Realistic Cell Model Research Articles

The effectiveness of combining gadolinium and boron neutron capture therapy at the cellular level

ABSTRACT The present study developed a program to investigate the effectiveness of combining gadolinium and boron neutron capture therapy at the cellular level. The program can automatically generate a 3D random cell array for the Monte Carlo code PHITS. It can create a cell array with random cell shape, size, structure distribution, and cell rotation to build as realistic human cell models as possible. The cell array is utilized to calculate the distribution of 157Gd or/and 10B in different cell structures. It also examines the scenario of two separate drug injections: first, administering BPA for irradiation, followed by the injection of 157Gd for subsequent irradiation. In both cases, the distribution of drugs within the cell structures is assumed to be random. The results showed that 1000 ppm of the 157Gd uniformly filling the whole-cell array and 65 ppm of BPA exhibited a comparable biologically equivalent dose at the cellular level. The biological equivalent dose significantly increases when gadolinium drugs enter the nucleus. Incorporating 157Gd into the nucleus can provide a sufficient therapeutic effect, making the combination of gadolinium and boron neutron capture therapy a promising approach for neutron capture therapy.

Utility of realistic microscopy-based cell models in simulation studies of nanoparticle-enhanced photon radiotherapy

To enhance the effect of radiation on the tumor without increasing the dose to the patient, the combination of high-Z nanoparticles with radiotherapy has been proposed. In this work, we investigate the effects of the physical parameters of nanoparticles (NPs) on the Dose Enhancement Factor (DEF), and on the Sensitive Enhancement Ratio (SER) by applying a version of the Linear Quadratic Model. A method for constructing voxelized realistic cell geometries in Monte Carlo simulations from confocal microscopy images was developed and applied to Gliobastoma Multiforme cell lines (U87 and U373). The comparison of simulations with realistic geometry and spherical geometry shows that there is significant impact on the survival curves obtained for the same irradiation conditions. Using this model, the DEF and the SER are determined as a function of the concentration, size and distribution of gold nanoparticles within the cell. For small NPs, d AuNP = 10 nm, no clear trend in the DEF and SER was observed when the number of NPs within the cell increases. Experimentally, the variable number of NPs measured inside the U373 cells (ranging between 1.48 × 105 and 1.19 × 106) also did not influence much the observed cell survival upon irradiation of the cells with a Co-60 source. The same lack of trend is obtained when the Au content in the cell is kept constant, 0.897 mg/g, but the size of the NPs is changed. However, if the number of NPs is kept constant (7.91 × 105) and the size changes, there is a critical diameter above which the dose effect increases significantly. Using the realistic geometries, it was verified that the key parameter for the DEF and the SER enhancement is the volume fraction of Au in the cell, with NP size being a more important parameter than the number of NPs.

Exploring the Physical and Biological Aspects of BNCT with a Carboranylmethylbenzo[b]acridone Compound in U87 Glioblastoma Cells.

Boron neutron capture therapy (BNCT) is a re-emerging technique for selectively killing tumor cells. Briefly, the mechanism can be described as follows: after the uptake of boron into cells, the thermal neutrons trigger the fission of the boron atoms, releasing the α-particles and recoiling lithium particles and high-energy photons that damage the cells. We performed a detailed study of the reactor dosimetry, cellular dose assessment, and radiobiological effects induced by BNCT in glioblastoma (GBM) cells. At maximum reactor power, neutron fluence rates were ϕ0 = 6.6 × 107 cm−2 s−1 (thermal) and θ = 2.4 × 104 cm−2 s−1 with a photon dose rate of 150 mGy·h−1. These values agreed with simulations to within 85% (thermal neutrons), 78% (epithermal neutrons), and 95% (photons), thereby validating the MCNPX model. The GEANT4 simulations, based on a realistic cell model and measured boron concentrations, showed that >95% of the dose in cells was due to the BNC reaction. Carboranylmethylbenzo[b]acridone (CMBA) is among the different proposed boron delivery agents that has shown promising properties due to its lower toxicity and important cellular uptake in U87 glioblastoma cells. In particular, the results obtained for CBMA reinforce radiobiological effects demonstrating that damage is mostly induced by the incorporated boron with negligible contribution from the culture medium and adjacent cells, evidencing extranuclear cell radiosensitivity.

