End of the spectacular decrease in fall-related mortality rate: Men are catching up

Objectives: We determined time trends in numbers and rates of fall-related mortality in an aging population, for men and women. Methods. We performed secular trend analysis of fall-related deaths in the older Dutch population (persons aged 65 years or older) from 1969 to 2008, using the national Official-Cause-of-Death-Statistics. Results. Between 1969 and 2008, the age-adjusted fall-related mortality rate decreased from 202.1 to 66.7 per 100 000 older persons (decrease of 67%). However, the annual percentage change (change per year) in mortality rates was not constant, and could be divided into 3 phases: (1) a rapid decrease until the mid-1980s (men -4.1%; 95% confidence interval [CI] = -4.9, -3.2; women -6.5%; 95% CI, -7.1, -5.9), (2) flattening of the decrease until the mid-1990s (men -1.4%; 95% CI = -2.4, -0.4; women -2.0%; 95% CI = -3.4, -0.6), and (3) stable mortality rates for women (0.0%; 95% CI = -1.2, 1.3) and rising rates for men (1.9%; 95% CI = 0.6, 3.2) over the last decade. Conclusions. The spectacular decrease in fall-related mortality ended in the mid-1990s and is currently increasing in older men at

Violence Prevention in the Emergency Department: Future Research Priorities

The 2009 Academic Emergency Medicine Consensus Conference working group session participants developed recommendations and research questions for violence prevention in the emergency department (ED). A writing group devised a working draft prior to the meeting and presented this to the breakout session at the consensus conference for input and approval. The recommendations include: 1) promote and facilitate the collection of standardized information related to violence victimization and perpetration in ED settings; 2) develop and validate brief practical screening instruments that can identify those at risk for perpetration of violence toward others or toward self; 3) develop and validate brief practical screening instruments that can identify victims at risk for violent reinjury and mental health sequelae; and 4) conduct efficacy, translational, and dissemination research on interventions for violence prevention. The work group emphasized the critical need and role of ED-based research to impact surveillance and prevention of future violence-related injury.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78605/1/j.1553-2712.2009.00544.x.pd

Multiway modeling and analysis in stem cell systems biology

<p>Abstract</p> <p>Background</p> <p>Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.</p> <p>Results</p> <p>We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.</p> <p>Conclusion</p> <p>Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.</p