men, women, or both. The 6 biomarkers adjusted for traditional risk factors that were selected in a multivariable model (odds ratios [OR] per standard deviation) using a stepwise procedure were apolipoprotein B/apolipoprotein A1 ratio (1.40), kallikrein (0.73), lipoprotein a (1.29), matrix metalloproteinase 9 (1.30), the interaction term IP-10/CXCL10×...