Conservative surgical debridement as a burn treatment : supporting evidence from a porcine burn model

In thermal deep-dermal burns, surgical debridement is normally used in conjunction with skin grafting or skin substitutes and debridement alone as a burn treatment is not usually practiced. The current study addresses whether or not debridement alone would enhance burn wound healing on small deep-dermal-partial thickness burns. This was a prospective and blinded experimental trial using a porcine deep-dermal-partial thickness burn model. Four burns, approximately 50 cm(2) in size, were created on each of eight pigs. Two burns from each pig were immediately surgically debrided and the other two were not debrided as the internal control. Hydrate gel together with paraffin gauze were used to cover the burns for four pigs and silver dressings for the other four. Clinical assessment of wound healing was conducted over a 6-week period. Skin samples were collected at the end of the experiment and histopathological evaluation was performed. The results show thinner scar formation and lower scar height in the debrided compared with nondebrided wounds in the hydrate gel/paraffin gauze groups. There were no statistically significant differences in wound healing assessment between the debrided and nondebrided wounds dressed with silver dressings. This study provides supporting evidence that immediate debridement with an appropriate dressing and without skin grafting may promote wound healing, suggesting its potential benefit for clinical patients.

Silver absorption on burns after the application of Acticoat : data from pediatric patients and a porcine burn model

Silver dressings have been widely used to successfully prevent burn wound infection and sepsis. However, a few case studies have reported the functional abnormality and failure of vital organs, possibly caused by silver deposits. The aim of this study was to investigate the serum silver level in the pediatric burn population and also in several internal organs in a porcine burn model after the application of Acticoat. A total of 125 blood samples were collected from 46 pediatric burn patients. Thirty-six patients with a mean of 13.4% TBSA burns had a mean peak serum silver level of 114 microg/L, whereas 10 patients with a mean of 1.85% TBSA burns had an undetectable level of silver (<5.4 microg/L). Overall, serum silver levels were closely related to burn sizes. However, the highest serum silver was 735 microg/L in a 15-month-old toddler with 10% TBSA burns and the second highest was 367 microg/L in a 3-year old with 28% TBSA burns. In a porcine model with 2% TBSA burns, the mean peak silver level was 38 microg/L at 2 to 3 weeks after application of Acticoat and was then significantly reduced to an almost undetectable level at 6 weeks. Of a total of four pigs, silver was detected in all four livers (1.413 microg/g) and all four hearts (0.342 microg/g), three of four kidneys (1.113 microg/g), and two of four brains (0.402 microg/g). This result demonstrated that although variable, the level of serum silver was positively associated with the size of burns, and significant amounts of silver were deposited in internal organs in pigs with only 2% TBSA burns, after application of Acticoat.

Burn healing is dependent on burn site : a quantitative analysis from a porcine burn model

This retrospective review examines healing in different sites on a porcine burn model; 24 pairs of burns on 18 pigs from other animal trials were selected for analysis. Each pair of burns was located on the either the cranial or the caudal part of the thoracic ribs region, on the same side of the animal. The burns were 40-50 cm(2) in size and of uniform deep-dermal partial thickness. Caudal burns healed significantly better than cranial burns, demonstrated by earlier closure of wounds, less scar formation and better cosmesis. To our knowledge, this is the first detailed study reporting that burn healing is affected by location on a porcine burn model. We recommend that similar symmetrical burns should be used for future comparative assessments of burn healing.

A topic based document relevance ranking model

Topic modelling has been widely used in the fields of information retrieval, text mining, machine learning, etc. In this paper, we propose a novel model, Pattern Enhanced Topic Model (PETM), which makes improvements to topic modelling by semantically representing topics with discriminative patterns, and also makes innovative contributions to information filtering by utilising the proposed PETM to determine document relevance based on topics distribution and maximum matched patterns proposed in this paper. Extensive experiments are conducted to evaluate the effectiveness of PETM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models.

Isolation of human lymphatic malformation endothelial cells, their in vitro characterization and in vivo survival in a mouse xenograft model

Human lymphatic vascular malformations (LMs), also known as cystic hygromas or lymphangioma, consist of multiple lymphatic endothelial cell-lined lymph-containing cysts. No animal model of this disease exists. To develop a mouse xenograft model of human LM, CD34NegCD31Pos LM lymphatic endothelial cells (LM-LEC) were isolated from surgical specimens and compared to foreskin CD34NegCD31Pos lymphatic endothelial cells (LECs). Cells were implanted into a mouse tissue engineering model for 1, 2 and 4 weeks. In vitro LM-LECs showed increased proliferation and survival under starvation conditions (P < 0.0005 at 48 h, two-way ANOVA), increased migration (P < 0.001, two-way ANOVA) and formed fewer (P = 0.029, independent samples t test), shorter tubes (P = 0.029, independent samples t test) than foreskin LECs. In vivo LM-LECs implanted into a Matrigel™-containing mouse chamber model assembled to develop vessels with dilated cystic lumens lined with flat endothelium, morphology similar to that of clinical LMs. Human foreskin LECs failed to survive implantation. In LM-LEC implanted chambers the percent volume of podoplaninPos vessels was 1.18 ± 2.24 % at 1 week, 6.34 ± 2.68 % at 2 weeks and increasing to 7.67 ± 3.60 % at 4 weeks. In conclusion, the significantly increased proliferation, migration, resistance to apoptosis and decreased tubulogenesis of LM-LECs observed in vitro is likely to account for their survival and assembly into stable LM-like structures when implanted into a mouse vascularised chamber model. This in vivo xenograft model will provide the basis of future studies of LM biology and testing of potential pharmacological interventions for patients with lymphatic malformations.

