Stromal vascular fraction cell sheets angiogenic potential for tissue engineering and regenerative medicine applications

One of the biggest concerns in the Tissue Engineering field is the correct vascularization of engineered constructs. Strategies involving the use of endothelial cells are promising but adequate cell sourcing and neo-vessels stability are enduring challenges. In this work, we propose the hypoxic pre-conditioning of the stromal vascular fraction (SVF) of human adipose tissue to obtain highly angiogenic cell sheets (CS). For that, SVF was isolated after enzymatic dissociation of adipose tissue and cultured until CS formation in normoxic (pO2=21%) and hypoxic (pO2=5%) conditions for 5 and 8 days, in basal medium. Immunocytochemistry against CD31 and CD146 revealed the presence of highly branched capillary-like structures, which were far more complex for hypoxia. ELISA quantification showed increased VEGF and TIMP-1 secretion in hypoxia for 8 days of culture. In a Matrigel assay, the formation of capillary-like structures by endothelial cells was more prominent when cultured in conditioned medium recovered from the cultures in hypoxia. The same conditioned medium increased the migration of adipose stromal cells in a scratch assay, when compared with the medium from normoxia. Histological analysis after implantation of 8 days normoxic- and hypoxic-conditioned SVF CS in a hindlimb ischemia murine model showed improved formation of neo-blood vessels. Furthermore, Laser Doppler results demonstrated that the blood perfusion of the injured limb after 30 days was enhanced for the hypoxic CS group. Overall, these results suggest that SVF CS created under hypoxia can be used as functional vascularization units for tissue engineering and regenerative medicine.

Risk assessment in a research laboratory during sol–gel synthesis of nano-TiO2

The occupational risks in the nanotechnology research laboratories are an important topic since a great number of researchers are involved in this area. The risk assessment performed by both qualitative and quantitative methods is a necessary step for the management of the occupational risks. Risk assessment could be performed by qualitative methods that gather consensus in the scientific community. It is also possible to use quantitative methods, based in different technics and metrics, as indicative exposure limits are been settled by several institutions. While performing the risk assessment, the information on the materials used is very important and, if it is not updated, it could create a bias in the assessment results. The exposure to TiO2 nanoparticles risk was assessed in a research laboratory using a quantitative exposure method and qualitative risk assessment methods. It was found the results from direct-reading Condensation Particle Counter (CPC) equipment and the CB Nanotool seem to be related and aligned, while the results obtained from the use of the Stoffenmanager Nano seem to indicate a higher risk level.

Predicting plateau pressure in intensive medicine for ventilated patients

Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm

Rockburst laboratory tests database - Application of data mining techniques

Rockburst is characterized by a violent explosion of a block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoided and/or managed saving costs and possibly lives. The failure mechanism of rockburst needs to be better understood. Laboratory experiments are undergoing at the Laboratory for Geomechanics and Deep Underground Engineering (SKLGDUE) of Beijing and the system is described. A large number of rockburst tests were performed and their information collected, stored in a database and analyzed. Data Mining (DM) techniques were applied to the database in order to develop predictive models for the rockburst maximum stress (σRB) and rockburst risk index (IRB) that need the results of such tests to be determined. With the developed models it is possible to predict these parameters with high accuracy levels using data from the rock mass and specific project.

Tuberculosis and HIV co-infection

Tuberculosis (TB) and human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) constitute the main burden of infectious disease in resource-limited countries. In the individual host, the two pathogens, Mycobacterium tuberculosis and HIV, potentiate one another, accelerating the deterioration of immunological functions. In high-burden settings, HIV coinfection is the most important risk factor for developing active TB, which increases the susceptibility to primary infection or reinfection and also the risk of TB reactivation for patients with latent TB. M. tuberculosis infection also has a negative impact on the immune response to HIV, accelerating the progression from HIV infection to AIDS. The clinical management of HIV-associated TB includes the integration of effective anti-TB treatment, use of concurrent antiretroviral therapy (ART), prevention of HIV-related comorbidities, management of drug cytotoxicity, and prevention/treatment of immune reconstitution inflammatory syndrome (IRIS).

Hind limb ischemia in type 1 diabetic mice as useful tool to evaluate the neovascularization of tissue engineering constructs

Hind-limb ischemia has been used in type 1 diabetic mice to evaluate treatments for peripheral arterial disease or mechanisms of vascular impairment in diabetes [1]. Vascular deficiency is not only a pathophysiological condition, but also an obvious circumstance in tissue regeneration and in tissue engineering and regenerative medicine (TERM) strategies. We performed a pilot experiment of hind-limb ischemia in streptozotocin(STZ)-induced type 1 diabetic mice to hypothesise whether diabetes influences neovascularization induced by biomaterials. The dependent variables included blood flow and markers of arteriogenesis and angiogenesis. Type 1 diabetes was induced in 8-week-old C57BL/6 mice by an i.p. injection of STZ (50 mg/kg daily for 5 days). Hind-limb ischemia was created under deep anaesthesia and the left femoral artery and vein were isolated, ligated, and excised. The contralateral hind limb served as an internal control within each mouse. Non-diabetic ischaemic mice were used as experiment controls. At the hind-limb ischemia surgical procedure, different types of biomaterials were placed in the blood vessels gap. Blood flow was estimated by Laser Doppler perfusion imager, right after surgery and then weekly. After 28 days of implantation, surrounding muscle was excised and evaluated by histological analysis for arteriogenesis and angiogenesis. The results showed that implanted biomaterials were promote faster restoration of blood flow in the ischemic limbs and improved neovascularization in the diabetic mice. Therefore, we herein demonstrate that the combined model of hind-limb ischemia in type 1 diabetes mice is suitable to evaluate the neovascularization potential of biomaterials and eventually tissue engineering constructs.  

Bioactive ceramics for tissue engineering and regenerative medicine derived from marine sponges

Publicado em "Journal of tissue engineering and regenerative medicine". Vol. 8, suppl. s1 (2014)

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.