Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes.

The association of machine learning (ML) tools with the synthesis of nanoparticles has the potential to streamline the development of more efficient and effective nanomedicines. The continuous-flow synthesis of nanoparticles via microfluidics represents an ideal playground for ML tools, where multiple engineering parameters - flow rates and mixing configurations, type and concentrations of the reagents - contribute in a non-trivial fashion to determine the resultant morphological and pharmacological attributes of nanomedicines. Here we present the application of ML models towards the microfluidic-based synthesis of liposomes loaded with a model hydrophobic therapeutic agent, curcumin.

Beating heart implantation of transventricular artificial cordae: How can access site selection and leaflet insertion improve mitral regurgitation correction?

NeoChord-DS1000-System (NC) and The Harpoon-Mitral-Repair-System (H-MRS) are two trans-apical chordal implantation devices developed for the treatment of degenerative mitral valve (MV) regurgitation (DMR) either if as Fibroelastic-Deficiency (FED), Forma-Frusta (FF), or Barlow (B) presentation. The aim of this study is to evaluate some of the advantages and disadvantages of these two different devices by performing numerical simulation analyses focused on different transventricular access sites in all subsets of DMR presentations. By applying a novel approach for the development of patient-specific MV domains we worked out a set of numerical simulations of the artificial chordae implantation.

Numerical Models Can Assist Choice of an Aortic Phantom for In Vitro Testing.

(1) Background: The realization of appropriate aortic replicas for in vitro experiments requires a suitable choice of both the material and geometry. The matching between the grade of details of the geometry and the mechanical response of the materials is an open issue that deserves attention. (2) Methods: To explore this issue, we performed a series of Fluid-Structure Interaction simulations, which compared the dynamics of three aortic models.

Drug delivery: Experiments, mathematical modelling and machine learning.

We address the problem of determining from laboratory experiments the data necessary for a proper modeling of drug delivery and efficacy in anticancer therapy. There is an inherent difficulty in extracting the necessary parameters, because the experiments often yield an insufficient quantity of information. To overcome this difficulty, we propose to combine real experiments, numerical simulation, and Machine Learning (ML) based on Artificial Neural Networks (ANN), aiming at a reliable identification of the physical model factors, e.

Predicting the growth of glioblastoma multiforme spheroids using a multiphase porous media model.

Tumor spheroids constitute an effective in vitro tool to investigate the avascular stage of tumor growth. These three-dimensional cell aggregates reproduce the nutrient and proliferation gradients found in the early stages of cancer and can be grown with a strict control of their environmental conditions. In the last years, new experimental techniques have been developed to determine the effect of mechanical stress on the growth of tumor spheroids.

Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks.

Nanoparticles with different sizes, shapes, and surface properties are being developed for the early diagnosis, imaging, and treatment of a range of diseases. Identifying the optimal configuration that maximizes nanoparticle accumulation at the diseased site is of vital importance. In this work, using a parallel plate flow chamber apparatus, it is demonstrated that an optimal particle diameter (d(opt)) exists for which the number (n(s)) of nanoparticles adhering to the vessel walls is maximized.