Molecular basis of transport of surface functionalised gold nanoparticles to pulmonary surfactant.

Ligands like alkanethiol (e.g. dodecanethiol, hexadecanethiol, etc.) and polymers (e.g. poly(vinyl pyrrolidone), polyethylene glycol-thiol) capped to the gold nanoparticles (AuNPs) are widely used in biomedical field as drug carriers and as promising materials for probing and manipulating cellular processes. Ligand functionalised AuNPs are known to interact with the pulmonary surfactant (PS) monolayer once reaching the alveolar region. Therefore, it is crucial to understand the interaction between AuNPs and PS monolayers. Using coarse-grained molecular dynamics simulations, the effect of ligand density, and ligand length have been studied for two classes of ligands on a PS model monolayer consisting of DPPC, POPG, cholesterol and SP-B (mini-peptide). The ligands considered in this study are alkanethiol and polyethylene glycol (PEG) thiol as examples of hydrophobic and hydrophilic ligands, respectively. It was observed that the interaction between AuNPs and PS changes the biophysical properties of PS monolayer in compressed and expanded states. The AuNPs with hydrophilic ligand, can penetrate through the monolayer more easily, while the AuNPs with hydrophobic ligand are embedded in the monolayer and participated in deforming the monolayer structure particularly the monolayer in the compressed state. The bare AuNPs hinder to lower the monolayer surface tension value at the interface, however introducing ligand to the bare AuNPs or increasing the ligand length and density have an impact of lowering of monolayer surface tension to a minor extent. The simulation results guide the design of ligand protected NPs as drug carriers and can identify the nanoparticles' potential side effects on lung surfactant.

Inverse identification of hyperelastic constitutive parameters of skeletal muscles via optimization of AI techniques.

Studies on the deformation characteristics and stress distribution in loaded skeletal muscles are of increasing importance. Reliable prediction of hyperelastic material parameters requires an inverse process, which possesses challenges. This work proposes two inverse procedures to identify the hyperelastic material parameters of skeletal muscles. The first one integrates nonlinear finite element method (FEM), random forest (RF) model, and Bayesian optimization (BO) algorithm. The other one integrates FEM, RF and hybrid Grid Search (GS), and Random Search (RS) algorithm. FEM models are first established to simulate nonlinear deformation of skeletal muscles subject to compression based on nonlinear mechanics principals. A dataset of nonlinear relationship between the nominal stress and principal stretch of skeletal muscles is created using our FEM models and the nonlinear relationship is learned through RF model. The BO, hybrid GS and RS algorithms are used to adjust the major model parameters in RF. Then the optimized RF is utilized to predict hyperelastic material parameters of skeletal muscles, with the help of uniaxial compression experiments. Intensive studies also have been carried out to compare the RF-BO approach with RF-Search approach, and the comparison results show that RF-BO approach is an effective and accurate approach to identify the hyperelastic material parameters of skeletal muscles. The present RF-BO model can be further extended for the predictions of constitutive parameters of other types of nonlinear soft materials.

Effect of concentration of PEG coated gold nanoparticle on lung surfactant studied with coarse-grained molecular dynamics simulations.

The surface modification of nanoparticles can not only change the physical and chemical properties of particles, such as the hydrophilic and hydrophobic properties and surface charges of nanoparticles to a certain extent, but also bring new functions to nanoparticles, such as membrane permeability and targeting. Inhaled nanoparticles (NPs) are experienced by the first biological barrier inside the alveolus known as lung surfactant (LS), consisting of phospholipids and proteins in the form of the monolayer at the air-water interface. Inhaled NPs can reach deep into the lungs and interfere with the biophysical properties of the lung components. The interaction mechanisms of bare gold nanoparticles (AuNPs) with the LS monolayer are not well understood. Coarse-grained molecular dynamics simulations were carried out to have a study on the interactions of PEG coated AuNPs with LS monolayers. It was observed that the interactions of AuNPs and LS components make the monolayer structure deform and change the biophysical properties of LS monolayer. The results also indicate that AuNPs with high concentrations hinder the lowering of the LS surface tension and reduce lateral mobility of lipids. Overall, the simulation results can provide guidance for the design of ligand protected NPs as drug carriers and can identify the nanoparticles potential side effect on lung surfactant.

Numerical simulation of deformed red blood cell by utilizing neural network approach and finite element analysis.

In order to have research on the deformation characteristics and mechanical properties of human red blood cells (RBCs), finite element models of RBC optical tweezers stretching and atomic force microscope (AFM) indentation were established. Non-linear elasticity of cell membrane was determined by using the neo-Hookean hyperelastic material model, and the deformation of RBC during stretching and indentation had been researched in ABAQUS, respectively. Considering the application of machine learning (ML) in material parameters identification, ML algorithm was combined with finite element (FE) method to identify the constitutive parameters. The material parameters were estimated according to the deformation characteristics of RBC obtained from the change of cell diameter with stretching force when RBC was stretched. The non-linear relationship between material parameter and RBC deformation was established by building a FE-model. The FE simulation of RBC stretching was used to construct the training set and the neural network trained by a large number of samples was used to predict the material parameter. With the predicted parameter, FE simulation of RBC under AFM indentation to explore the local deformation mechanism was completed.