Multiscale Computational Model Research Articles

Successful cardiac resynchronization therapy reduces negative septal work in patient-specific models of dyssynchronous heart failure.

In patients with dyssynchronous heart failure (DHF), cardiac conduction abnormalities cause the regional distribution of myocardial work to be non-homogeneous. Cardiac resynchronization therapy (CRT) using an implantable, programmed biventricular pacemaker/defibrillator, can improve the synchrony of contraction between the right and left ventricles in DHF, resulting in reduced morbidity and mortality and increased quality of life. Since regional work depends on wall stress, which cannot be measured in patients, we used computational methods to investigate regional work distributions and their changes after CRT. We used three-dimensional multi-scale patient-specific computational models parameterized by anatomic, functional, hemodynamic, and electrophysiological measurements in eight patients with heart failure and left bundle branch block (LBBB) who received CRT. To increase clinical translatability, we also explored whether streamlined computational methods provide accurate estimates of regional myocardial work. We found that CRT increased global myocardial work efficiency with significant improvements in non-responders. Reverse ventricular remodeling after CRT was greatest in patients with the highest heterogeneity of regional work at baseline, however the efficacy of CRT was not related to the decrease in overall work heterogeneity or to the reduction in late-activated regions of high myocardial work. Rather, decreases in early-activated regions of myocardium performing negative myocardial work following CRT best explained patient variations in reverse remodeling. These findings were also observed when regional myocardial work was estimated using ventricular pressure as a surrogate for myocardial stress and changes in endocardial surface area as a surrogate for strain. These new findings suggest that CRT promotes reverse ventricular remodeling in human dyssynchronous heart failure by increasing regional myocardial work in early-activated regions of the ventricles, where dyssynchrony is specifically associated with hypoperfusion, late systolic stretch, and altered metabolic activity and that measurement of these changes can be performed using streamlined approaches.

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about $1\,{\text{mm}^{2}}$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials can be computed from the simulated spiking activity. We here extend a local network and local field potential model to an area of $4\times 4\,{\text{mm}^{2}}$, preserving the neuron density and introducing distance-dependent connection probabilities and conduction delays. We find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental recordings from sensory cortex. Also compatible with experimental observations, the correlation of local field potential signals is strong and decays over a distance of several hundred micrometers. Enhanced spatial coherence in the low-gamma band around $50\,\text{Hz}$ may explain the recent report of an apparent band-pass filter effect in the spatial reach of the local field potential.

Understanding mechanotransduction in the distal colon and rectum via multiscale and multimodal computational modeling

Visceral pain in the large bowel is a defining symptom of irritable bowel syndrome (IBS) and the primary reason that patients visit gastroenterologists. This pain is reliably triggered by mechanical distension of the distal colon and rectum (colorectum). Consequently, the process of mechanotransduction by sensory afferents, responsible for translating mechanical colorectal stimuli into neural action potentials, plays a central role in IBS-related bowel pain. In this study, we aim to enhance our understanding of colorectal mechanotransduction by combining experimental findings in colorectal biomechanics and afferent neural encoding within a comprehensive computational simulation framework. To achieve this, we implemented a three-layered, fiber-reinforced finite element model that accurately replicates the nonlinear, heterogeneous, and anisotropic mechanical characteristics of the mouse colorectum. This model facilitates the computation of local mechanical stresses and strains around individual afferent endings, which have diameters on the micron-scale. We then integrated a neural membrane model to simulate the encoding of action potentials by afferent nerves in response to microscopic stresses and strains along the afferent endings. Our multiscale simulation framework enables the assessment of three hypotheses regarding the mechanical gating of action potential generation: (1) axial stress dominates mechanical gating of mechanosensitive channels, (2) both axial and circumferential stresses contribute, and (3) membrane shear stress dominates. Additionally, we explore how the orientation of afferent endings impacts neural encoding properties. This computational framework not only allows for the virtual investigation of colorectal mechanotransduction in the context of prolonged visceral hypersensitivity but can also guide the development of new experimental studies aimed at uncovering the neural and biomechanical mechanisms underlying IBS-related bowel pain.

