Evidence Based Development of a Novel Lateral Fibula Plate (VariAx Fibula) Using a Real CT Bone Data Based Optimization Process During Device Development

Development of novel implants in orthopaedic trauma surgery is based on limited datasets of cadaver trials or artificial bone models. A method has been developed whereby implants can be constructed in an evidence based method founded on a large anatomic database consisting of more than 2.000 datasets of bones extracted from CT scans. The aim of this study was the development and clinical application of an anatomically pre-contoured plate for the treatment of distal fibular fractures based on the anatomical database. 48 Caucasian and Asian bone models (left and right) from the database were used for the preliminary optimization process and validation of the fibula plate. The implant was constructed to fit bilaterally in a lateral position of the fibula. Then a biomechanical comparison of the designed implant to the current gold standard in the treatment of distal fibular fractures (locking 1/3 tubular plate) was conducted. Finally, a clinical surveillance study to evaluate the grade of implant fit achieved was performed. The results showed that with a virtual anatomic database it was possible to design a fibula plate with an optimized fit for a large proportion of the population. Biomechanical testing showed the novel fibula plate to be superior to 1/3 tubular plates in 4-point bending tests. The clinical application showed a very high degree of primary implant fit. Only in a small minority of cases further intra-operative implant bending was necessary. Therefore, the goal to develop an implant for the treatment of distal fibular fractures based on the evidence of a large anatomical database could be attained. Biomechanical testing showed good results regarding the stability and the clinical application confirmed the high grade of anatomical fit.

Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: an Artificial Neural Network perspective

Clinical studies indicate that exaggerated postprandial lipemia is linked to the progression of atherosclerosis, leading cause of Cardiovascular Diseases (CVD). CVD is a multi-factorial disease with complex etiology and according to the literature postprandial Triglycerides (TG) can be used as an independent CVD risk factor. Aim of the current study is to construct an Artificial Neural Network (ANN) based system for the identification of the most important gene-gene and/or gene-environmental interactions that contribute to a fast or slow postprandial metabolism of TG in blood and consequently to investigate the causality of postprandial TG response. The design and development of the system is based on a dataset of 213 subjects who underwent a two meals fatty prandial protocol. For each of the subjects a total of 30 input variables corresponding to genetic variations, sex, age and fasting levels of clinical measurements were known. Those variables provide input to the system, which is based on the combined use of Parameter Decreasing Method (PDM) and an ANN. The system was able to identify the ten (10) most informative variables and achieve a mean accuracy equal to 85.21%.

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

Comparison of immortalized bEnd5 and primary mouse brain microvascular endothelial cells as in vitro blood-brain barrier models for the study of T cell extravasation

Important insights into the molecular mechanism of T cell extravasation across the blood-brain barrier (BBB) have already been obtained using immortalized mouse brain endothelioma cell lines (bEnd). However, compared with bEnd, primary brain endothelial cells have been shown to establish better barrier characteristics, including complex tight junctions and low permeability. In this study, we asked whether bEnd5 and primary mouse brain microvascular endothelial cells (pMBMECs) were equally suited as in vitro models with which to study the cellular and molecular mechanisms of T cell extravasation across the BBB. We found that both in vitro BBB models equally supported both T cell adhesion under static and physiologic flow conditions, and T cell crawling on the endothelial surface against the direction of flow. In contrast, distances of T cell crawling on pMBMECs were strikingly longer than on bEnd5, whereas diapedesis of T cells across pMBMECs was dramatically reduced compared with bEnd5. Thus, both in vitro BBB models are suited to study T cell adhesion. However, because pMBMECs better reflect endothelial BBB specialization in vivo, we propose that more reliable information about the cellular and molecular mechanisms of T cell diapedesis across the BBB can be attained using pMBMECs.

E-Health towards ecumenical framework for personalized medicine via Decision Support System

The purpose of the present manuscript is to present the advances performed in medicine using a Personalized Decision Support System (PDSS). The models used in Decision Support Systems (DSS) are examined in combination with Genome Information and Biomarkers to produce personalized result for each individual. The concept of personalize medicine is described in depth and application of PDSS for Cardiovascular Diseases (CVD) and Type-1 Diabetes Mellitus (T1DM) are analyzed. Parameters extracted from genes, biomarkers, nutrition habits, lifestyle and biological measurements feed DSSs, incorporating Artificial Intelligence Modules (AIM), to provide personalized advice, medication and treatment.