Numerical modeling of Surface-Scan MRI experiments for improved diagnostics of commercial battery cells

Recent progress in MRI methods development led to a novel concept for operando screening of commercial battery cells. Sensing electrochemical processes inside an operating cell can be done via the detection of associated magnetic fields. This concept is based on the classic phenomenon described by Ampère's circuital and Biot–Savart laws. A new method referred to as Surface-Scan MRI employs ultra-fast quantitative mapping of the magnetic field in a thin layer of polymer placed in direct contact with the cell. Preliminary experimental work demonstrated the ability of Surface-Scan MRI to detect cells that undergone overcharging, a hazardous event that can degrade cells' components and ultimately creates a risk of fire. In the future, the analysis of Surface-Scan MRI data can rely largely on numerical simulations of electromagnetic phenomena based on accurate models of real cells. A series of numerical tests performed in this work confirms the capacity of Surface-Scan MRI to detect a variety of defects associated with electrochemical degradation of cathode, anode and electrolyte materials. Effects of morphology, location and conductivity of the defects on magnetic field distributions were examined for realistic cell models including flat jelly roll configurations. MRI resolution limits associated with the defect domain size and with the distance between the defect and the detection medium were estimated. Location and morphology of highly conductive defects (σ > 105 S m−1), e.g. regions rich in quasi-metallic dendrites, can be directly identified from Surface-Scan MRI images. Low conductivity defects , e.g. degraded electrolyte or cathode and anode coatings, manifest via patterns much larger than the defects.

Spherical harmonics based model of electric field effects on neocortical neurons

Abstract Transcranial electrical stimulation (tES) techniques generate electric fields in the brain whose effects on single neurons can be approximated from the cable equation as a membrane potential perturbation that is proportional to the membrane length constant (lambda). This is referred to as the lambda-E model and is a simple mechanistic approach that assumes that the membrane perturbation can be modeled as the dot product of a vector parallel to the orthodromic direction of cortical neurons with the electric field, multiplied by lambda. We propose a more general way to represent the model of the membrane perturbation as a function of the magnitude and angular orientation of the E-field, using a spherical harmonics expansion that can capture the dependence of the perturbation on electric field direction. In this representation, the lambda-E model corresponds to the first-order degree (l=1) term of the expansion. This approach can represent the three-dimensional response of a specific cell part, obtained experimentally. Here we employ it using synthetic data from realistic neocortical cell models in a DC field in the tDCS regime, fitting the coefficients for the membrane responses. We use realistic reconstructions of the somatosensory cortex from the Blue Brain Project to simulate the response of different pyramidal cells and interneurons to electric fields of 1 V/m as a function of the orientation of the field with respect to the cell using the NEURON simulation environment. We report membrane perturbation values comparable to those in the literature, extend it to different cell types, and provide the spherical harmonic coefficient profile for each neuron. Finally, we provide an azimuth-independent version of the model providing an averaged response that only depends on the electric field vector and the cortical surface. We believe this method can improve the accuracy and detail of real life simulation-based stimulation. Keywords: Electric field coupling, Multicompartment models, tES, NEURON

Interactive 3D Human Heart Simulations on Segmented Human MRI Hearts.

Understanding cardiac arrhythmic mechanisms and developing new strategies to control and terminate them using computer simulations requires realistic physiological cell models with anatomically accurate heart structures. Furthermore, numerical simulations must be fast enough to study and validate model and structure parameters. Here, we present an interactive parallel approach for solving detailed cell dynamics in high-resolution human heart structures with a local PC's GPU. In vitro human heart MRI scans were manually segmented to produce 3D structures with anatomically realistic electrophysiology. The Abubu.js library was used to create an interactive code to solve the OVVR human ventricular cell model and the FDA extension of the model in the human MRI heart structures, allowing the simulation of reentrant waves and investigation of their dynamics in real time. Interactive simulations of a physiological cell model in a detailed anatomical human heart reveals propagation of waves through the fine structures of the trabeculae and pectinate muscle that can perpetuate arrhythmias, thereby giving new insights into effects that may need to be considered when planning ablation and other defibrillation methods.

