Design of a semi-implantable hearing device for direct acoustic cochlear stimulation

A new hearing therapy based on direct acoustic cochlear stimulation was developed for the treatment of severe to profound mixed hearing loss. The device efficacy was validated in an initial clinical trial with four patients. This semi-implantable investigational device consists of an externally worn audio processor, a percutaneous connector, and an implantable microactuator. The actuator is placed in the mastoid bone, right behind the external auditory canal. It generates vibrations that are directly coupled to the inner ear fluids and that, therefore, bypass the external and the middle ear. The system is able to provide an equivalent sound pressure level of 125 dB over the frequency range between 125 and 8000 Hz. The hermetically sealed actuator is designed to provide maximal output power by keeping its dimensions small enough to enable implantation. A network model is used to simulate the dynamic characteristics of the actuator to adjust its transfer function to the characteristics of the middle ear. The geometry of the different actuator components is optimized using finite-element modeling.

Enumerating constrained elementary flux vectors of metabolic networks

The concept of elementary vector is generalised to the case where the steady-state space of the metabolic network is not a flux cone but is a general polyhedron due to further inhomogeneous constraints on the flows through some of the reactions. On one hand, this allows to selectively enumerate elementary modes which satisfy certain optimality criteria and this can yield a large computational gain compared with full enumeration. On the other hand, in contrast to the single optimum found by executing a linear program, this enables a comprehensive description of the set of alternate optima often encountered in flux balance analysis. The concepts are illustrated on a metabolic network model of human cardiac mitochondria.

Nerve Injury-Induced Neuropathic Pain Causes Disinhibition of the Anterior Cingulate Cortex

Neuropathic pain caused by peripheral nerve injury is a debilitating neurological condition of high clinical relevance. On the cellular level, the elevated pain sensitivity is induced by plasticity of neuronal function along the pain pathway. Changes in cortical areas involved in pain processing contribute to the development of neuropathic pain. Yet, it remains elusive which plasticity mechanisms occur in cortical circuits. We investigated the properties of neural networks in the anterior cingulate cortex (ACC), a brain region mediating affective responses to noxious stimuli. We performed multiple whole-cell recordings from neurons in layer 5 (L5) of the ACC of adult mice after chronic constriction injury of the sciatic nerve of the left hindpaw and observed a striking loss of connections between excitatory and inhibitory neurons in both directions. In contrast, no significant changes in synaptic efficacy in the remaining connected pairs were found. These changes were reflected on the network level by a decrease in the mEPSC and mIPSC frequency. Additionally, nerve injury resulted in a potentiation of the intrinsic excitability of pyramidal neurons, whereas the cellular properties of interneurons were unchanged. Our set of experimental parameters allowed constructing a neuronal network model of L5 in the ACC, revealing that the modification of inhibitory connectivity had the most profound effect on increased network activity. Thus, our combined experimental and modeling approach suggests that cortical disinhibition is a fundamental pathological modification associated with peripheral nerve damage. These changes at the cortical network level might therefore contribute to the neuropathic pain condition.

Macrophages and dendritic cells express tight junction proteins and exchange particles in an in vitro model of the human airway wall

The human airway epithelium serves as structural and functional barrier against inhaled particulate antigen. Previously, we demonstrated in an in vitro epithelial barrier model that monocyte derived dendritic cells (MDDC) and monocyte derived macrophages (MDM) take up particulate antigen by building a trans-epithelial interacting network. Although the epithelial tight junction (TJ) belt was penetrated by processes of MDDC and MDM, the integrity of the epithelium was not affected. These results brought up two main questions: (1) Do MDM and MDDC exchange particles? (2) Are those cells expressing TJ proteins, which are believed to interact with the TJ belt of the epithelium to preserve the epithelial integrity? The expression of TJ and adherens junction (AJ) mRNA and proteins in MDM and MDDC monocultures was determined by RT-PCR, and immunofluorescence, respectively. Particle uptake and exchange was quantified by flow cytometry and laser scanning microscopy in co-cultures of MDM and MDDC exposed to polystyrene particles (1 μm in diameter). MDM and MDDC constantly expressed TJ and AJ mRNA and proteins. Flow cytometry analysis of MDM and MDDC co-cultures showed increased particle uptake in MDDC while MDM lost particles over time. Quantitative analysis revealed significantly higher particle uptake by MDDC in co-cultures of epithelial cells with MDM and MDDC present, compared to co-cultures containing only epithelial cells and MDDC. We conclude from these findings that MDM and MDDC express TJ and AJ proteins which could help to preserve the epithelial integrity during particle uptake and exchange across the lung epithelium.

Effects of glucosamine, chondroitin, or placebo in patients with osteoarthritis of hip or knee: network meta-analysis

OBJECTIVE: To determine the effect of glucosamine, chondroitin, or the two in combination on joint pain and on radiological progression of disease in osteoarthritis of the hip or knee. Design Network meta-analysis. Direct comparisons within trials were combined with indirect evidence from other trials by using a Bayesian model that allowed the synthesis of multiple time points. MAIN OUTCOME MEASURE: Pain intensity. Secondary outcome was change in minimal width of joint space. The minimal clinically important difference between preparations and placebo was prespecified at -0.9 cm on a 10 cm visual analogue scale. DATA SOURCES: Electronic databases and conference proceedings from inception to June 2009, expert contact, relevant websites. Eligibility criteria for selecting studies Large scale randomised controlled trials in more than 200 patients with osteoarthritis of the knee or hip that compared glucosamine, chondroitin, or their combination with placebo or head to head. Results 10 trials in 3803 patients were included. On a 10 cm visual analogue scale the overall difference in pain intensity compared with placebo was -0.4 cm (95% credible interval -0.7 to -0.1 cm) for glucosamine, -0.3 cm (-0.7 to 0.0 cm) for chondroitin, and -0.5 cm (-0.9 to 0.0 cm) for the combination. For none of the estimates did the 95% credible intervals cross the boundary of the minimal clinically important difference. Industry independent trials showed smaller effects than commercially funded trials (P=0.02 for interaction). The differences in changes in minimal width of joint space were all minute, with 95% credible intervals overlapping zero. Conclusions Compared with placebo, glucosamine, chondroitin, and their combination do not reduce joint pain or have an impact on narrowing of joint space. Health authorities and health insurers should not cover the costs of these preparations, and new prescriptions to patients who have not received treatment should be discouraged.

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

Recurrent antitopographic inhibition mediates competitive stimulus selection in an attention network

Topographically organized neurons represent multiple stimuli within complex visual scenes and compete for subsequent processing in higher visual centers. The underlying neural mechanisms of this process have long been elusive. We investigate an experimentally constrained model of a midbrain structure: the optic tectum and the reciprocally connected nucleus isthmi. We show that a recurrent antitopographic inhibition mediates the competitive stimulus selection between distant sensory inputs in this visual pathway. This recurrent antitopographic inhibition is fundamentally different from surround inhibition in that it projects on all locations of its input layer, except to the locus from which it receives input. At a larger scale, the model shows how a focal top-down input from a forebrain region, the arcopallial gaze field, biases the competitive stimulus selection via the combined activation of a local excitation and the recurrent antitopographic inhibition. Our findings reveal circuit mechanisms of competitive stimulus selection and should motivate a search for anatomical implementations of these mechanisms in a range of vertebrate attentional systems.

An Urn Model for Cascading Failures on a Lattice

A cascading failure is a failure in a system of interconnected parts, in which the breakdown of one element can lead to the subsequent collapse of the others. The aim of this paper is to introduce a simple combinatorial model for the study of cascading failures. In particular, having in mind particle systems and Markov random fields, we take into consideration a network of interacting urns displaced over a lattice. Every urn is Pólya-like and its reinforcement matrix is not only a function of time (time contagion) but also of the behavior of the neighboring urns (spatial contagion), and of a random component, which can represent either simple fate or the impact of exogenous factors. In this way a non-trivial dependence structure among the urns is built, and it is used to study default avalanches over the lattice. Thanks to its flexibility and its interesting probabilistic properties, the given construction may be used to model different phenomena characterized by cascading failures such as power grids and financial networks.

Cortical reorganization after brain damage: the oculomotor model

Recovery from eye movement deficits after cortical lesions is amazingly rapid and almost complete, which is in sharp contrast to most other neurological deficits of cerebral lesions. The underlying mechanisms of this successful recovery remain uncertain. We had the rare opportunity to examine two patients with recovery from saccade deficits after a lesion restricted to the frontal eye field (FEF) by means of transcranial magnetic stimulation (TMS). The results provide direct evidence that recovery depended on the integrity of the oculomotor regions of the nonlesioned contralesional hemisphere, and that the compensatory network is task-specific.

Enhancing sealing of fetal membrane defects using tissue engineered native amniotic scaffolds in the rabbit model

OBJECTIVE: The purpose of this study was to compare the efficacy of native engineered amniotic scaffolds (AS) and polyesterurethane scaffolds (DegraPol) and document wound healing response when sealing iatrogenic fetal membrane defects in the rabbit model. STUDY DESIGN: Native AS were engineered from freshly harvested membranes of 23 days' gestational age (GA; term = 31-2 d). Acellularity of AS was assessed by histology, light and scanning electron microscopy. Fetal membrane defects were created by 14 gauge-needle puncture at GA 23 days and primarily closed with AS (n = 10) or DegraPol (n = 10) or left unclosed (positive controls; n = 10). Sixty-one sacs served as negative controls. At GA 30 days a second look hysterotomy was performed to assess presence of amniotic fluid (AF) and harvest plugging sites for microscopic evaluation. RESULTS: Engineered AS had a cell-free collagenous fiber network. AF was significantly higher only in the DegraPol group (78%; P < .05) compared to the AF in positive controls (17%). Integration of plugs in the fetal membrane defect was better with AS than DegraPol, with higher reepithelialization rates (AS: 52.5% +/- 6.5%; DegraPol: 11.6% +/- 2.6%; P < .001) and proliferation indices (AS: 0.47 +/- 0.03; DegraPol: 0.28 +/- 0.04; P = .001). In both treatment groups, cell proliferation in the myometrium was increased (P < .05). CONCLUSION: Native AS seal iatrogenic fetal membrane defects better than DegraPol. Within a week, there is abundant reepithelilization and minimal local inflammation. This yields the proof of principle that engineered native, amniotic membrane scaffolds enhance fetal membrane wound healing response.

Predicting asthma control and exacerbations: chronic asthma as a complex dynamic model

PURPOSE OF REVIEW: Predicting asthma episodes is notoriously difficult but has potentially significant consequences for the individual, as well as for healthcare services. The purpose of this review is to describe recent insights into the prediction of acute asthma episodes in relation to classical clinical, functional or inflammatory variables, as well as present a new concept for evaluating asthma as a dynamically regulated homeokinetic system. RECENT FINDINGS: Risk prediction for asthma episodes or relapse has been attempted using clinical scoring systems, considerations of environmental factors and lung function, as well as inflammatory and immunological markers in induced sputum or exhaled air, and these are summarized here. We have recently proposed that newer mathematical methods derived from statistical physics may be used to understand the complexity of asthma as a homeokinetic, dynamic system consisting of a network comprising multiple components, and also to assess the risk for future asthma episodes based on fluctuation analysis of long time series of lung function. SUMMARY: Apart from the classical analysis of risk factor and functional parameters, this new approach may be used to assess asthma control and treatment effects in the individual as well as in future research trials.

Learning real-world stimuli in a neural network with spike-driven synaptic dynamics

We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).

Dendritic cells and macrophages form a transepithelial network against foreign particulate antigens

Fine particles (0.1-2.5 microm in diameter) may cause increased pulmonary morbidity and mortality. We demonstrate with a cell culture model of the human epithelial airway wall that dendritic cells extend processes between epithelial cells through the tight junctions to collect particles in the "luminal space" and to transport them through cytoplasmic processes between epithelial cells across the epithelium or to transmigrate through the epithelium to take up particles on the epithelial surface. Furthermore, dendritic cells interacted with particle-loaded macrophages on top of the epithelium and with other dendritic cells within or beneath the epithelium to take over particles. By comparing the cellular interplay of dendritic cells and macrophages across epithelial monolayers of different transepithelial electrical resistance, we found that more dendritic cells were involved in particle uptake in A549 cultures showing a low transepithelial electrical resistance compared with dendritic cells in16HBE14o cultures showing a high transepithelial electrical resistance 10 min (23.9% versus 9.5%) and 4 h (42.1% versus 14.6%) after particle exposition. In contrast, the macrophages in A549 co-cultures showed a significantly lower involvement in particle uptake compared with 16HBE14o co-cultures 10 min (12.8% versus 42.8%) and 4 h (57.4% versus 82.7%) after particle exposition. Hence we postulate that the epithelial integrity influences the particle uptake by dendritic cells, and that these two cell types collaborate as sentinels against foreign particulate antigen by building a transepithelial interacting cellular network.

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem.