title: Optimizing parameters on alignment of PCL/PGA nanofibrous scaffold: An artificial neural networks approach.
abstract: This paper proposes an artificial neural networks approach to finding the effects of electrospinning parameters on alignment of poly(ɛ-caprolactone)/poly(glycolic acid) blend nanofibers. Four electrospinning parameters, namely total polymer concentration, working distance, drum speed and applied voltage were considered as input and the standard deviation of the angles of nanofibers, introducing fibers alignments, as the output of the model. The results demonstrated that drum speed and applied voltage are two critical factors influencing nanofibers alignment, however their effect are entirely interdependent. Their effects also are not independent of other electrospinning parameters. In obtaining aligned electrospun nanofibers, the concentration and working distance can also be effective. In vitro cell culture study on random and aligned nanofibers showed directional growth of cells on aligned fibers.
authors.affiliation: Department of Medical Nanotechnology, School of Advanced Technologies in medicine, Tehran University of Medical Sciences, Tehran, Iran; National Cell Bank of Iran, Pasteur Institute of Iran, Tehran, Iran.
authors.forename: Farnoush Asghari
authors.initials: FA
authors.lastname: Paskiabi
authors.affiliation: Department of Medical Nanotechnology, School of Advanced Technologies in medicine, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Esmaeil
authors.initials: E
authors.lastname: Mirzaei
authors.affiliation: Department of Medical Nanotechnology, School of Advanced Technologies in medicine, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Amir
authors.initials: A
authors.lastname: Amani
authors.affiliation: National Cell Bank of Iran, Pasteur Institute of Iran, Tehran, Iran.
authors.forename: Mohammad Ali
authors.initials: MA
authors.lastname: Shokrgozar
authors.affiliation: Department of Medical Nanotechnology, School of Advanced Technologies in medicine, Tehran University of Medical Sciences, Tehran, Iran; Research Center for Science and Technology in Medicine (RCSTIM), Imam Khomeini Hospital Complex - Keshavarz Blvd., Tehran, Iran.
authors.forename: Reza
authors.initials: R
authors.lastname: Saber
authors.affiliation: Department of Medical Nanotechnology, School of Advanced Technologies in medicine, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: refaridi@sina.tums.ac.ir.
authors.forename: Reza
authors.initials: R
authors.lastname: Faridi-Majidi
mesh_terms: D002448:Cell Adhesion / Q000187:drug effects; D048430:Cell Shape / Q000187:drug effects; D004560:Electricity; D005347:Fibroblasts / Q000166:cytology / Q000187:drug effects / Q000648:ultrastructure; D006801:Humans; D057139:Nanofibers / Q000737:chemistry* / Q000648:ultrastructure; D016571:Neural Networks, Computer; D011091:Polyesters / Q000737:chemistry* / Q000494:pharmacology; D011100:Polyglycolic Acid / Q000737:chemistry* / Q000494:pharmacology; D015203:Reproducibility of Results; D054457:Tissue Scaffolds / Q000737:chemistry*
chemical_list: D011091:Polyesters; C016240:polycaprolactone; D011100:Polyglycolic Acid
keywords: Artificial neural networks; Electrospinning; Nanofibers alignment; Poly(glycolic acid); Poly(ɛ-caprolactone)