Cellular S-value evaluation based on real human cell models using the GATE MC package

Exploring the spatial distribution of the energy loss of ionising radiation at the subcellular level is indispensable for evaluating the radiobiological effects of targeted radionuclide therapy accurately. Believing that S-values are important for obtaining the target dose, the Committee on Medical Internal Radiation Dose (MIRD) proposed a method to obtain the cellular dosimetric parameter. However, most available data on cellular S-values were calculated based on simple geometric models, such as ellipsoids or spheres, which do not accurately reflect biological reality. To investigate the influence of the cellular model on S-values, calculations were performed for two kinds of polygon-surface phantom models of realistic, individual human cells, the lung epithelial cell model (the B2B Phantom model) and the hepatocyte model (the Liver Phantom model), using the Monte Carlo (MC) software package GATE. To analyse the influence of cell geometry on the final S-value, the differences in the S-values between the realistic cell models and simple geometric sphere and ellipsoid models with similar volumes were calculated and compared for six different combinations of source and target regions. The irradiation conditions were 0.01–1.10 MeV monoenergetic electron sources and the Auger electronic therapy nuclides Ga-67, Tc-99m, In-111, I-125 and Tl-201, which are commonly used in nuclear medicine. The S-values calculated in this study are different from the results of the simple geometry models proposed by previous researchers. Two more precise polygon-surface phantom models of realistic, individual human cells were used, which provided more accurate information about the cell dose and will be very useful for the diagnostic application of radiotherapy in the future.

Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes.

Measurement of nuclear-to-cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely heavily on immunofluroescent staining and manual reading. To achieve the goal of rapid and label-free cell classification, realistic optical cell models (OCMs) have been developed for simulation of diffraction imaging by single cells. A total of 1892 OCMs were obtained with varied nuclear volumes and orientations to calculate cross-polarized diffraction image (p-DI) pairs divided into three nuclear size groups of OCMS , OCMO and OCML based on three prostate cell structures. Binary classifications were conducted among the three groups with image parameters extracted by the algorithm of gray-level co-occurrence matrix. The averaged accuracy of support vector machine (SVM) classifier on test dataset of p-DI was found to be 98.8% and 97.5% respectively for binary classifications of OCMS vs OCMO and OCMO vs OCML for the prostate cancer cell structure. The values remain about the same at 98.9% and 97.8% for the smaller prostate normal cell structures. The robust performance of SVM over clustering classifiers suggests that the high-order correlations of diffraction patterns are potentially useful for label-free detection of single cells with large N:C ratios.

Three-dimensional traction microscopy accounting for cell-induced matrix degradation

Tractions exerted by cells on the extracellular matrix (ECM) are critical in many important physiological and pathological processes such as embryonic morphogenesis, wound healing, and cancer metastasis. Three-dimensional Traction Microscopy (3DTM) is a tool to quantify cellular tractions by first measuring the displacement field in the ECM in response to these tractions, and then using this measurement to infer tractions. Most applications of 3DTM have assumed that the ECM has spatially-uniform mechanical properties, but cells secrete enzymes that can locally degrade the ECM. In this work, a novel computational method is developed to quantify both cellular tractions and ECM degradation. In particular, the ECM is modeled as a hyperelastic, Neo-Hookean solid, whose material parameters are corrupted by a single degradation parameter. The feasibility of determining both the traction and the degradation parameter is first demonstrated by showing the existence and uniqueness of the solution. An inverse problem is then formulated to determine the nodal values of the traction vector and the degradation parameter, with the objective of minimizing the difference between a predicted and measured displacement field, under the constraint that the predicted displacement field satisfies the equation of equilibrium. The inverse problem is solved by means of a gradient-based optimization approach, and the gradient is computed efficiently using appropriately derived adjoint fields. The computational method is validated in-silico using a geometrically realistic neuronal cell model and synthetic traction and degradation fields. It is found that the method accurately recovers both the traction and degradation fields. Moreover, it is found that neglecting ECM degradation can yield significant errors in traction measurements. Our method can extend the range of context where tractions can be appropriately measured.

Disentangling astroglial physiology with a realistic cell model in silico

Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we systematically incorporate multi-scale, tri-dimensional astroglial architecture into a realistic multi-compartmental cell model, which we constrain by empirical tests and integrate into the NEURON computational biophysical environment. This approach is implemented as a flexible astrocyte-model builder ASTRO. As a proof-of-concept, we explore an in silico astrocyte to evaluate basic cell physiology features inaccessible experimentally. Our simulations suggest that currents generated by glutamate transporters or K+ channels have negligible distant effects on membrane voltage and that individual astrocytes can successfully handle extracellular K+ hotspots. We show how intracellular Ca2+ buffers affect Ca2+ waves and why the classical Ca2+ sparks-and-puffs mechanism is theoretically compatible with common readouts of astroglial Ca2+ imaging.

ARACHNE: A neural-neuroglial network builder with remotely controlled parallel computing.

Creating and running realistic models of neural networks has hitherto been a task for computing professionals rather than experimental neuroscientists. This is mainly because such networks usually engage substantial computational resources, the handling of which requires specific programing skills. Here we put forward a newly developed simulation environment ARACHNE: it enables an investigator to build and explore cellular networks of arbitrary biophysical and architectural complexity using the logic of NEURON and a simple interface on a local computer or a mobile device. The interface can control, through the internet, an optimized computational kernel installed on a remote computer cluster. ARACHNE can combine neuronal (wired) and astroglial (extracellular volume-transmission driven) network types and adopt realistic cell models from the NEURON library. The program and documentation (current version) are available at GitHub repository https://github.com/LeonidSavtchenko/Arachne under the MIT License (MIT).

Effect of the form and anisotropy of the left ventricle on the drift of spiral waves

Three-dimensional spiral waves of electrical excitation in the myocardium are sources of dangerous cardiac arrhythmias. In this work, the dynamics of spiral waves of electrical excitation were studied in a symmetric anatomical model of the human heart left ventricle and a realistic ionic cell model of the human ventricular myocardium. Three factors that affect the drift waves in the heart were compared for the first time: the geometry of the heart wall, myocardial anisotropy, and wave chirality. Cardiac anisotropy was identified as a main factor in determining the drift of spiral waves. In the isotropic case, the dynamics were determined by the wall thickness, but did not depend on the wave chirality. In the anisotropic case, chirality was found to play a crucial role.

Diffusion through thin membranes: Modeling across scales.

From macroscopic to microscopic scales it is demonstrated that diffusion through membranes can be modeled using specific boundary conditions across them. The membranes are here considered thin in comparison to the overall size of the system. In a macroscopic scale the membrane is introduced as a transmission boundary condition, which enables an effective modeling of systems that involve multiple scales. In a mesoscopic scale, a numerical lattice-Boltzmann scheme with a partial-bounceback condition at the membrane is proposed and analyzed. It is shown that this mesoscopic approach provides a consistent approximation of the transmission boundary condition. Furthermore, analysis of the mesoscopic scheme gives rise to an expression for the permeability of a thin membrane as a function of a mesoscopic transmission parameter. In a microscopic model, the mean waiting time for a passage of a particle through the membrane is in accordance with this permeability. Numerical results computed with the mesoscopic scheme are then compared successfully with analytical solutions derived in a macroscopic scale, and the membrane model introduced here is used to simulate diffusive transport between the cell nucleus and cytoplasm through the nuclear envelope in a realistic cell model based on fluorescence microscopy data. By comparing the simulated fluorophore transport to the experimental one, we determine the permeability of the nuclear envelope of HeLa cells to enhanced yellow fluorescent protein.

Realistic optical cell modeling and diffraction imaging simulation for study of optical and morphological parameters of nucleus.

