3 resultados para Smooth particle hydrodynamics
em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco
Resumo:
[EN] Diabetic foot ulcers (DFUs) represent a major clinical challenge in the ageing population. To address this problem, rhEGF-loaded Poly-Lactic-co-Glycolic-Acid (PLGA)-Alginate microspheres (MS) were prepared by a modified w/o/w-doubleemulsion/ solvent evaporation method. Different formulations were evaluated with the aim of optimising MSs properties by adding NaCl to the surfactant solution and/or the solvent removal phase and adding alginate as a second polymer. The characterization of the developed MS showed that alginate incorporation increased the encapsulation efficiency (EE) and NaCl besides increasing the EE also became the particle surface smooth and regular. Once the MS were optimised, the target loading of rhEGF was increased to 1% (PLGA-Alginate MS), and particles were sterilised by gamma radiation to provide the correct dosage for in vivo studies. In vitro cell culture assays demonstrated that neither the microencapsulation nor the sterilisation process affected rhEGF bioactivity or rhEGF wound contraction. Finally, the MS were evaluated in vivo for treatment of the full-thickness wound model in diabetised Wistar rats. rhEGF MS treated animals showed a statistically significant decrease of the wound area by days 7 and 11, a complete re-epithelisation by day 11 and an earlier resolution of the inflammatory process. Overall, these findings demonstrate the promising potential of rhEGF-loaded MS (PLGA-Alginate MS) to promote faster and more effective wound healing, and suggest its possible application in DFU treatment.
Resumo:
201 p.
Resumo:
In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed