Development of an auxiliary system for the execution of vascular catheter interventions with a reduced radiological risk; system description and first experimental results

Vascular catheterization is a common procedure in clinical medicine. It is normally performed by a specialist using an X-ray fluoroscopic guide and contrast-media. In the present paper, an image-guided navigation system which indicates a path providing guidance to the desired target inside the vascular tree is described with the aim of reducing the exposure of personnel and patients to X-rays during the catheterization procedure. A 3D model of the patient vascular tree, reconstructed with data collected by an angiography before starting the intervention, is used as a guide map instead of fluoroscopic scans. An accurate spatial correspondence between the body of the patient and the 3D reconstructed vascular model is established and, by means of a position indicator installed over the catheter tip, the real-time position/orientation of the tip is indicated correctly. This paper describes the system and the operational procedures necessary to use the proposed method efficiently during a catheter intervention. Preliminary experimental results on a phantom are also reported.     

An expert system for managing medical appropriateness criteria based on computer algebra techniques

This article presents the development of an expert system for managing medical appropriateness criteria together with an outline of its theoretical foundations. Techniques borrowed from computer algebra (Gröbner bases) are applied to this field of medicine.

The steps of the expert system construction process are as follows. First, the knowledge provided in table format by experts in coronary diseases is translated into a set of production rules of a rule-based expert system (RBES). Kleene's three-valued logic augmented with modal operators is chosen in order to manage uncertainty.     

Unimodality,Independence Lead to NP-Hardness of Interval Probability Problems

In many real-life situations, we only have partial information about probabilities. This information is usually described by bounds on moments, on probabilities of certain events, etc. –i.e., by characteristics c(p) which are linear in terms of the unknown probabilities p _j. If we know interval bounds on some such characteristics c="/content/3820066j1568u532/11155_2006_9031_Article_IEq1.gif" alt="$$ \underline{a}_i\leq c_i(p)\leq \bar{a}_i $$" align="middle" border="0">, and we are interested in a characteristic c(p), then we can find the bounds on c(p) by solving a linear programming problem. In some situations, we also have additional conditions on the probability distribution –e.g., we may know that the two variables x ₁ and x ₂ are independent, or that the joint distribution of x ₁ and x ₂ is unimodal. We show that adding each of these conditions makes the corresponding interval probability problem NP-hard.     

Three dimensional stereolithography models of cancellous bone structures from muCT data: testing and validation of finite element results

Stereolithography (STL) models of complex cancellous bone structures have been produced from three-dimensional micro-computed tomography data sets of human cancellous bone histological samples from four skeletal sites. The STL models have been mechanically tested and the derived stiffness compared with that predicted by finite element analysis. The results show a strong correlation (R2 = 0.941) between the predicted and calculated stiffnesses of the structures and show promise for the use of STL as an additional technique to complement the use of finite element models, for the assessment of the mechanical properties of complex cancellous bone structures.     

Memory capacity for sequences in a recurrent network with biological constraints

The CA3 region of the hippocampus is a recurrent neural network that is essential for the storage and replay of sequences of patterns that represent behavioral events. Here we present a theoretical framework to calculate a sparsely connected network's capacity to store such sequences. As in CA3, only a limited subset of neurons in the network is active at any one time, pattern retrieval is subject to error, and the resources for plasticity are limited. Our analysis combines an analytical mean field approach, stochastic dynamics, and cellular simulations of a time-discrete McCulloch-Pitts network with binary synapses. To maximize the number of sequences that can be stored in the network, we concurrently optimize the number of active neurons, that is, pattern size, and the firing threshold. We find that for one-step associations (i.e., minimal sequences), the optimal pattern size is inversely proportional to the mean connectivity c, whereas the optimal firing threshold is independent of the connectivity. If the number of synapses per neuron is fixed, the maximum number P of stored sequences in a sufficiently large, nonmodular network is independent of its number N of cells. On the other hand, if the number of synapses scales as the network size to the power of 3/2, the number of sequences P is proportional to N. In other words, sequential memory is scalable. Furthermore, we find that there is an optimal ratio r between silent and nonsilent synapses at which the storage capacity alpha = P//[c(1 + r)N] assumes a maximum. For long sequences, the capacity of sequential memory is about one order of magnitude below the capacity for minimal sequences, but otherwise behaves similar to the case of minimal sequences. In a biologically inspired scenario, the information content per synapse is far below theoretical optimality, suggesting that the brain trades off error tolerance against information content in encoding sequential memories.     

The importance of different timings of excitatory and inhibitory pathways in neural field models

In this paper, we consider a neural field model comprised of two distinct populations of neurons, excitatory and inhibitory, for which both the velocities of action potential propagation and the time courses of synaptic processing are different. Using recently-developed techniques, we construct the Evans function characterising the stability of both stationary and travelling wave solutions, under the assumption that the firing rate function is the Heaviside step. We find that these differences in timing for the two populations can cause instabilities of these solutions, leading to, for example, stationary breathers. We also analyse "anti-pulses", a novel type of pattern for which all but a small interval of the domain (in moving coordinates) is active. These results extend previous work on neural fields with space-dependent delays, and demonstrate the importance of considering the effects of the different time-courses of excitatory and inhibitory neural activity.     

Avoidance of bird repellents by mice (Mus musculus)

It is believed that mammalian chemosensory irritants are not aversive to birds and vice versa. Nevertheless, few avian repellents have been tested against mammals. For that reason, we evaluated the efficacy of 1.0% w/v methyl anthranilate, orthoaminoacetophenone, 2-amino-4c="/content/l243811m6354041q/xxlarge8242.gif" alt="prime" align="BASELINE" BORDER="0">,5c="/content/l243811m6354041q/xxlarge8242.gif" alt="prime" align="BASELINE" BORDER="0">-methoxyacetophenone, 2-methoxyacetophenone, and veratryl amine as mouse repellents in 3-hr no-choice drinking tests. Relative to ingestion of plain water, all test substances significantly reduced (P < 0.05) intake. Orthoaminoaceto-phenone was the most effective repellent, with intake reduced to levels statistically indistinguishable from zero.