Coherent light scattering presents complex spatial patterns that depend on morphological and molecular features of biological cells. We present a numerical approach to establish realistic optical cell models for generating virtual cells and accurate simulation of diffraction images that are comparable to measured data of prostate cells. With a contourlet transform algorithm, it has been shown that the simulated images and extracted parameters can be used to distinguish virtual cells of different nuclear volumes and refractive indices against the orientation variation. These results demonstrate significance of the new approach for development of rapid cell assay methods through diffraction imaging.

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-Imaging.

With its single cell sensitivity over volumes as large as or larger than a mouse, cryo-imaging enables imaging of stem cell biodistribution, homing, engraftment, and molecular mechanisms. We developed and evaluated a highly automated software tool to detect fluorescently labeled stem cells within very large (∼200 GB) cryo-imaging datasets. Cell detection steps are: preprocess, remove immaterial regions, spatially filter to create features, identify candidate pixels, classify pixels using bagging decision trees, segment cell patches, and perform 3D labeling. There are options for analysis and visualization. To train the classifier, we created synthetic images by placing realistic digital cell models onto cryo-images of control mice devoid of cells. Very good cell detection results were (precision=98.49%, recall=99.97%) for synthetic cryo-images, (precision=97.81%, recall=97.71%) for manually evaluated, actual cryo-images, and false positives in control mice. An α-multiplier applied to features allows one to correct for experimental variations in cell brightness due to labeling. On dim cells (37% of standard brightness), with correction, we improved recall (49.26%→ 99.36%) without a significant drop in precision (99.99%→ 99.75%) . With tail vein injection, multipotent adult progenitor cells in a graft-versus-host-disease model in the first days post injection were predominantly found in lung, liver, spleen, and bone marrow. Distribution was not simply related to blood flow. The lung contained clusters of cells while other tissues contained single cells. Our methods provided stem cell distribution anywhere in mouse with single cell sensitivity. Methods should provide a rational means of evaluating dosing, delivery methods, cell enhancements, and mechanisms for therapeutic cells.

Genomic approaches for understanding the genetics of complex disease.

There are thousands of known associations between genetic variants and complex human phenotypes, and the rate of novel discoveries is rapidly increasing. Translating those associations into knowledge of disease mechanisms remains a fundamental challenge because the associated variants are overwhelmingly in noncoding regions of the genome where we have few guiding principles to predict their function. Intersecting the compendium of identified genetic associations with maps of regulatory activity across the human genome has revealed that phenotype-associated variants are highly enriched in candidate regulatory elements. Allele-specific analyses of gene regulation can further prioritize variants that likely have a functional effect on disease mechanisms; and emerging high-throughput assays to quantify the activity of candidate regulatory elements are a promising next step in that direction. Together, these technologies have created the ability to systematically and empirically test hypotheses about the function of noncoding variants and haplotypes at the scale needed for comprehensive and systematic follow-up of genetic association studies. Major coordinated efforts to quantify regulatory mechanisms across genetically diverse populations in increasingly realistic cell models would be highly beneficial to realize that potential.

Forced convection through open cell foams based on homogenization approach: Steady state analysis

To investigate thermal transportation through open cell foams there are various microscopic and macroscopic numerical models along with their limitations available in the literature. The purpose of this study is to investigate the limitations of macroscopic models and to propose some reliable ideas as conclusion that can be used to overcome the existing limitations. Therefore, a combined experimental and numerical study is presented. The experimental study comprises of steady state forced convection experiments which involve three different regimes of heat transfer. Further, in macroscopic models these three regimes of heat transfer namely conduction, thermal dispersion and interstitial convection are governed by stagnant effective thermal conductivity, ke, dispersion conductivity, kd and volumetric heat transfer coefficient, hv respectively. Moreover, the complex structure of the open cell foams is simplified into a rather realistic Kelvin cell model for the determination of ke. The influence of the geometrical parameters such as pore diameter, d and foam porosity, ε is investigated by examining 10, 20 and 30 PPI (pores per inch) alumina foams for a porosity range of 0.79–0.87. The findings of this study reveal that it is important to consider both local thermal non-equilibrium (LTNE) and thermal dispersion together for improved analysis. Further, it is revealed that although with the above consideration, it is possible to exhibit the effect of geometrical parameters on each regime of heat transfer but the accuracy of the results predicted through macroscopic models remain under question as there is no basis to validate the results.