Multiscale Computational Modeling of 3D Printed Continuous Fiber-Reinforced Composites

The printing parameters used during the printing procedure have a significant effect on the mechanical characteristics of 3D printed continuous fiber reinforced composites (3DP-CFRPCs). However, conducting experimental assessments of the material characteristics of 3DP-CFRPCs may require more effort and incur more costs. Computational material modeling may be used as a viable alternative to investigate the behavior of 3DP-CFRPCs under various printing conditions. The current work used material modeling approaches to examine the impact of different printing settings on the elastic characteristics of 3DP-CFRPCs. The inherent flexibility of beads is primarily established by homogenizing the pores within the matrix via the use of the Mori-Tanaka process. Subsequently, the elastic modulus is calculated by using finite element modeling on Representative Volume Element (RVE), which takes into account the microstructure and other printing attributes. An inconsistency was seen in the variation of projected elastic properties across models distinguished by various microstructures, with a more pronounced differentiation observed between intricate and simpler microstructures. Computational modeling has enhanced our understanding of the elastic properties of 3DP-CFRPCs under various printing conditions. Moreover, it has been shown that alterations in printing parameters have diverse impacts on the pliable characteristics of 3DP-CFRPCs. The impact of layer thickness on the mechanical characteristics of 3DP-CFRPCs was determined to be more substantial compared to the effect of printing temperature. The application of offset layup printing techniques enhanced the elastic properties of 3DP-CFRPCs, with the degree of improvement varying based on the orientation. As the level of porosity increased, the influence of pores situated between beads on the overall stiffness of 3DP-CFRPCs gradually diminished, while the impact of matrix pores on the overall stiffness of 3DP-CFRPCs gradually intensified.

Multiscale modeling shows how 2’-deoxy-ATP rescues ventricular function in heart failure

2'-deoxy-ATP (dATP) improves cardiac function by increasing the rate of crossbridge cycling and Ca[Formula: see text] transient decay. However, the mechanisms of these effects and how therapeutic responses to dATP are achieved when dATP is only a small fraction of the total ATP pool remain poorly understood. Here, we used a multiscale computational modeling approach to analyze the mechanisms by which dATP improves ventricular function. We integrated atomistic simulations of prepowerstroke myosin and actomyosin association, filament-scale Markov state modeling of sarcomere mechanics, cell-scale analysis of myocyte Ca[Formula: see text] dynamics and contraction, organ-scale modeling of biventricular mechanoenergetics, and systems level modeling of circulatory dynamics. Molecular and Brownian dynamics simulations showed that dATP increases the actomyosin association rate by 1.9 fold. Markov state models predicted that dATP increases the pool of myosin heads available for crossbridge cycling, increasing steady-state force development at low dATP fractions by 1.3 fold due to mechanosensing and nearest-neighbor cooperativity. This was found to be the dominant mechanism by which small amounts of dATP can improve contractile function at myofilament to organ scales. Together with faster myocyte Ca[Formula: see text] handling, this led to improved ventricular contractility, especially in a failing heart model in which dATP increased ejection fraction by 16% and the energy efficiency of cardiac contraction by 1%. This work represents a complete multiscale model analysis of a small molecule myosin modulator from single molecule to organ system biophysics and elucidates how the molecular mechanisms of dATP may improve cardiovascular function in heart failure with reduced ejection fraction.

