Management of thoracolumbar spine fractures

Background contextTraumatic fractures of the spine are most common at the thoracolumbar junction and can be a source of great disability.PurposeTo review the most current information regarding the pathophysiology, injury pattern, treatment options, and outcomes.Study designLiterature review.MethodsRelevant articles, textbook chapters, and abstracts covering thoracolumbar spine fractures with and without neurologic deficit from 1960 to the present were reviewed.ResultsThe thoracolumbar spine represents a unique system from a skeletal as well as neurological standpoint. The rigid rib-bearing thoracic spine articulates with the more mobile lumbar spine at the thoracolumbar junction (T10 - L2), the site of most fractures. A complete examination includes a careful neurologic examination of both motor and sensory systems. CT scans best describe bony detail while MRI is most efficient at describing soft tissues and neurological structures. The most recent classification system is that of the new Thoracolumbar Injury Classification and Severity Score. The different fracture types include compression fractures, burst fractures - both stable and unstable -, flexion-distraction injuries and fracture dislocations. Their treatment, both operative and non-operative depends on the degree of bony compromise, neurological involvement, and the integrity of the posterior ligamentous complex. Minimally invasive approaches to the care of thoracolumbar injuries have become more popular, thus, the evidence regarding their efficacy is presented. Finally, the treatment of osteoporotic fractures of the thoracolumbar spine is reviewed, including vertebroplasty and kyphoplasty, their risks and controversies, and senile burst fractures, as well.ConclusionsThoracolumbar spine fractures remain a significant source of potential morbidity. Advances in treatment have minimized the invasiveness of our surgery and in certain stable situations, eliminated it all together.     

Association between MRI findings and clinical outcomes in a period of 5 years after lumbar spine microdiscectomy

European Journal of Orthopaedic Surgery & Traumatology - To evaluate the associations between magnetic resonance imaging (MRI) findings and pain, disability and quality of life before surgery...     

A mathematical model of mortality dynamics across the lifespan combining heterogeneity and stochastic effects

The mortality patterns in human populations reflect biological, social and medical factors affecting our lives, and mathematical modelling is an important tool for the analysis of these patterns. It is known that the mortality rate in all human populations increases with age after sexual maturity. This increase is predominantly exponential and satisfies the Gompertz equation. Although the exponential growth of mortality rates is observed over a wide range of ages, it excludes early- and late-life intervals. In this work we accept the fact that the mortality rate is an exponential function of age and analyse possible mechanisms underlying the deviations from the exponential law across the human lifespan. We consider the effect of heterogeneity as well as stochastic factors in altering the exponential law and compare our results to publicly available age-dependent mortality data for Swedish and US populations. In a model of heterogeneous populations we study how differences in parameters of the Gompertz equation describing different subpopulations account for mortality dynamics at different ages. Particularly, we show that the mortality data on Swedish populations can be reproduced fairly well by a model comprising four subpopulations. We then analyse the influence of stochastic effects on the mortality dynamics to show that they play a role only at early and late ages, when only a few individuals contribute to mortality. We conclude that the deviations from exponential law at young ages can be explained by heterogeneity, namely by the presence of a subpopulation with high initial mortality rate presumably due to congenital defects, while those for old ages can be viewed as fluctuations and explained by stochastic effects.     

Evidence that NPHS2-R229Q predisposes to proteinuria and renal failure in familial hematuria

Background  

Familial hematuria (FH) is associated with at least two pathological entities: thin basement membrane nephropathy (TBMN), caused by heterozygous COL4A3/COL4A4 mutations, and C3 nephropathy caused by CFHR5 mutations. It is now known that TBMN patients develop proteinuria and changes of focal segmental glomerulosclerosis when biopsied. End-stage kidney disease (ESKD) is observed in 20% of carriers, at ages 50–70. A similar progression is observed in CFHR5 nephropathy. Recent evidence suggests that NPHS2-R229Q, a podocin polymorphism, may contribute to proteinuria in TBMN and to micro-albuminuria in the general population.     

Autologous platelet-rich plasma (PRP) effect on intervertebral disc restoration: an experimental rabbit model

European Journal of Orthopaedic Surgery & Traumatology - Platelet-rich plasma (PRP) treatment for intervertebral disc (IVD) repair and tissue engineering technologies have been the target of...     

Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets'' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time‐averaged approaches and sophisticated algorithms that can capture the time‐varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word ‘chronnectome’ (integration of the Greek word ‘Chronos’, which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well‐known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi‐faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject''s repeated fMRI scans.