The cytoplasm as a radiation target: an in silico study of microbeam cell irradiation

We performed in silico microbeam cell irradiation modelling to quantitatively investigate ionisations resulting from soft x-ray and alpha particle microbeams targeting the cytoplasm of a realistic cell model. Our results on the spatial distribution of ionisations show that as x-rays are susceptible to scatter within a cell that can lead to ionisations in the nucleus, soft x-ray microbeams may not be suitable for investigating the DNA damage response to radiation targeting the cytoplasm alone. In contrast, ionisations from an ideal alpha microbeam are tightly confined to the cytoplasm, but a realistic alpha microbeam degrades upon interaction with components upstream of the cellular target. Thus it is difficult to completely rule out a contribution from alpha particle hits to the nucleus when investigating DNA damage response to cytoplasmic irradiation. We find that although the cytoplasm targeting efficiency of an alpha microbeam is better than that of a soft x-ray microbeam (the probability of stray alphas hitting the nucleus is 0.2% compared to 3.6% for x-rays), stray alphas produce more ionisations in the nucleus and thus have greater potential for initiating damage responses therein. Our results suggest that observed biological responses to cytoplasmic irradiation include a small component that can be attributed to stray ionisations in the nucleus resulting from the stochastic nature of particle interactions that cause out-of-beam scatter. This contribution is difficult to isolate experimentally, thus demonstrating the value of the in silico approach.

Limiting Light Escape Angle in Silicon Photovoltaics: Ideal and Realistic Cells

Restricting the light escape angle within a solar cell significantly enhances light trapping, resulting in potentially higher efficiency in thinner cells. Using an improved detailed balance model for silicon and neglecting diffuse light, we calculate an efficiency gain of 3% <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">abs</sub> for an ideal Si cell of 3-μm thickness and the escape angle restricted to 2.767° under AM1.5 direct illumination. Applying the model to current high-efficiency cell technologies, we find that a heterojunction-type device with better surface and contact passivation is better suited to escape angle restriction than a homojunction type device. In these more realistic cell models, we also find that there is little benefit gained by restricting the escape angle to less than 10°. The benefits of combining moderate escape angle restriction with low to moderate concentration offers further efficiency gains. Finally, we consider two potential structures for escape angle restriction: a narrowband graded index optical multilayer and a broadband ray optical structure. The broadband structure, which provides greater angle restriction, allows for higher efficiencies and much thinner cells than the narrowband structure.

Light trapping efficiency of periodic and quasiperiodic back-reflectors for thin film solar cells: A comparative study

Recently, great efforts have been carried out to design optimized metallic nano-grating back-reflectors to improve the light absorption in thin film solar cells. In this work, we compare the performances of deterministic aperiodic backreflectors in the form of 1-D nanogratings based on the generalized Fibonacci deterministic aperiodic sequence with a standard periodic one. The case of study here analyzed relies on a realistic solar cell model, where light absorption is evaluated only in the intrinsic region of an amorphous silicon P-I-N junction. We found that the results of comparison are strongly influenced by the amorphous silicon extinction coefficient within the near-infrared wavelength range, where most photonic-plasmonic modes (responsible for the light absorption enhancement typically observed when structured metal nanogratings are employed) are excited. In particular, with device-grade hydrogenated amorphous silicon, we demonstrate that Fibonacci-like backreflectors are able to provide an absorption enhancement of about 4% and 20% with respect to periodic and flat metallic backreflectors, respectively. We also found that aperiodic gratings guarantee better results in terms of robustness to the incident angle of the incoming radiation. Overall, our results confirm that aperiodic geometries are effectively able to offer some intriguing perspectives to enhance light trapping capability in thin film solar cells especially thanks to the large set of patterns employable to enable a proper design of resonant modes number and their spectral locations.