Surviving pediatric intensive care: physical outcome after 3 months

Objective: This study investigated the prevalence and nature of physical and neurocognitive sequelae in pediatric intensive care unit ( PICU) survivors. Design and setting: Prospective follow-up study 3 months after discharge from a 14-bed tertiary PICU in The Netherlands. Patients and participants: The families of 250 previously healthy children unexpectedly admitted to the PICU were invited to visit the outpatient follow-up clinic for structured medical examination of the child 3 months after discharge; 186 patients were evaluated. Measurements and results: Pediatric Cerebral Performance Category ( PCPC) and Pediatric Overall Performance Category ( POPC) values were determined at PICU discharge, at the outpatient follow-up clinic, and retrospectively before admission to the PICU. We found that 69% of children had physical sequelae. In 30% of cases these were caused by a previously unknown illness and in 39% by acquired morbidity. In 8% of the children the acquired morbidity was related to complications from PICU procedures. Three months after discharge 77% of the children had normal PCPC scores and 31% had normal POPC scores. Conclusions: Our results indicate that PICU survival may be associated with substantial physical sequelae. Structured follow-up research, preferably by multicenter studies, is warranted in PICU survivor

Ischemia of the lung causes extensive long-term pulmonary injury: an experimental study

Background: Lung ischemia-reperfusion injury (LIRI) is suggested to be a major risk factor for development of primary acute graft failure (PAGF) following lung transplantation, although other factors have been found to interplay with LIRI. The question whether LIRI exclusively results in PAGF seems difficult to answer, which is partly due to the lack of a long-term experimental LIRI model, in which PAGF changes can be studied. In addition, the long-term effects of LIRI are unclear and a detailed description of the immunological changes over time after LIRI is missing. Therefore our purpose was to establish a long-term experimental model of LIRI, and to study the impact of LIRI on the development of PAGF, using a broad spectrum of LIRI parameters including leukocyte kinetics.Methods: Male Sprague-Dawley rats (n = 135) were subjected to 120 minutes of left lung warm ischemia or were sham-operated. A third group served as healthy controls. Animals were sacrificed 1, 3, 7, 30 or 90 days after surgery. Blood gas values, lung compliance, surfactant conversion, capillary permeability, and the presence of MMP-2 and MMP-9 in broncho-alveolar-lavage flui

Outcome of paediatric intensive care survivors

The development of paediatric intensive care has contributed to the improved survival of critically ill children. Physical and psychological sequelae and consequences for quality of life (QoL) in survivors might be significant, as has been determined in adult intensive care unit (ICU) survivors. Awareness of sequelae due to the original illness and its treatment may result in changes in treatment and support during and after the acute phase. To determine the current knowledge on physical and psychological sequelae and the quality of life in survivors of paediatric intensive care, we undertook a computerised comprehensive search of online databases for studies reporting sequelae in survivors of paediatric intensive care. Studies reporting sequelae in paediatric survivors of cardiothoracic surgery and trauma were excluded, as were studies reporting only mortality. All other studies reporting aspects of physical and psychological sequelae were analysed. Twenty-seven studies consisting of 3,444 survivors met the selection criteria. Distinct physical and psychological sequelae in patients have been determined and seemed to interfere with quality of life. Psychological sequelae in parents seem to be common. Small numbers, methodological limitations and quantitative and qualitative heterogeneity hamper the interpretation of data. We conclude that paediatric intensive care survivors and their parents have physical and psychological sequelae affecting quality of life. Further well-designed prospective studies evaluating sequelae of the original illness and its treatment are warranted

The Glasgow Outcome Scale -- 40 years of application and refinement

The Glasgow Outcome Scale (GOS) was first published in 1975 by Bryan Jennett and Michael Bond. With over 4,000 citations to the original paper, it is the most highly cited outcome measure in studies of brain injury and the second most-cited paper in clinical neurosurgery. The original GOS and the subsequently developed extended GOS (GOSE) are recommended by several national bodies as the outcome measure for major trauma and for head injury. The enduring appeal of the GOS is linked to its simplicity, short administration time, reliability and validity, stability, flexibility of administration (face-to-face, over the telephone and by post), cost-free availability and ease of access. These benefits apply to other derivatives of the scale, including the Glasgow Outcome at Discharge Scale (GODS) and the GOS paediatric revision. The GOS was devised to provide an overview of outcome and to focus on social recovery. Since the initial development of the GOS, there has been an increasing focus on the multidimensional nature of outcome after head injury. This Review charts the development of the GOS, its refinement and usage over the past 40 years, and considers its current and future roles in developing an understanding of brain injury

Multiscale Simulations Suggest a Mechanism for the Association of the Dok7 PH Domain with PIP-Containing Membranes

Dok7 is a peripheral membrane protein that is associated with the MuSK receptor tyrosine kinase. Formation of the Dok7/MuSK/membrane complex is required for the activation of MuSK. This is a key step in the complex exchange of signals between neuron and muscle, which lead to neuromuscular junction formation, dysfunction of which is associated with congenital myasthenic syndromes. The Dok7 structure consists of a Pleckstrin Homology (PH) domain and a Phosphotyrosine Binding (PTB) domain. The mechanism of the Dok7 association with the membrane remains largely unknown. Using multi-scale molecular dynamics simulations we have explored the formation of the Dok7 PH/membrane complex. Our simulations indicate that the PH domain of Dok7 associates with membranes containing phosphatidylinositol phosphates (PIPs) via interactions of the β1/β2, β3/β4, and β5/β6 loops, which together form a positively charged surface on the PH domain and interact with the negatively charged headgroups of PIP molecules. The initial encounter of the Dok7 PH domain is followed by formation of additional interactions with the lipid bilayer, and especially with PIP molecules, which stabilizes the Dok7 PH/membrane complex. We have quantified the binding of the PH domain to the model bilayers by calculating a density landscape for protein/membrane interactions. Detailed analysis of the PH/PIP interactions reveal both a canonical and an atypical site to be occupied by the anionic lipid. PH domain binding leads to local clustering of PIP molecules in the bilayer. Association of the Dok7 PH domain with PIP lipids is therefore seen as a key step in localization of Dok7 to the membrane and formation of a complex with MuSK

Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli

Long-term associative learning predicts verbal short-term memory performance

Studies using tests such as digit span and nonword repetition have implicated short-term memory across a range of developmental domains. Such tests ostensibly assess specialized processes for the short-term manipulation and maintenance of information that are often argued to enable long-term learning. However, there is considerable evidence for an influence of long-term linguistic learning on performance in short-term memory tasks that brings into question the role of a specialized short-term memory system separate from long-term knowledge. Using natural language corpora, we show experimentally and computationally that performance on three widely used measures of short-term memory (digit span, nonword repetition, and sentence recall) can be predicted from simple associative learning operating on the linguistic environment to which a typical child may have been exposed. The findings support the broad view that short-term verbal memory performance reflects the application of long-term language knowledge to the experimental setting

Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back

The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons