Translating myosin-binding protein C and titin abnormalities to whole-heart function using a novel calcium-contraction coupling model

Mutations in cardiac myosin-binding protein C (cMyBP-C) or titin may respectively lead to hypertrophic (HCM) or dilated (DCM) cardiomyopathies. The mechanisms leading to these phenotypes remain unclear because of the challenge of translating cellular abnormalities to whole-heart and system function. We developed and validated a novel computer model of calcium-contraction coupling incorporating the role of cMyBP-C and titin based on the key assumptions: 1) tension in the thick filament promotes cross-bridge attachment mechanochemically, 2) with increasing titin tension, more myosin heads are unlocked for attachment, and 3) cMyBP-C suppresses cross-bridge attachment. Simulated stationary calcium-tension curves, isotonic and isometric contractions, and quick release agreed with experimental data. The model predicted that a loss of cMyBP-C function decreases the steepness of the calcium-tension curve, and that more compliant titin decreases the level of passive and active tension and its dependency on sarcomere length. Integrating this cellular model in the CircAdapt model of the human heart and circulation showed that a loss of cMyBP-C function resulted in HCM-like hemodynamics with higher left ventricular end-diastolic pressures and smaller volumes. More compliant titin led to higher diastolic pressures and ventricular dilation, suggesting DCM-like hemodynamics. The novel model of calcium-contraction coupling incorporates the role of cMyBP-C and titin. Its coupling to whole-heart mechanics translates changes in cellular calcium-contraction coupling to changes in cardiac pump and circulatory function and identifies potential mechanisms by which cMyBP-C and titin abnormalities may develop into HCM and DCM phenotypes. This modeling platform may help identify distinct mechanisms underlying clinical phenotypes in cardiac diseases.

Dendritic effects of tDCS insights from a morphologically realistic model neuron

Transcranial direct current stimulation (tDCS) induces subcellular compartmental-dependent polarization, maximal in the distal portions of axons and dendrites. Using a morphologically realistic neuron model, we simulated tDCS-induced membrane polarization of the apical dendrite. Thus, we investigated the differential dendritic effects of anodal and cathodal tDCS on membrane potential polarization along the dendritic structure and its subsequent effects on dendritic membrane resistance, excitatory postsynaptic potential amplitude, backpropagating action potential amplitude, input/output relations, and long-term synaptic plasticity. We further showed that the effects of anodal and cathodal tDCS on the backpropagating action potential were asymmetric, and explained this asymmetry. Additionally, we showed that the effects on input/output relations were rather weak and limited to the low-mid range of stimulation frequencies, and that synaptic plasticity effects were mostly limited to the distal portion of the dendrite. Thus, we demonstrated how tDCS modifies dendritic physiology due to the dendrite's unique morphology and composition of voltage-gated ion channels.

Emergence of brain-inspired small-world spiking neural network through neuroevolution

Studies suggest that the brain's high efficiency and low energy consumption may be closely related to its small-world topology and critical dynamics. However, existing efforts on the performance-oriented structural evolution of spiking neural networks (SNNs) are time-consuming and ignore the core structural properties of the brain. Here, we introduce a multi-objective Evolutionary Liquid State Machine (ELSM), which blends the small-world coefficient and criticality to evolve models and guide the emergence of brain-inspired, efficient structures. Experiments reveal ELSM's consistent and comparable performance, achieving 97.23% on NMNIST and outperforming LSM models on MNIST and Fashion-MNIST with 98.12% and 88.81% accuracies, respectively. Further analysis shows its versatility and spontaneous evolution of topologies such as hub nodes, short paths, long-tailed degree distributions, and numerous communities. This study evolves recurrent spiking neural networks into brain-inspired energy-efficient structures, showcasing versatility in multiple tasks and potential for adaptive general artificial intelligence.

Fully angularly resolved 3D microrheology with optical tweezers

Microrheology with optical tweezers (MOT) is an all-optical technique that allows the user to investigate a materials' viscoelastic properties at microscopic scales, and is particularly useful for those materials that feature complex microstructures, such as biological samples. MOT is increasingly being employed alongside 3D imaging systems and particle tracking methods to generate maps showing not only how properties may vary between different points in a sample but also how at a single point the viscoelastic properties may vary with direction. However, due to the diffraction limited shape of focussed beams, optical traps are inherently anisotropic in 3D. This can result in a significant overestimation of the fluids' viscosity in certain directions. As such, the rheological properties can only be accurately probed along directions parallel or perpendicular to the axis of trap beam propagation. In this work, a new analytical method is demonstrated to overcome this potential artefact. This is achieved by performing principal component analysis on 3D MOT data to characterise the trap, and then identify the frequency range over which trap anisotropy influences the data. This approach is initially applied to simulated data for a Newtonian fluid where the trap anisotropy induced maximum error in viscosity is reduced from ~ 150% to less than 6%. The effectiveness of the method is corroborated by experimental MOT measurements performed with water and gelatine solutions, thus confirming that the microrheology of a fluid can be extracted reliably across a wide frequency range and in any arbitrary direction. This work opens the door to fully spatially and angularly resolved 3D mapping of the rheological properties of soft materials over a broad frequency range.

From the outer ear to the nerve: A complete computer model of the peripheral auditory system

Computer models of the individual components of the peripheral auditory system - the outer, middle, and inner ears and the auditory nerve - have been developed in the past, with varying level of detail, breadth, and faithfulness of the underlying parameters. Building on previous work, we advance the modeling of the ear by presenting a complete, physiologically justified, bottom-up computer model based on up-to-date experimental data that integrates all of these parts together seamlessly. The detailed bottom-up design of the present model allows for the investigation of partial hearing mechanisms and their defects, including genetic, molecular, and microscopic factors. Also, thanks to the completeness of the model, one can study microscopic effects in the context of their implications on hearing as a whole, enabling the correlation with neural recordings and non-invasive psychoacoustic methods. Such a model is instrumental for advancing quantitative understanding of the mechanism of hearing, for investigating various forms of hearing impairment, as well as for devising next generation hearing aids and cochlear implants.

Brain signatures indexing variation in internal processing during perceptual decision-making

Brain activity is highly variable during a task. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimental manipulations. However, changes in internal processing could arise from many factors independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials. Subjects (N = 25) performed a motion discrimination task with six interleaved levels of coherence. Clustering identified two discrete subtypes of trials with different patterns of activity. Surprisingly, Subtype 1 occurred more frequently in trials with lower motion coherence but was associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight across-trial variability in decision processes traditionally hidden to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior.

Analysis of continuous enzyme kinetic data using ICEKAT

ICEKAT (Interactive Continuous Enzyme Analysis Tool) is an interactive web-based program for calculating initial rates and kinetic parameters (e.g., Vmax, kcat, KM, EC50, IC50) from continuous enzyme kinetic assay data that satisfy Michaelis-Menten and steady-state kinetic assumptions. ICEKAT is valuable in educational and research settings to consistently and accurately calculate initial rates and kinetic parameters, increasing assay veracity and reproducibility. Provided freely online to the scientific community, ICEKAT has been cited in at least 26 publications, and the initial journal article has been accessed nearly 9000 times since its debut in 2020 (Olp et al., 2020). Here, we provide in-depth instructions for software use, offer vital considerations for data analysis, and highlight updated software features for new and existing users. Through ICEKAT, we aim for the analysis of data from continuous enzyme kinetic studies worldwide to become more rapid, reliable, and repeatable. ICEKAT remains free of charge and available to all scientists at https://icekat.herokuapp.com/icekat; the source code for local use is found at https://github.com/SmithLabMCW/icekat.

Endothelial tissue remodeling induced by intraluminal pressure enhances paracellular solute transport

The endothelial layers of the microvasculature regulate the transport of solutes to the surrounding tissues. It remains unclear how this barrier function is affected by blood flow-induced intraluminal pressure. Using a 3D microvessel model, we compare the transport of macromolecules through endothelial tissues at mechanical rest or with intraluminal pressure, and correlate these data with electron microscopy of endothelial junctions. On application of an intraluminal pressure of 100 Pa, we demonstrate that the flow through the tissue increases by 2.35 times. This increase is associated with a 25% expansion of microvessel diameter, which leads to tissue remodeling and thinning of the paracellular junctions. We recapitulate these data with the deformable monopore model, in which the increase in paracellular transport is explained by the augmentation of the diffusion rate across thinned junctions under mechanical stress. We therefore suggest that the deformation of microvasculatures contributes to regulate their barrier function.

Study for skin gloss perception using computer modulation and frequency separation on surface reflection

OBJECTIVE

Skin gloss is a parameter of beauty and health and is used to evaluate the efficacy of makeup or skin care products. Skin gloss is a perception-based characteristic, which yet needs to be evaluated quantitatively and objectively. This study aimed to investigate the factors affecting the perception of matte, glossiness, and greasiness on Korean women's skin, focusing on the effect of surface reflection.

METHODS

In this study, a visual evaluation of skin gloss was performed using modulated images that combined facial reference (normal) images with images characterized by 64 different levels of surface-reflection to investigate factors that might affect the perception of matte, glossy, and greasy skin. Fifteen participants classified the respective images into three categories (matte, glossy, and greasy), and the intensity of each image was rated using a three-point scale (weak, moderate, or strong). A multinomial logistic regression analysis was performed using 960 data points (n = 64 × 15). A high-frequency average + 1 standard deviation (SD = 4) was set as the criterion for a highly glossy object.

RESULTS

A significant difference was found between the three categories considering four parameters: low-frequency mean (LFM) and SD (LFSD), and high-frequency mean (HFM) and SD (HFSD). The results of the multinomial logistic regression analysis showed that LFM and HFSD were significant influencing factors for perceiving matte, glossiness, and greasiness. As the LFM and HFSD of the images increased, perception changed from matte, to glossy and greasy. Further, the total number and total area of highly glossy objects were significantly different among the three categories.

CONCLUSION

We identified four different factors related to perceiving matte, glossiness, and greasiness on the faces of Korean women, including the LFM and HFSD of surface reflection, as well as the number of highly glossy objects and the area of surface reflection. Furthermore, we identified evenness of shine as a key factor for the perception of greasiness. These parameters could be useful for distinguishing skin gloss and for a quantitative evaluation of greasiness.

Design and evaluation of novel analogs of 2-amino-4-boronobutanoic acid (ABBA) as inhibitors of human gamma-glutamyl transpeptidase

Inhibitors of gamma-glutamyl transpeptidase (GGT1, aka gamma-glutamyl transferase) are needed for the treatment of cancer, cardiovascular illness and other diseases. Compounds that inhibit GGT1 have been evaluated in the clinic, but no inhibitor has successfully demonstrated specific and systemic GGT1 inhibition. All have severe side effects. L-2-amino-4‑boronobutanoic acid (l-ABBA), a glutamate analog, is the most potent GGT1 inhibitor in vitro. In this study, we have solved the crystal structure of human GGT1 (hGGT1) with ABBA bound in the active site. The structure was interrogated to identify interactions between the enzyme and the inhibitor. Based on these data, a series of novel ABBA analogs were designed and synthesized. Their inhibitory activity against the hydrolysis and transpeptidation activities of hGGT1 were determined. The lead compounds were crystalized with hGGT1 and the structures solved. The kinetic data and structures of the complexes provide new insights into the critical role of protein structure dynamics in developing compounds for inhibition of hGGT1.

What good is maths in studies of wound healing?

Wound healing is an aspect of normal physiology that we all take for granted until it goes wrong, such as, for example, the scarring that results from a severe burn, or those patients who suffer from debilitating chronic wounds that fail to heal. Ever since wound repair research began as a discipline, clinicians and basic scientists have collaborated to try and understand the cell and molecular mechanisms that underpin healthy repair in the hope that this will reveal clues for the therapeutic treatment of pathological healing. In recent decades mathematicians and physicists have begun to join in with this important challenge. Here we describe examples of how mathematical modeling married to biological experimentation has provided insights that biology alone could not fathom. To date, these studies have largely focused on wound re-epithelialization and inflammation, but we also discuss other components of wound healing that might be ripe for similar interdisciplinary approaches.

