1,721,103 research outputs found
7 Walde P. ,Wick R. , Fresta M. , Autopoietic self-reproduction of fatty acid vesicles J. Am. Chem. Soc. 116 (1994) 11649
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“Lei non sa chi sono io”. L’automobile: un secolo di evoluzione legislativa, sociale, culturale
No full textGemcitabine-loaded innovative nanocarriers vs GEMZAR: biodistribution, pharmacokinetic features and in vivo antitumor activity.
No full textINTRODUCTION: Gemcitabine, an anticancer drug, is a nucleoside analog deoxycytidine antimetabolite, which acts against a wide range of solid tumors. The limitation of gemcitabine is its rapid inactivation by the deoxycytidine deaminase enzyme following its in vivo administration. AREAS COVERED: One of the most promising new approaches for improving the biopharmaceutical properties of gemcitabine is the use of innovative drug delivery devices. This review explains the current status of gemcitabine drug delivery, which has been under development over the past 5 years, with particular emphasis on liposomal delivery. In addition, the use of novel supramolecular vesicular aggregates (SVAs), polymeric nanoparticles and squalenoylation were treated as interesting innovative approaches for the administration of the nucleoside analog. EXPERT OPINION: Different colloidal systems containing gemcitabine have been realized, with the aim of providing important potential advancements through traditional ways of therapy. A possible future commercialization of modified gemcitabine is desirable, as was true in the case of liposomal doxorubicin (Doxil(®), Caely(®))
Release behaviour of polyethyl- and polyisobutylcyanoacrylate nanoparticles as a function of surfactant used in the preparation procedure
No full textNeural Architecture for Tennis Shot Classification on Embedded System
No full textData analysis has become a common practice in professional and amateur sport activities, to monitor the player state and enhance performance. In tennis, performance analysis requires detecting and recognizing the different types of shots. With the advances in microcontrollers and machine learning algorithms, this topic becomes ever more considerable. We propose a 1-D convolutional neural network (CNN) model and an embedded system based on Arduino-Nano system for real-time shot classification. The network is trained through a dataset composed of three different tennis shot types, with 6 features recorded by an inertial device placed on the racket. Results demonstrate that the proposed model is able to discriminate the tennis shots with high accuracy, also generalizing to different users. The network has been deployed on a low-cost Arduino nano 33 IoT model, with an inference time of 65 ms
Supramolecular devices to improve the treatment of brain diseases.
No full textThe blood-brain barrier (BBB) hinders the accumulation of active compounds in the central nervous system, thus decreasing their therapeutic effectiveness. To overcome this obstacle, interesting supramolecular nanodevices are herein considered. These systems have many advantages over the conventional formulations, such as having structures made up of biocompatible and biodegradable materials, the possibility of bypassing the BBB in a non-invasive manner (without structural modifications) and the possibility of being structurally modified to modulate the biopharmaceutical properties of the encapsulated compounds. Polymolecular (liposomes, niosomes, nanogels) and oligomolecular (cyclodextrins) devices have potential clinical applications in brain drug delivery, being capable of active targeting that can concentrate bioactives in the brain
AN INTERPRETATIVE ANALYSIS OF THE EFFECT OF THE SURFACTANTS USED FOR THE PREPARATION OF POLYALKYLCYANOACRYLATE NANOPARTICLES ON THE RELEASE PROCESS
No full textThe release of fluorescein from polyethylcyanoacrylate (PECA) or polyisobutyl-cyanoacrylate (PICA) nanoparticles was affected by the surfactants used for the preparation. The different surfactants also modified the molecular weight, size and loading of the nanoparticles. However, these factors were not be responsible for the different release. According to the release profiles and the Baker-Lonsdale model, a portion of fluorescein was concentrated near the nanoparticle surface. Thus, a non-homogeneous distribution of the fluorescent probe inside the nanoparticles was hypothesized. This distribution could reflect the fluorescein position inside the micella during the polymerization stage, or could be reached during the washing stage as the consequence of a different effect of the surfactants on the porosity of the nanoparticle structure
Leveraging Neural Architecture Search for Structural Health Monitoring on Resource-Constrained Devices
No full textIn recent decades signal processing incorporated the capabilities offered by Deep Learning (DL) models, especially for complex tasks. DL models demand significant memory, power, and computational resources, posing challenges for Microcontroller Units (MCUs) with limited capacities. The possibility to run models directly on the edge device is key in connectivity-limited scenarios such as Structural Health Monitoring (SHM). For those scenarios, it is necessary to use Tiny Machine Learning techniques to reduces computational requirements. This study focuses on the impact of the extended version of the state-of-the-art Neural Architecture Search (NAS) tool, μNAS, for SHM applications, targeting four commonly used MCUs. Our assessment is based on the Z24 Bridge benchmark dataset, a common dataset for SHM we employed to train and evaluate models. We then discuss if the models found fit the constraints of the MCUs and the possible tradeoffs between error rate and model computational requirements. We also offer a comparison with the Raspberry Pi 4 Model B to highlight μNAS’s capability in achieving high accuracy with higher computing capabilities. The obtained results are promising, as the found models satisfy the given constraints both in term of accuracy and memory footprint
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