Social engineering in social networking sites : affect-based model

While social engineering represents a real and ominous threat to many organizations, companies, governments, and individuals, social networking sites (SNSs), have been identified as among the most common means of social engineering attacks. Owing to factors that reduce the ability of users to detect social engineering tricks and increase the ability of attackers to launch them, SNSs seem to be perfect breeding ground for exploiting the vulnerabilities of people, and the weakest link in security. This work will contribute to the knowledge of social engineering by identifying different entities and subentities that affect social engineering based attacks in SNSs. Moreover, this paper includes an intensive and comprehensive overview of different aspects of social engineering threats in SNSs.

Hybrid model for motorway travel time estimation considering increased detector spacing

Travel time estimation and prediction on motorways has long been a topic of research. Prediction modeling generally assumes that the estimation is perfect. No matter how good is the prediction modeling- the errors in estimation can significantly deteriorate the accuracy and reliability of the prediction. Models have been proposed to estimate travel time from loop detector data. Generally, detectors are closely spaced (say 500m) and travel time can be estimated accurately. However, detectors are not always perfect, and even during normal running conditions few detectors malfunction, resulting in increase in the spacing between the functional detectors. Under such conditions, error in the travel time estimation is significantly large and generally unacceptable. This research evaluates the in-practice travel time estimation model during different traffic conditions. It is observed that the existing models fail to accurately estimate travel time during large detector spacing and congestion shoulder periods. Addressing this issue, an innovative Hybrid model that only considers loop data for travel time estimation is proposed. The model is tested using simulation and is validated with real Bluetooth data from Pacific Motorway Brisbane. Results indicate that during non free flow conditions and larger detector spacing Hybrid model provides significant improvement in the accuracy of travel time estimation.

The development of a carrying capacity assessment model for the Australian socio-environmental context

A key aim of this research was to highlight how society's understanding of constraints to the productive capacity of its resource base is vital to its long-term survival. This was achieved through the development of an online model, the Carrying Capacity Dashboard. The Dashboard was developed to estimate how much land Australian populations require for the production of their food, textiles, timber and liquid fuel. Findings reveal that Australia's estimated carrying capacity is currently over 40 million people but longer-term and more regional analyses suggest a much smaller number. Carrying capacity assessment also indicates that optimal resource security is to be found in balancing both small and large-scale self-sufficiency.

Model-predictive control and closed-loop stability considerations for nonlinear plants described by local ARX-type models

In this paper, a model-predictive control (MPC) method is detailed for the control of nonlinear systems with stability considerations. It will be assumed that the plant is described by a local input/output ARX-type model, with the control potentially included in the premise variables, which enables the control of systems that are nonlinear in both the state and control input. Additionally, for the case of set point regulation, a suboptimal controller is derived which has the dual purpose of ensuring stability and enabling finite-iteration termination of the iterative procedure used to solve the nonlinear optimization problem that is used to determine the control signal.

Embedding EfS in teacher education : an introductory guide to using the systems change model

Welcome to this introductory guide on using a systems change model to embed Education for Sustainability (EfS) into teacher education. Pressing sustainability issues such as climate change, biodiversity loss and depletion of non-renewable resources pose new challenges for education. The importance of education in preparing future citizens to engage in sustainable living practices and help create a more sustainable world is widely acknowledged. As a result many universities around the world are beginning to recognize the need to integrate EfS into their teacher education programs. However, evidence indicates that there is little or no core EfS knowledge or pedagogy in pre-service teacher courses available to student teachers in a thorough and systematic fashion. Instead efforts are fragmented and individually or, at best, institutionally-based and lacking a systems approach to change, an approach that is seen as essential to achieving a sustainable society (Henderson & Tilbury, 2004). The result is new teachers are graduating without the necessary knowledge or skills to teach in ways that enable them to prepare their students to cope well with the new and emerging challenges their communities face. This guide has been prepared as part of a teaching and learning research project that applied a systems change approach to embedding the learning and teaching of sustainability into pre-service teacher education. The processes, outcomes and lessons learnt from this project are presented here as a guide for navigating pathways to systemic change in the journey of re-orienting teacher education towards sustainability. The guide also highlights how a systems change approach can be used to successfully enact change within a teacher education system. If you are curious about how to introduce and embed EfS into teacher education – or have tried other models and are looking for a more encompassing, transformative approach – this guide is designed to help you. The material presented in this guide is designed to be flexible and adaptive. However you choose to use the content, our aim is to help you and your students develop new perspectives, promote discussion and to engage with a system-wide approach to change.

The mouse model of Chlamydia genital tract infection: a review of infection, disease, immunity and vaccine development

Chlamydia trachomatis is the most common sexually transmitted bacterial infection worldwide. The impact of this pathogen on human reproduction has intensified research efforts to better understand chlamydial infection and pathogenesis. Whilst there are animal models available that mimic the many aspects of human chlamydial infection, the mouse is regarded as the most practical and widely used of the models. Studies in mice have greatly contributed to our understanding of the host-pathogen interaction and provided an excellent medium for evaluating vaccines. Here we explore the advantages and disadvantages of all animal models of chlamydial genital tract infection, with a focus on the murine model and what we have learnt from it so far.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.