A Multiscale Computational Modeling Framework to Quantify Microstructural Effects on Nucleation of Li Metal on Carbon Scaffold Architectures

Carbon materials with scaffold architecture have been shown to mitigate the non-uniform Li plating/stripping issues during cycling, which is critical for achieving significantly enhanced energy densities and improved safety of Li metal batteries. However, optimal design of the architecture and microstructure of the carbon materials requires fundamental understanding of the Li metal nucleation behavior at different length scales. In this poster, we present a multiscale computational modeling framework to quantify the microstructural effects of carbon materials on the tendency of Li metal nucleation during plating by integrating atomic-scale simulation, mesoscale microstructure-based homogenization, continuum-scale electrochemistry modeling, and classical nucleation theory. We modeled the architecture of the carbon scaffold and the electrochemistry of Li transport across the electrode and electrolyte by finite element simulation to determine boundary conditions for our mesoscale models. We then generated different porous microstructures of various carbon materials by stochastic and physics-based simulations informed from experiments and performed multiphysics-based simulations at mesoscale to identify the electro-chemo-mechanical hotspots which are sensitive to the microstructure morphology. We calculated the energy barriers for Li to form clusters on graphene surfaces as a function of local Li concentrations and electric fields by ab initio molecular dynamics simulations. The synergistic deployment of different models across scales allows us to theoretically estimate the Li metal nucleation tendency based on a modified classical nucleation theory for different microstructures under various electroplating conditions. Comparison of the theoretical predictions and experimental results will be briefly discussed. The insights obtained from this multiscale framework offer new understanding and strategies for the design of carbon scaffold architectures to mitigate the non-uniformity of Li plating and issues in Li metal anode deployment in solid-state batteries.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and 22-ERD-043.

Efficient uncertainty quantification in a spatially multiscale model of pulmonary arterial and venous hemodynamics.

Pulmonary hypertension (PH) is a debilitating disease that alters the structure and function of both the proximal and distal pulmonary vasculature. This alters pressure-flow relationships in the pulmonary arterial and venous trees, though there is a critical knowledge gap in the relationships between proximal and distal hemodynamics in disease. Multiscale computational models enable simulations in both the proximal and distal vasculature. However, model inputs and measured data are inherently uncertain, requiring a full analysis of the sensitivity and uncertainty of the model. Thus, this study quantifies model sensitivity and output uncertainty in a spatially multiscale, pulse-wave propagation model of pulmonary hemodynamics. The model includes fifteen proximal arteries and twelve proximal veins, connected by a two-sided, structured tree model of the distal vasculature. We use polynomial chaos expansions to expedite sensitivity and uncertainty quantification analyses and provide results for both the proximal and distal vasculature. We quantify uncertainty in blood pressure, blood flow rate, wave intensity, wall shear stress, and cyclic stretch. The latter two are important stimuli for endothelial cell mechanotransduction. We conclude that, while nearly all the parameters in our system have some influence on model predictions, the parameters describing the density of the microvascular beds have the largest effects on all simulated quantities in both the proximal and distal arterial and venous circulations.

Overview of Multi-Scale Simulation Techniques for Three Typical Steel Manufacturing Processes.

Steel products typically undergo intricate manufacturing processes, commencing from the liquid phase, with casting, hot rolling, and laminar cooling being among the most crucial processes. In the background of carbon neutrality, thin-slab casting and direct rolling (TSCR) technology has attracted significant attention, which integrates the above three processes into a simpler and more energy-efficient sequence compared to conventional methods. Multi-scale computational modeling and simulation play a crucial role in steel design and optimization, enabling the prediction of properties and microstructure in final steel products. This approach significantly reduces the time and cost of production compared to traditional trial-and-error methodologies. This study provides a review of cross-scale simulations focusing on the casting, hot-rolling, and laminar cooling processes, aiming at presenting the key techniques for realizing cross-scale simulation of the TSCR process.

"Modeling Diffusive Search by Non-Adaptive Sperm: Empirical and Computational Insights".

