Christmas article: High incidence of ponytail in polytraumatised riders.

Introduction Imaging experience made us suspect an overrepresentation of ponytails in riders admitted as polytrauma after falling from their horse. Methods In a single-centre case-control study conducted over three months, we reviewed the records of all admitted polytraumatised patients for trauma mechanism and presence of ponytail on CT. Cerebral CTs were reviewed in the three standard imaging planes using a bone or lung window. Ponytail was diagnosed if most or all of the hair on the head was gathered and secured at the back of the head with a hair tie. Data were analysed with Fisher's exact test. Results Seven female riders (mean age 22 years) were admitted after falling from their horse (study group); six of these riders wore a ponytail. No male riders were admitted. Therefore, only female polytraumatised patients having suffered any other trauma were selected as controls. The control group consisted of 13 patients (mean age 33,5 years), two of whom wore a ponytail. In three controls, all without ponytails, the trauma also had been related to a horse. Thus, horses were involved in 50% of the traumas included in this study. Ponytail was found more frequently in riders admitted after falling from their horse, p less-than 0,005. Conclusion Having an almost circumferential vision, horses may be scared by the sideways swaying of a ponytail worn by their own rider. In riders, ponytails can trigger a sensation of tightness or even headache which may impair focus. Thus, while female riders most likely wear ponytails for practicality, ponytails may increase the risk of accident by affecting rider and horse. Further studies are required to determine if the observed association between ponytails and trauma is causal. Funding none. Trial registration not applicable.

Divalent metal transporter 1 regulates iron-mediated ROS and pancreatic ß cell fate in response to cytokines.

Reactive oxygen species (ROS) contribute to target-cell damage in inflammatory and iron-overload diseases. Little is known about iron transport regulation during inflammatory attack. Through a combination of in vitro and in vivo studies, we show that the proinflammatory cytokine IL-1ß induces divalent metal transporter 1 (DMT1) expression correlating with increased ß cell iron content and ROS production. Iron chelation and siRNA and genetic knockdown of DMT1 expression reduce cytokine-induced ROS formation and cell death. Glucose-stimulated insulin secretion in the absence of cytokines in Dmt1 knockout islets is defective, highlighting a physiological role of iron and ROS in the regulation of insulin secretion. Dmt1 knockout mice are protected against multiple low-dose streptozotocin and high-fat diet-induced glucose intolerance, models of type 1 and type 2 diabetes, respectively. Thus, ß cells become prone to ROS-mediated inflammatory damage via aberrant cellular iron metabolism, a finding with potential general cellular implications.

Integrative analysis for finding genes and networks involved in diabetes and other complex diseases.

We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases.

Novel analytical methods applied to type 1 diabetes genome-scan data.

Complex traits like type 1 diabetes mellitus (T1DM) are generally taken to be under the influence of multiple genes interacting with each other to confer disease susceptibility and/or protection. Although novel methods are being developed, analyses of whole-genome scans are most often performed with multipoint methods that work under the assumption that multiple trait loci are unrelated to each other; that is, most models specify the effect of only one locus at a time. We have applied a novel approach, which includes decision-tree construction and artificial neural networks, to the analysis of T1DM genome-scan data. We demonstrate that this approach (1) allows identification of all major susceptibility loci identified by nonparametric linkage analysis, (2) identifies a number of novel regions as well as combinations of markers with predictive value for T1DM, and (3) may be useful in characterizing markers in linkage disequilibrium with protective-gene variants. Furthermore, the approach outlined here permits combined analyses of genetic-marker data and information on environmental and clinical covariates.