Release of insulin from PLGA-alginate dressing stimulates regenerative healing of burn wounds in rats

Burn wound healing involves a complex set of overlapping processes in an environment conducive to ischemia, inflammation, and infection costing $7.5 billion/year in the US alone, in addition to the morbidity and mortality that occur when the burns are extensive. We previously showed that insulin, when topically applied to skin excision wounds, accelerates re-epithelialization, and stimulates angiogenesis. More recently, we developed an alginate sponge dressing (ASD) containing insulin encapsulated in PLGA microparticles that provides a sustained release of bioactive insulin for >20days in a moist and protective environment. We hypothesized that insulin-containing ASD accelerates burn healing and stimulates a more regenerative, less scarring, healing. Using a heat-induced burn injury in rats, we show that burns treated with dressings containing 0.04mg insulin/cm2, every three days for 9 days, have faster closure, faster rate of disintegration of dead tissue, and decreased oxidative stress.In addition, in insulin-treated wounds the pattern of neutrophil inflammatory response suggests faster clearing of the burn dead tissue. We also observe faster resolution of the pro-inflammatory macrophages. We also found that insulin stimulates collagen deposition and maturation with the fibers organized more like a basket weave (normal skin) than aligned and crosslinked (scar tissue). In summary , application of ASD-containing insulin-loaded PLGA particles on burns every three days stimulates faster and more regenerative healing. These results suggest insulin as a potential therapeutic agent in burn healing and, because of its long history of safe use in humans, insulin could become one of the treatments of choice when repair and regeneration are critical for proper tissue function.

Lightweight concrete masonry units based on processed granulate of corn cob as aggregate

A research work was performed in order to assess the potential application of processed granulate of corn cob (PCC) as an alternative lightweight aggregate for the manufacturing process of lightweight concrete masonry units (CMU). Therefore, CMU-PCC were prepared in a factory using a typical lightweight concrete mixture for non-structural purposes. Additionally, lightweight concrete masonry units based on a currently applied lightweight aggregate such as expanded clay (CMU-EC) were also manufactured. An experimental work allowed achieving a set of results that suggest that the proposed building product presents interesting material properties within the masonry wall context. Therefore, this unit is promising for both interior and exterior applications. This conclusion is even more relevant considering that corn cob is an agricultural waste product.

A study on the performance of technology transfer units

Universities are increasingly institutionalizing activities related to technology transfer and one of the main institutional mechanisms that has emerged is the “technology transfer unit” (TTU). Many of them are focusing their activities on the management of the university intellectual property. Studies have investigated factors that seem to affect their performance, but few have looked in detail at internal procedures and techniques that are used in their processes related to technology evaluation and licensing. The aim of this paper is to provide a comprehensive overview of some of the several steps that comprises the processes regarding technology evaluation and licensing, providing an analysis of the critical issues that affect each step of the process. A review of the literature was made, complemented with interviews to seven university TTUs, which was used as a check and a complement to the literature review and as way of perceiving from an insider perspective, the problems and issues that this paper wants to emphasize and to state clearly.

Cost-effective and lightweight mobile units for MixAR: A comparative trial among different setups

Cultural heritage has arousing the interest of the general public (e.g. tourists), resulting in the increasing number of visitations to archaeological sites. However, many buildings and monuments are severely damaged or completely destroyed, which doesn’t allow to get a full experience of “travelling in time”. Over the years, several Augmented Reality (AR) approaches were proposed to overcome these issues by providing three-dimensional visualization of reconstructed ancient structures in situ. However, most of these systems were made available through heavy and expensive technological bundles. Alternatively, MixAR intends to be a lightweight and cost-effective Mixed Reality system which aims to provide the visualization of virtual ancient buildings reconstructions in situ, properly superimposed and aligned with real-world ruins. This paper proposes and compares different AR mobile units setups to be used in the MixAR system, with low-cost and lightweight requirements in mind, providing different levels of immersion. It was propounded four different mobile units, based on: a laptop computer, a single-board computer (SBC), a tablet and a smartphone, which underwent a set of tests to evaluate their performances. The results show that mobile units based on laptop computer and SBC reached a good overall performance while mobile units based on tablet and smartphone did not meet such a satisfactory result even though they are acceptable for the intended use.

Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Improved burn wound healing using a bioactive peptide

[Excerpt] Antimicrobial peptides (AMPs) are good candidates to treat burn wounds, a major cause of morbidity, impaired life quality and resources consumption in developed countries. We took advantage of a commercially available hydrogel, Carbopol, a vehicle for topical administration that maintains a moist environment within the wound site. We hypothesized that the incorporation of LLKKK18 conjugated to dextrin would improve the healing process in rat burns. Whereas the hydrogel improves healing, LLKKK18 released from the dextrin conjugates further accelerates wound closure, and simultaneously improving the quality of healing. Indeed, the release of LLKKK18 reduces oxidative stress and inflammation (low neutrophil and macrophage infiltration and pro-inflammatory cytokines levels). Importantly, it induced a faster resolution of the inflammatory stage through early M2 macrophage recruitment. In addition, LLKKK18 stimulates angiogenesis (increased VEGF and microvessel development in vivo), potentially contributing to more effective transport of nutrients and cytokines. Moreover, collagen staining evaluated by Masson’s Trichrome was visually much more intense after treatment with LLKKK18, suggesting higher collagen deposition. (...)

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.