During fertilization, mammalian sperm undergo a winnowing selection process that reduces the candidate pool of potential fertilizers from ~106-1011 cells to 101-102 cells (depending on the species). Classical sperm competition theory addresses the positive or 'stabilizing' selection that acts on sperm phenotypes within populations of organisms but does not strictly address the developmental consequences of sperm traits among individual organisms that are under purifying selection during fertilization. It is the latter that is of utmost concern for improving assisted reproductive technologies (ART) because 'low fitness' sperm may be inadvertently used for fertilization during interventions that rely heavily on artificial sperm selection, such as intracytoplasmic sperm injection (ICSI). Importantly, some form of sperm selection is used in nearly all forms of ART (e.g., differential centrifugation, swim-up, or hyaluronan binding assays, etc.). To date, there is no unifying quantitative framework (i.e., theory of sperm selection) that synthesizes causal mechanisms of selection with observed natural variation in individual sperm traits. In this report, we reframe the physiological function of sperm as a collective diffusive search process and develop multi-scale computational models to explore the causal dynamics that constrain sperm 'fitness' during fertilization. Several experimentally useful concepts are developed, including a probabilistic measure of sperm 'fitness' as well as an information theoretic measure of the magnitude of sperm selection, each of which are assessed under systematic increases in microenvironmental selective pressure acting on sperm motility patterns.

A Model with Dopamine Depletion in Basal Ganglia and Cerebellum Predicts Changes in Thalamocortical Beta Oscillations.

Parkinsonism is presented as a motor syndrome characterized by rigidity, tremors, and bradykinesia, with Parkinson's disease (PD) being the predominant cause. The discovery that those motor symptoms result from the death of dopaminergic cells in the substantia nigra led to focus most of parkinsonism research on the basal ganglia (BG). However, recent findings point to an active involvement of the cerebellum in this motor syndrome. Here, we have developed a multiscale computational model of the rodent brain's BG-cerebellar network. Simulations showed that a direct effect of dopamine depletion on the cerebellum must be taken into account to reproduce the alterations of neural activity in parkinsonism, particularly the increased beta oscillations widely reported in PD patients. Moreover, dopamine depletion indirectly impacted spike-time-dependent plasticity at the parallel fiber-Purkinje cell synapses, degrading associative motor learning as observed in parkinsonism. Overall, these results suggest a relevant involvement of cerebellum in parkinsonism associative motor symptoms.

Multiscale computational model of aortic remodeling following postnatal disruption of TGFβ signaling

The healthy adult aorta is a remarkably resilient structure, able to resist relentless cardiac-induced and hemodynamic loads under normal conditions. Fundamental to such mechanical homeostasis is the mechano-sensitive cell signaling that controls gene products and thus the structural integrity of the wall. Mouse models have shown that smooth muscle cell-specific disruption of transforming growth factor-beta (TGFβ) signaling during postnatal development compromises this resiliency, rendering the aortic wall susceptible to aneurysm and dissection under normal mechanical loading. By contrast, disruption of such signaling in the adult aorta appears to introduce a vulnerability that remains hidden under normal loading, but manifests under increased loading as experienced during hypertension. We present a multiscale (transcript to tissue) computational model to examine possible reasons for compromised mechanical homeostasis in the adult aorta following reduced TGFβ signaling in smooth muscle cells.

Platelet Biorheology and Mechanobiology in Thrombosis and Hemostasis: Perspectives from Multiscale Computation.

Thrombosis is the pathological clot formation under abnormal hemodynamic conditions, which can result in vascular obstruction, causing ischemic strokes and myocardial infarction. Thrombus growth under moderate to low shear (<1000 s-1) relies on platelet activation and coagulation. Thrombosis at elevated high shear rates (>10,000 s-1) is predominantly driven by unactivated platelet binding and aggregating mediated by von Willebrand factor (VWF), while platelet activation and coagulation are secondary in supporting and reinforcing the thrombus. Given the molecular and cellular level information it can access, multiscale computational modeling informed by biology can provide new pathophysiological mechanisms that are otherwise not accessible experimentally, holding promise for novel first-principle-based therapeutics. In this review, we summarize the key aspects of platelet biorheology and mechanobiology, focusing on the molecular and cellular scale events and how they build up to thrombosis through platelet adhesion and aggregation in the presence or absence of platelet activation. In particular, we highlight recent advancements in multiscale modeling of platelet biorheology and mechanobiology and how they can lead to the better prediction and quantification of thrombus formation, exemplifying the exciting paradigm of digital medicine.

