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. After generating over 200 different liposome configurations by systematically modulating flow rates, lipid concentrations, organic:water mixing volume ratios, support-vector machine models and feed-forward artificial neural networks were trained to predict, respectively, the liposome dispersity/stability and size. This work presents an initial step towards the application and cultivation of ML models to instruct the microfluidic formulation of nanoparticles.

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. Different leaflet insertions and ventricle access sites were investigated, and resulting contact-area (CA), tensioning-forces (F) and leaflet's stress (LS) were calculated. The analyses showed that: i) NC-approach maintains low LS when performed with a posterior access site and optimizes the overlap between the leaflets at the systolic peak; ii) H-MRS-system presents better results in case of a more anterior ventricular entry site; however, for FED prolapse large variation of F and LS with respect to NC-approach are found; iii) an accidental contact between artificial sutures and the anterior leaflet may occur when valve function is restored through an excessive anterior access site. Present findings set light on specific technical aspects of transapical off-pump chords implantation, either performed with NC and H-MRS systems and highlight the advantages and disadvantages proper to the two devices. Our study also paves the basis for a systematic application of computational methodology, in order to plan a patient-specific mini-invasive approach thus maximizing the outcomes.

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. Specifically, we reproduced a patient-specific geometry with a wall of biological tissue or silicone, and a parametric geometry based on in vivo data made in silicone. The biological tissue and the silicone were modeled with a fiber-oriented anisotropic and isotropic hyperelastic model, respectively. (3) Results: Clearly, both the aorta's geometry and its constitutive material contribute to the determination of the aortic arch deformation; specifically, the parametric aorta exhibits a strain field similar to the patient-specific model with biological tissue. On the contrary, the local geometry affects the flow velocity distribution quite a lot, although it plays a minor role in the helicity along the arch. (4) Conclusions: The use of a patient-specific prototype in silicone does not a priori ensure a satisfactory reproducibility of the real aorta dynamics. Furthermore, the present simulations suggest that the realization of a simplified replica with the same compliance of the real aorta is able to mimic the overall behavior of the vessel.

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.g. the killing action of the drug. To this purpose, we exploit the employed mathematical-numerical model for tumor growth and drug delivery, together with the ANN - ML procedure, to integrate the results of the experimental tests and feed back the model itself, thus obtaining a reliable predictive tool. The procedure represents a hybrid data-driven, physics-informed approach to machine learning. The physical and mathematical model employed for the numerical simulations is without extracellular matrix (ECM) and healthy cells because of the experimental conditions we reproduce.

The neochord mitral valve repair procedure: Numerical simulation of different neochords tensioning protocols.

Transapical off-pump mitral valve repair with neochord implantation is an established technique for minimally-invasive intervention on mitral valve prolapse/flail. The procedure involves the positioning of artificial chords, whose length/tension is adjusted intraoperatively, adopting different methods based on the experience of the surgeon. This unsystematic approach occasionally leads to complications such as leaflet rupture and excessive/insufficient load on the neochords. In this study, finite element models of a generalized prolapsing mitral valve are used to verify the effect of two alternative tensioning approaches (AT - All together and 1-by-1 - one by one sequences) on the coaptation area and valve biomechanics, comparing results with a corresponding healthy configuration. The total force of about 1 N is exerted by the chords in both strategies, but the maximum stress and coaptation area are closer to those of the healthy configuration in the 1-by-1 sequence. However, the analysis also provides an explanation for the chords unloading in the 1-by-1 strategy observed in the clinical practice, and suggests an optimum tensioning methodology for NeoChord procedures. The study also reveals the potential power of the implemented numerical approach to serve as a tool for procedural planning, supporting the identification of the most suitable ventricular access site and the most effective stitching points for the artificial chords.

Evaluating the influence of mechanical stress on anticancer treatments through a multiphase porous media model.

Drug resistance is one of the leading causes of poor therapy outcomes in cancer. As several chemotherapeutics are designed to target rapidly dividing cells, the presence of a low-proliferating cell population contributes significantly to treatment resistance. Interestingly, recent studies have shown that compressive stresses acting on tumor spheroids are able to hinder cell proliferation, through a mechanism of growth inhibition. However, studies analyzing the influence of mechanical compression on therapeutic treatment efficacy have still to be performed. In this work, we start from an existing mathematical model for avascular tumors, including the description of mechanical compression. We introduce governing equations for transport and uptake of a chemotherapeutic agent, acting on cell proliferation. Then, model equations are adapted for tumor spheroids and the combined effect of compressive stresses and drug action is investigated. Interestingly, we find that the variation in tumor spheroid volume, due to the presence of a drug targeting cell proliferation, considerably depends on the compressive stress level of the cell aggregate. Our results suggest that mechanical compression of tumors may compromise the efficacy of chemotherapeutic agents. In particular, a drug dose that is effective in reducing tumor volume for stress-free conditions may not perform equally well in a mechanically compressed environment.

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. These studies report a reduction in cell proliferation as a function of increasingly applied stress on the surface of the spheroids. This work presents a specialization for tumor spheroid growth of a previous more general multiphase model. The equations of the model are derived in the framework of porous media theory, and constitutive relations for the mass transfer terms and the stress are formulated on the basis of experimental observations. A set of experiments is performed, investigating the growth of U-87MG spheroids both freely growing in the culture medium and subjected to an external mechanical pressure induced by a Dextran solution. The growth curves of the model are compared to the experimental data, with good agreement for both the experimental settings. A new mathematical law regulating the inhibitory effect of mechanical compression on cancer cell proliferation is presented at the end of the paper. This new law is validated against experimental data and provides better results compared to other expressions in the literature.

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

BACKGROUND: 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. Such a diameter depends on the wall shear rate (S). Artificial neural networks are proposed as a tool to predict n(s) as a function of S and particle diameter (d), from which to eventually derive d(opt). Artificial neural networks are trained using data from flow chamber experiments. Two networks are used, ie, ANN231 and ANN2321, exhibiting an accurate prediction for n(s) and its complex functional dependence on d and S. This demonstrates that artificial neural networks can be used effectively to minimize the number of experiments needed without compromising the accuracy of the study. A similar procedure could potentially be used equally effectively for in vivo analysis.