Initial intramuscular perfusion pressure predicts early skeletal muscle function following isolated tibial fractures

<p>Abstract</p> <p>Background</p> <p>The severity of associated soft tissue trauma in complex injuries of the extremities guides fracture treatment and decisively determines patient's prognosis. Trauma-induced microvascular dysfunction and increased tissue pressure is known to trigger secondary soft tissue damage and seems to adversely affect skeletal muscle function.</p> <p>Methods</p> <p>20 patients with isolated tibial fractures were included. Blood pressure and compartment pressure (anterior and deep posterior compartment) were measured continuously up to 24 hours. Corresponding perfusion pressure was calculated. After 4 and 12 weeks isokinetic muscle peak torque and mean power of the ankle joint in dorsal and plantar flexion were measured using a Biodex dynamometer.</p> <p>Results</p> <p>A significant inverse correlation between the anterior perfusion pressure at 24 hours and deficit in dorsiflexion at 4 weeks was found for both, the peak torque (R = -0.83; p < 0.01) and the mean power (R = -0.84; p < 0.01). The posterior perfusion pressure at 24 h and the plantar flexion after 4 weeks in both, peak torque (R = -0.73, p =< 0.05) and mean power (R = -0.7, p =< 0.05) displayed a significant correlation.</p> <p>Conclusion</p> <p>The functional relationship between the decrease in intramuscular perfusion pressures and muscle performance in the early rehabilitation period indicate a causative and prognostic role of early posttraumatic microcirculatory derangements and skeletal muscle function. Therapeutic concepts aimed at effective muscle recovery, early rehabilitation, and decreased secondary tissue damage, should consider the maintenance of an adequate intramuscular perfusion pressure.</p

Differential transcriptional profiling of damaged and intact adjacent dorsal root ganglia neurons in neuropathic pain

Neuropathic pain, caused by a lesion in the somatosensory system, is a severely impairing mostly chronic disease. While its underlying molecular mechanisms are not thoroughly understood, neuroimmune interactions as well as changes in the pain pathway such as sensitization of nociceptors have been implicated. It has been shown that not only are different cell types involved in generation and maintenance of neuropathic pain, like neurons, immune and glial cells, but, also, intact adjacent neurons are relevant to the process. Here, we describe an experimental approach to discriminate damaged from intact adjacent neurons in the same dorsal root ganglion (DRG) using differential fluorescent neuronal labelling and fluorescence-activated cell sorting (FACS). Two fluorescent tracers, Fluoroemerald (FE) and 1-dioctadecyl-3,3,3,3-tetramethylindocarbocyanine perchlorate (DiI), were used, whose properties allow us to distinguish between damaged and intact neurons. Subsequent sorting permitted transcriptional analysis of both groups. Results and qPCR validation show a strong regulation in damaged neurons versus contralateral controls as well as a moderate regulation in adjacent neurons. Data for damaged neurons reveal an mRNA expression pattern consistent with established upregulated genes like galanin, which supports our approach. Moreover, novel genes were found strongly regulated such as corticotropinreleasing hormone (CRH), providing novel targets for further research. Differential fluorescent neuronal labelling and sorting allows for a clear distinction between primarily damaged neuropathic neurons and "bystanders," thereby facilitating a more detailed understanding of their respective roles in neuropathic processes in the DRG

Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care