Multiscale Modeling of Plutonium Radiation Chemistry in Nitric Acid Solutions. 1. Cobalt-60 Gamma Irradiation of Pu(IV).

Careful manipulation of the plutonium oxidation states is essential in the study and utilization of its rich redox chemistry. To achieve this level of control, a comprehensive mechanistic understanding of radiation-induced plutonium redox chemistry is critical due to the unavoidable exposure of plutonium to ionizing radiation fields, both inherent and from in-process applications. To this end, we have developed an experimentally evaluated multiscale computer model for the prediction of gamma radiation-induced Pu(IV) redox chemistry in concentrated nitric acid solutions (1.0, 3.0, and 6.0 M). Under these acidic, aqueous solution conditions, cobalt-60 gamma irradiation afforded marginal net conversion of Pu(IV) to Pu(VI), the extent of which was dependent on the concentration of HNO3 and absorbed gamma dose. Multiscale calculations, which are in excellent agreement with experimental data, indicate that this observation is due to a combination of inherent plutonium disproportionation reactions and several radiation-induced processes, including redox cycling between Pu(IV) and Pu(III), as achieved by the reduction of Pu(IV) by nitrous acid and hydrogen peroxide, the oxidation of Pu(III) by nitrate and hydroxyl radicals, and the sequential oxidation of Pu(IV) to Pu(V) and Pu(VI) by the remaining available yield of nitrate radicals.

Comparing the bone regeneration potential between a trabecular bone and a porous scaffold through osteoblast migration and differentiation: A multiscale approach.

Both cell migration and osteogenic differentiation are critical for successful bone regeneration. Therefore, understanding the mechanobiological aspects that govern these two processes is essential in designing effective scaffolds that promote faster bone regeneration. Studying these two factors at different locations is necessary to manage bone regeneration in various sections of a scaffold. Hence, a multiscale computational model was used to observe the mechanical responses of osteoblasts placed in different positions of the trabecular bone and gyroid scaffold. Fluid shear stresses in scaffolds at cell seeded locations (representing osteogenic differentiation) and strain energy densities in cells at cell substrate interface (representing cell migration) were observed as mechanical response parameters in this study. Comparison of these responses, as two critical factors for bone regeneration, between the trabecular bone and gyroid scaffold at different locations, is the overall goal of the study. This study reveals that the gyroid scaffold exhibits higher osteogenic differentiation and cell migration potential compared to the trabecular bone. However, the responses in the gyroid only mimic the trabecular bone in two out of nine positions. These findings can guide us in predicting the ideal cell seeded sites within a scaffold for better bone regeneration and in replicating a replaced bone condition by altering the physical parameters of a scaffold.

Multiscale Computational Framework for the Liquid-Liquid Phase Separation of Intrinsically Disordered Proteins.

The reversible assembly of intrinsically disordered proteins (IDPs) to form membraneless organelles (MLOs) is a fundamental process involved in the spatiotemporal regulation in living cells. MLOs formed via liquid-liquid phase separation (LLPS) serve as molecule-enhancing hubs to regulate cell functions. Owing to the complexity and dynamic nature of the protein assembly via a network of weak inter- and intra-molecular interactions, it is challenging to describe and predict the LLPS behavior. We have developed a multiscale computational model for IDPs, using the fused in sarcoma (FUS) protein and its variants as illustrative examples. To simplify the description of protein, FUS is represented as a linear chain of stickers interspaced by spacers, as inspired by the associative polymer theory. Low-complexity aromatic-rich kinked segments (LARKS) available in FUS were identified using LARKSdb and represented as "stickers". The pairwise potential energies of each pair of stickers and their β-sheet-forming propensity were estimated via molecular docking and all atomistic molecular dynamics (AA-MD) simulations. Subsequently, FUS chains were randomly positioned in a cubic lattice as coarse-grained (CG) beads, with the bead assignment based on the Kuhn length estimation of stickers and spacers. Stochastic FUS movements were modeled by Monte Carlo (MC) simulations. In addition to the Metropolis algorithm, discretized pair potential distributions between stickers were considered in the move acceptance criteria. The chosen pair potential represents one of the possible binding energy states, with its probability determined by the frequency of the binding energy distribution histogram. The fluctuations of averaged radial distribution functions (RDFs) in successive MC trial move intervals of equilibrated lattice MC simulations were used to indicate the dynamic nature of assembly/disassembly of the protein chains. This multiscale computational framework provides an economical and efficient way of predicting and describing the LLPS behavior of IDPs.

Higher-order multi-scale computational approach and its convergence for nonlocal gradient elasticity problems of composite materials

A novel higher-order multi-scale computational approach for efficient nonlocal gradient elasticity simulation of composite materials is proposed in this paper. Based on the use of decoupling technique, raw fourth-order nonlocal gradient equations with periodically oscillatory coefficients are decoupled as the new second-order multi-scale equations for reducing the continuity requirements when implementing multi-scale simulation. Next, a novel macro-micro coupled multi-scale computational model with higher-order corrected terms is rigorously devised for high-resolution multi-scale and nonlocal analysis of composite materials via multi-scale asymptotic analysis. Then, the local error analysis of higher-order multi-scale solutions in the point-wise sense illustrates that establishing higher-order asymptotic corrected terms plays a vital role in investigating the micromechanical mechanisms. Moreover, a rigorous global error estimation with an explicit rate of higher-order multi-scale solutions is first derived in the energy norm sense. In addition, an efficient multi-scale numerical algorithm is presented to effectively simulate nonlocal gradient elasticity problems of composite materials based on finite element method (FEM). And we also derive the convergence estimation of the proposed numerical algorithm. Finally, numerical experiments are conducted to gauge the efficiency and accuracy of the proposed higher-order multi-scale method. This paper offers a unified higher-order two-scale computational framework for enabling nonlocal gradient elasticity behavior simulation of composite materials.

Making the Case for Scaling Up Microwave Sintering of Ceramics

The densification and sintering of ceramics using microwaves is first reported in the mid‐1960s. Today, the reduced carbon footprint of this process has renewed interest as it uses less energy overall compared to conventional process heating/furnaces. However, scaling up and commercializing the microwave sintering process of ceramics remains a formidable challenge. As a contactless method, microwave sintering offers geometric flexibility over other field‐assisted sintering processes. Yet, the inability to address multiscale, multiphysics‐driven heterogeneities arising during microwave coupling limits discussions about a future scale‐up process. Herein, the case is made that unlike 60 years ago, new advances in multiscale computational modeling, materials characterization, control systems, and software open up new avenues for addressing these challenges. More importantly, the rise of additive manufacturing techniques demands the innovation of sintering processes in the ceramics community for realizing near‐net‐shaped and complex parts for applications ranging from medical implants to automotive and aerospace parts.

Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review

Herein, state‐of‐the‐art multiscale modeling methods have been described. This research includes notable molecular, micro‐, meso‐, and macroscale models for hard (polymer, metal, yarn, fiber, fiber‐reinforced polymer, and polymer matrix composites) and soft (biological tissues such as brain white matter [BWM]) composite materials. These numerical models vary from molecular dynamics simulations to finite‐element (FE) analyses and machine learning/deep learning surrogate models. Constitutive material models are summarized, such as viscoelastic hyperelastic, and emerging models like fractional viscoelastic. Key challenges such as meshing, data variability and material nonlinearity‐driven uncertainty, limitations in terms of computational resources availability, model fidelity, and repeatability are outlined with state‐of‐the‐art models. Latest advancements in FE modeling involving meshless methods, hybrid ML and FE models, and nonlinear constitutive material (linear and nonlinear) models aim to provide readers with a clear outlook on futuristic trends in composite multiscale modeling research and development. The data‐driven models presented here are of varying length and time scales, developed using advanced mathematical, numerical, and huge volumes of experimental results as data for digital models. An in‐depth discussion on data‐driven models would provide researchers with the necessary tools to build real‐time composite structure monitoring and lifecycle prediction models.

A Computational Multiscale Modeling Method for Nanosilver-Sintered Joints with Stochastically Distributed Voids

A high power density is required in wide band gap power semiconductor packaging, which has led to the popularity of sintered nanosilver as an interconnecting material. However, affected by stochastically distributed voids in its microstructure, this material in practice exhibits instability leading to reduced reliability. In this paper, a computational multiscale modeling method is proposed to simulate the influence of micro-voids on macro-properties, providing an efficient tool to analyze the aforementioned problem. At the micro-scale, the three-parameter Weibull distribution of the equivalent Young’s modulus and the normal distribution of the equivalent Poisson’s ratio are captured by Monte Carlo-based finite element simulation on the reconstructed stochastic representative elements, where the density and distribution morphology of micro-voids are taken into consideration. At the macro-scale, the effect of the microscopic voids is transferred through a random sampling process to construct the multiscale model. The effectiveness and validity of the proposed method are verified through experimental case studies involving the modeling of nanosilver-sintered joints sintered at temperatures of 275°C and 300°C. In addition, the effects of the sintering temperature on the dispersion of the micro-voids, the distribution fluctuation of the constitutive parameters, and the mechanical properties are also discussed based on numerical and experimental results.Graphical

Spatiotemporal modeling of nano-delivered chemotherapeutics for synergistic microwave ablation cancer therapy

Background and objectiveThe effectiveness of current microwave ablation (MWA) therapies is limited. Administration of thermosensitive liposomes (TSLs) which release drugs in response to heat has presented a significant potential for enhancing the efficacy of thermal ablation treatment, and the benefits of targeted drug delivery. However, a complete knowledge of the mechanobiological processes underlying the drug release process, especially the intravascular drug release mechanism and its distribution in response to MWA needs to be improved. Multiscale computational-based modeling frameworks, integrating different biophysical phenomena, have recently emerged as promising tools to decipher the mechanobiological events in combo therapies. The present study aims to develop a novel multiscale computational model of TSLs delivery following MWA implantation. MethodsDue to the complex interplay between the heating procedure and the drug concentration maps, a computational model is developed to determine the intravascular release of doxorubicin from TSL, its transvascular transport into the interstitium, transport in the interstitium, and cell uptake. Computational models can estimate the interplays among liposome and drug properties, tumor perfusion, and heating regimen to examine the impact of essential parameters and to optimize a targeted drug delivery platform. ResultsResults indicated that the synergy of TSLs with MWA allows more localized drug delivery with lower side effects. The drug release rate and tumor permeability play crucial roles in the efficacy of TSLs during MWA treatment. The computational model predicted an unencapsulated drug lime around the ablated zone, which can destroy more cancer cells compared to MWA alone by 40%. Administration of TSLs with a high release rate capacity can improve the percentage of killed cancer cells by 24%. Since the heating duration in MWA is less than 15 min, the presented combination therapy showed better performance for highly permeable tumors. ConclusionThis study highlights the potential of the proposed computational framework to address complex and realistic scenarios in cancer treatment, which can serve as the future research foundation, including advancements in nanomedicine and optimizing the pair of TSL and MWA for both preclinical and clinical studies. The present model could be as a valuable tool for patient-specific calibration of essential parameters.