5 research outputs found

    Solvent-cast direct-writing and electrospinning as a dual fabrication strategy for drug-eluting polymeric bioresorbable stents

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    Bioresorbable stents (BRS) are conceived to retain sufficient radial strength after implantation while releasing an antiproliferative drug in order to prevent vessel restenosis until complete resorption. Ongoing research trends involve the use of innovative manufacturing techniques to achieve thinner struts combined with optimized local drug delivery. This work presents a combination of solvent-cast direct-writing (SC-DW) and electrospinning (ES) using poly-l-lactic acid (PLLA) and poly(l-lactic-co-Âż-caprolactone) (PLCL) as a new approach to generate everolimus-eluting BRS for cardiovascular applications. A Design of Experiment (DoE) was conducted to determine the optimal parameters to obtain a homogeneous coating with high specific surface. Manufactured stents were characterized by means of mechanical tests and scanning electron microscopy (SEM), with everolimus release in accelerated conditions quantified through High Performance Liquid Chromatography (HPLC). Drug loading was achieved either encapsulated in the struts of the stent or in an electrospun PLCL membrane covering the stent. In the former case, everolimus release was found to be insufficient, less than 3% of total drug loading after 8 weeks. In the latter, everolimus release considerably increased with respect to drug-loaded 3D-printed stents, with over 50% release in the first 6 hours of the test. In conclusion, everolimus release from PLCL-coated 3D-printed stents would match the dose and timeframe required for in vivo applications, while providing thinner struts than SC-DW drug-loaded stents.Peer ReviewedPostprint (published version

    Meta-heuristic improvements applied for steel sheet incremental cold shaping

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    In previous studies, a wrapper feature selection method for decision support in steel sheet incremental cold shaping process (SSICS) was proposed. The problem included both regression and classification, while the learned models were neural networks and support vector machines, respectively. SSICS is the type of problem for which the number of features is similar to the number of instances in the data set, this represents many of real world decision support problems found in the industry. This study focuses on several questions and improvements that were left open, suggesting proposals for each of them. More specifically, this study evaluates the relevance of the different cross validation methods in the learned models, but also proposes several improvements such as allowing the number of chosen features as well as some of the parameters of the neural networks to evolve, accordingly. Well-known data sets have been use in this experimentation and an in-depth analysis of the experiment results is included. 5 Ă— 2 CV has been found the more interesting cross validation method for this kind of problems. In addition, the adaptation of the number of features and, consequently, the model parameters really improves the performance of the approach. The different enhancements have been applied to the real world problem, an several conclusions have been drawn from the results obtained
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