DSpace Collezione:http://elea.unisa.it/xmlui/handle/10556/24182024-03-27T17:41:39Z2024-03-27T17:41:39ZDesign and production of personalised medicines via innovative 3D printing technologiesSaviano, Marilenahttp://elea.unisa.it/xmlui/handle/10556/66072024-02-07T15:42:25Z2021-05-04T00:00:00ZTitolo: Design and production of personalised medicines via innovative 3D printing technologies
Autori: Saviano, Marilena
Abstract: The perspective of personalized therapies in recent years is projecting the community towards a rampant hankering for individualization of healthcare.
Paramount is the necessity of re-organizing the healthcare services and the production scales to meet the expectations of patients for targeted medicine. Thus, worldwide organizations are fostering the re-centering of the development and the design of medicines on the single patient, supporting the individual-oriented research, the flexibility of manufacturing processes, and the decentralization of medicine production.
All these changes in the healthcare system could improve the effectiveness of pharmaceutical treatments, leading to a reduction of the associated costs for care and enhancing patient compliance to therapeutic plans.
Particularly three-dimensional printing (3DP, also known as additive manufacturing, AM) is a disruptive technology, encompassing a wide range of techniques, which is revolutionizing the perspective of pharmaceutical production, especially in the personalized medicine frame.
The approval of the marketing authorization for the first 3D printed drug, SPRITAM® (Aprecia Pharmaceuticals, Langhorne, PA, USA) in 2015 has been a landmark for the application of 3DP technologies in pharmaceutical compounding. This approval reflected the interest of the pharmaceutical companies in these novel manufacturing processes and spurred the research in finding new solutions also for clinical practice.
3DP could allow targeted use of well-known drugs for subcategories of patients and can become a daily approach in pharmaceutical prescriptions. .. [Outline of the PhD project, edited by Author]
Descrizione: 2019 - 20202021-05-04T00:00:00ZApplication of Artificial Neural Networks to the design and optimization of superhydrophobic coatingsMarrafino, Francescohttp://elea.unisa.it/xmlui/handle/10556/66002024-02-07T15:42:13Z2021-05-19T00:00:00ZTitolo: Application of Artificial Neural Networks to the design and optimization of superhydrophobic coatings
Autori: Marrafino, Francesco
Abstract: Recently, considerable attention has been devoted to developing superhydrophobic surfaces due to their advantageous antimicrobial and self-cleaning properties. While significant effort has been devoted to their fabrication, very few polymeric superhydrophobic surfaces can be considered durable against externally imposed stresses. This work focuses on developing a coating with strong superhydrophobic properties and abrasion resistance, using a simple and scalable preparation process. Pyrogenic hydrophobic silica nanoparticles were used to confer superhydrophobic properties to the coatings. 450 samples were prepared using a layer-by-layer approach, deposing an epoxy resin or PDMS layer as adhesive on a substrate (PC/ABS), followed by one or more layers of silica nanoparticles or silica-resin mixed layers. The coating with the best properties shows a contact angle of 157° and a tape peeling grade resistance. The developed preparation method involves the spray deposition of a multilayer coating composed of four layers. Layers 1-3 are 1) silica nanoparticles, 2) epoxy resin, and 3) silica nanoparticles, followed by partial curing of the coating (15 minutes, 70°C); another silica layer is then sprayed on the surface and is cured for 10 minutes. In the second part of the work, the focus shifts to optimizing the coating and preparation process using Artificial Neural Networks. Given the high number of parameters involved, process optimization is a complex operation. Artificial Neural Networks are the best tool to deal with multivariate analysis problems. For this reason, data from all the prepared samples were collected into a dataset used to train a neural network capable of predicting the degree of hydrophobicity and abrasion resistance of a silica nanoparticles-based coating. The algorithms were used to prepare an optimized coating with a contact angle >160° and a high degree of abrasion resistance, currently under patent evaluation for potential application in antibacterial surfaces.
Finally, the application of Artificial Neural Networks to develop two bioinformatics predictive tools will be very briefly discussed. [edited by author]
Descrizione: 2019 - 20202021-05-19T00:00:00ZInteractome analysis of bioactive molecules: optimization of a functional proteomics platformMorretta, Elvahttp://elea.unisa.it/xmlui/handle/10556/65792024-02-07T15:37:16Z2020-10-24T00:00:00ZTitolo: Interactome analysis of bioactive molecules: optimization of a functional proteomics platform
Autori: Morretta, Elva
Abstract: The identification of natural products (NPs) target proteins is pivotal to understand their
mechanism of action, in order to develop molecular probes and/or potential drugs. In the last 15 years,
affinity chromatography-coupled to mass spectrometry (AP-MS) has been the top-choice technique
in the Drug Target Deconvolution field, having brought brilliant results in the targetome profiling of
a multitude of bioactive compounds.
Unfortunately, since a chemical modification of the molecule to be investigated is mandatory, AP MS is not suitable for compounds that do not exhibit properly reactive structural feature. .. [edited by the Author]
Descrizione: 2018 - 20192020-10-24T00:00:00ZValorization of typical agricultural productions and related biomasses as sources of bioactive compoundsBottone, Alfredohttp://elea.unisa.it/xmlui/handle/10556/63492024-02-07T15:32:18Z2019-11-05T00:00:00ZTitolo: Valorization of typical agricultural productions and related biomasses as sources of bioactive compounds
Autori: Bottone, Alfredo
Abstract: Main target of this PhD project was to define the metabolome of the main by-products, and in certain
cases of the edible parts, of selected agroalimentary productions, that are the flowers of Opuntia ficus
indica Mill. (nopal cactus), the leaves of Ficus carica L. (common fig), the leaves, the husks, the
shells and the kernels of Prunus dulcis Mill. (sweet almond) and Pistacia vera L. (pistachio).
Attention was mainly focused on valorizing the waste material generated from manufacturing
processes as potential sources of bioactives, pointing out their perspective employment in nutraceutics
and cosmetics.
Particularly, the selected plant parts were extracted by employing different solvents and extraction
methods. The obtained extracts were submitted to LC-HRMS/MS experiments, in order to achieve a
preliminary overview on their chemical composition. Polar extracts were fractionated by different
chromatographic approaches, and the isolated compounds were characterized by 1D and 2D-NMR
experiments, further supported by HRMS experiments. In addition, total phenolic content was
determined by Folin-Ciocalteu assay, while radical scavenging activity was evaluated by DPPH and
ABTS assays. Moreover, the main constituents of certain extracts were quantified by LC-MS/MS
experiments by a Multiple Reaction Monitoring approach.
The obtained results highlighted sweet almonds and pistachios as rich sources of unsaturated fatty
acids and antioxidant phenolics. On the other hand, the main by-products of the selected species
exhibited a variegated metabolome, with several constituents belonging to different chemical classes,
mainly phenolics, reported for their antioxidant and antinflammatory properties, suggesting their
potential employment for the manufacturing of nutraceutical and cosmetic formulations. [edited by Author]
Descrizione: 2017 - 20182019-11-05T00:00:00Z