Methodology and models in erosion research: discussion and conclusions

This paper summarises the discussions which took place at the Workshop on Methodology in Erosion Research in Zürich, 2010, and aims, where possible, to offer guidance for the development and application of both in vitro and in situ models for erosion research. The prospects for clinical trials are also discussed. All models in erosion research require a number of choices regarding experimental conditions, study design and measurement techniques, and these general aspects are discussed first. Among in vitro models, simple (single- or multiple-exposure) models can be used for screening products regarding their erosive potential, while more elaborate pH cycling models can be used to simulate erosion in vivo. However, in vitro models provide limited information on intra-oral erosion. In situ models allow the effect of an erosive challenge to be evaluated under intra-oral conditions and are currently the method of choice for short-term testing of low-erosive products or preventive therapeutic products. In the future, clinical trials will allow longer-term testing. Possible methodologies for such trials are discussed.

Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients

Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence techniques are presented.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

Echinococcus metacestodes as laboratory models for the screening of drugs against cestodes and trematodes

Among the cestodes, Echinococcus granulosus, Echinococcus multilocularis and Taenia solium represent the most dangerous parasites. Their larval stages cause the diseases cystic echinococcosis (CE), alveolar echinococcosis (AE) and cysticercosis, respectively, which exhibit considerable medical and veterinary health concerns with a profound economic impact. Others caused by other cestodes, such as species of the genera Mesocestoides and Hymenolepis, are relatively rare in humans. In this review, we will focus on E. granulosus and E. multilocularis metacestode laboratory models and will review the use of these models in the search for novel drugs that could be employed for chemotherapeutic treatment of echinococcosis. Clearly, improved therapeutic drugs are needed for the treatment of AE and CE, and this can only be achieved through the development of medium-to-high throughput screening approaches. The most recent achievements in the in vitro culture and genetic manipulation of E. multilocularis cells and metacestodes, and the accessability of the E. multilocularis genome and EST sequence information, have rendered the E. multilocularis model uniquely suited for studies on drug-efficacy and drug target identification. This could lead to the development of novel compounds for the use in chemotherapy against echinococcosis, and possibly against diseases caused by other cestodes, and potentially also trematodes.

Simple beam models for Monte Carlo photon beam dose calculations in radiotherapy

Monte Carlo (code GEANT) produced 6 and 15 MV phase space (PS) data were used to define several simple photon beam models. For creating the PS data the energy of starting electrons hitting the target was tuned to get correct depth dose data compared to measurements. The modeling process used the full PS information within the geometrical boundaries of the beam including all scattered radiation of the accelerator head. Scattered radiation outside the boundaries was neglected. Photons and electrons were assumed to be radiated from point sources. Four different models were investigated which involved different ways to determine the energies and locations of beam particles in the output plane. Depth dose curves, profiles, and relative output factors were calculated with these models for six field sizes from 5x5 to 40x40cm2 and compared to measurements. Model 1 uses a photon energy spectrum independent of location in the PS plane and a constant photon fluence in this plane. Model 2 takes into account the spatial particle fluence distribution in the PS plane. A constant fluence is used again in model 3, but the photon energy spectrum depends upon the off axis position. Model 4, finally uses the spatial particle fluence distribution and off axis dependent photon energy spectra in the PS plane. Depth dose curves and profiles for field sizes up to 10x10cm2 were not model sensitive. Good agreement between measured and calculated depth dose curves and profiles for all field sizes was reached for model 4. However, increasing deviations were found for increasing field sizes for models 1-3. Large deviations resulted for the profiles of models 2 and 3. This is due to the fact that these models overestimate and underestimate the energy fluence at large off axis distances. Relative output factors consistent with measurements resulted only for model 4.

Human experimental pain models in drug development: translational pain research

Human experimental pain models require standardized stimulation and quantitative assessment of the evoked responses. This approach can be applied to healthy volunteers and pain patients before and after pharmacological interventions. Standardized stimuli of different modalities (ie, mechanical, chemical, thermal or electrical) can be applied to the skin, muscles and viscera for a differentiated and comprehensive assessment of various pain pathways and mechanisms. Using a multi-modal, multi-tissue approach, new and existing analgesic drugs can be profiled by their modulation of specific biomarkers. It has been shown that biomarkers, for example, those related to the central integration of repetitive nociceptive stimuli, can predict efficacy of a given drug in neuropathic pain conditions. Human experimental pain models can bridge animal and clinical pain research, and act as translational research providing new possibilities for designing successful clinical trials. Proof-of-concept studies provide cheap, fast and reliable information on dose-efficacy relationships and how pain sensed in the skin, muscles and viscera are inhibited.