Towards a multi-scale computer modeling workflow for simulation of pulmonary ventilation in advanced COVID-19

Physics-based multi-scale in silico models offer an excellent opportunity to study the effects of heterogeneous tissue damage on airflow and pressure distributions in COVID-19-afflicted lungs. The main objective of this study is to develop a computational modeling workflow, coupling airflow and tissue mechanics as the first step towards a virtual hypothesis-testing platform for studying injury mechanics of COVID-19-afflicted lungs. We developed a CT-based modeling approach to simulate the regional changes in lung dynamics associated with heterogeneous subject-specific COVID-19-induced damage patterns in the parenchyma. Furthermore, we investigated the effect of various levels of inflammation in a meso-scale acinar mechanics model on global lung dynamics. Our simulation results showed that as the severity of damage in the patient's right lower, left lower, and to some extent in the right upper lobe increased, ventilation was redistributed to the least injured right middle and left upper lobes. Furthermore, our multi-scale model reasonably simulated a decrease in overall tidal volume as the level of tissue injury and surfactant loss in the meso-scale acinar mechanics model was increased. This study presents a major step towards multi-scale computational modeling workflows capable of simulating the effect of subject-specific heterogenous COVID-19-induced lung damage on ventilation dynamics.

Experimental and computational model approach to assess the photothermal effects in transparent nanocrystalline yttria stabilized zirconia cranial implant

BACKGROUND AND OBJECTIVE

In the last few years, we have been exploring the use of transparent nanocrystalline yttria-stabilized zirconia (nc-YSZ) ceramics as a biomedical transparent cranial implant, referred as the "Window to the Brain" (WttB). The WttB aims at providing chronical optical access to the brain for diagnostics and therapeutic procedures and it has shown to provide an effective means to obtain enhanced results from optical imaging techniques. The objective of this work is to explore the photothermal effects of the Wttb produced when it is irradiated by a laser source.

METHODS

We make experimental and computer models. The thermal effects of laser irradiation on the nc-YSZ samples were evaluated upon registering the induced temperature changes by means of thermal imaging. The computer models try to mimic the experimental models using a similar geometry, reproducing the physical situation by a couple thermal-optical problem and adjusting the main parameters from the experimental results.

RESULTS

Experimental and computational coincides in results: Temperatures at the bottom surface of the implant does not exceed those which produce thermal damage. The quantitative comparison between experimental and computational models show that differences in results are under a reasonable value of 5% and qualitatively we observe a similar behavior. The results provide optimum values for the thermal-optical nc-YSZ parameters considering a linear and exponential relationship with temperature for the absorption coefficient: The thermal conductivity is k = 2.13 W/m·K and the absorption coefficient α varies from 426 to 526 m^-1 with the linear relationship, and k = 2.04 W/m·K and α ∈ [433,502] m^-1 with the exponential. The reflection coefficient is R = 19% in both cases.

CONCLUSIONS

The temperatures achieved in the nc-YSZ during the laser irradiation are suitable for biomedical applications. The combination of experimental and computational models contributes to build a clinically oriented model with the thermal-optical parameters values stablished and to determine their influence in results. Specifically, the absorption coefficient of the nc-YSZ samples is the most influent parameter in the obtained temperatures. Moreover, this combination provides a method to evaluate the relevant thermal-optical parameters of nc-YSZ samples obtained with different manufacturing processes.

Mathematical models to study the biology of pathogens and the infectious diseases they cause

Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

Comparing transfer learning to feature optimization in microstructure classification

Human analysis of research data is slow and inefficient. In recent years, machine learning tools have advanced our capability to perform tasks normally carried out by humans, such as image segmentation and classification. In this work, we seek to further improve binary classification models for high-throughput identification of different microstructural morphologies. We utilize a dataset with limited observations (133 dendritic structures, 444 non-dendritic) and employ data augmentation via rotation and translation to enhance the dataset six-fold. Then, transfer learning is carried out using pre-trained networks VGG16, InceptionV3, and Xception achieving only moderate F1 scores (0.801–0.822). We hypothesize that feature engineering could yield better results than transfer learning alone. To test this, we employ a new nature-inspired feature optimization algorithm, the Binary Red Deer Algorithm (BRDA), to carry out binary classification and observe F1 scores in the range of 0.96.

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A dataset comprising two categories of micrographs has been prepared

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Transfer learning has been implemented for micrograph classification

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To improve upon the classification accuracy, we perform feature engineering

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Feature engineering has been performed using Binary Red Deer Algorithm

Biosafety-inspired structural optimization of triazolium ionic liquids based on structure-toxicity relationships

Ionic liquids (ILs), owing to their low vapor pressure and excellent solvating ability, are being increasingly applied in various industries to replace highly toxic organic solvents. They mainly pollute aquatic environment and soils, directly endangering eco-environment and human health. Therefore, it is critical to understand and optimize structural motifs of ILs with reduced toxicity. Considering human oral exposure is the major route, our investigations employed a human cell panel (modeling oral exposures) including human stomach (GES-1), intestinal (FHC), liver (HepG2) and kidney (HEK293) cells using a series of experimental and computational approaches to explore the cytotoxicity and molecular mechanism of ILs. We discovered that the cytotoxicity of triazolium and imidazolium ILs was human cell line-dependent with cytotoxicity in an order of FHC > GES-1 > HepG2 > HEK293. For this reason, a toxicity assay using a single cell line was highly inappropriate. Compared to anions (Br^-, OTs^-, OTMBS^-) we tested, the cation of ILs played a major role in causing cytotoxicity. Ionic liquids with cations having longer hydrophobic sidechains (IL09 vs. IL01) readily insert into cell membranes with enhanced membrane and lipidomic perturbations, induce cytotoxicity by triggering cell cycle arrest and apoptosis. Reducing sidechain length and incorporating three nitrogen atoms (triazolium) instead of two (imidazolium) in the cation core alleviated cytotoxicity by reducing cell membrane perturbations and cell function interference. These findings provide important guiding principles for the design of the next-generation of "green" and safe ILs.

Computer modeling of radiofrequency cardiac ablation: 30 years of bioengineering research

This review begins with a rationale of the importance of theoretical, mathematical and computational models for radiofrequency (RF) catheter ablation (RFCA). We then describe the historical context in which each model was developed, its contribution to the knowledge of the physics of RFCA and its implications for clinical practice. Next, we review the computer modeling studies intended to improve our knowledge of the biophysics of RFCA and those intended to explore new technologies. We describe the most important technical details of the implementation of mathematical models, including governing equations, tissue properties, boundary conditions, etc. We discuss the utility of lumped element models, which despite their simplicity are widely used by clinical researchers to provide a physical explanation of how RF power is absorbed in different tissues. Computer model verification and validation are also discussed in the context of RFCA. The article ends with a section on the current limitations, i.e. aspects not yet included in state-of-the-art RFCA computer modeling and on future work aimed at covering the current gaps.

Identification of Ca2+ oscillations with the 0-1 test for periodic and chaotic time-series: One- and two-parameter bifurcations

This paper examines the oscillatory responses (periodic and chaotic) of a biosystem store model for bursting and complex Ca²⁺ oscillations in which three compartments have been taken into consideration: the cytosol, endoplasmic reticulum (ER) and mitochondria. The oscillatory model is used to examine the reliability of the 0-1 test for chaos in the bifurcation analysis of continuous signals obtained when the frequencies of oscillatory responses vary significantly with a relatively small changes of the bifurcation parameters. The illustrative examples in both the one- and two-parameter cases are designed to show that for a periodic time-series the test's reliability may be questioned when a periodic series is classified as a chaotic one - the 'false-positive' case. To prevent the incorrect result an additional computational work is needed to examine the frequency spectrum of the periodic time-series. The illustrative examples utilize an autonomous dynamical model of cytosolic calcium oscillations with three dynamical variables and sixteen parameters. The dynamical model is such that the frequency of oscillations may change by the factor of about 200, when a certain dynamical system's parameter changes from its minimum to maximum values, making selection of the parameters in the 0-1 test extremely difficult. The extra computational work improves the test's reliability and eliminates the 'false-positive' outcomes of the test. The paper is focused on the computational aspects of the 0-1 test for periodic and chaotic oscillations rather than on the properties of the store model for bursting and complex Ca²⁺ oscillations.

Combining Computational Chemistry and Crystallography for a Better Understanding of the Structure of Cellulose

The approaches in this article seek to enhance understanding of cellulose at the molecular level, independent of the source and the particular crystalline form of cellulose. Four main areas of structure research are reviewed. Initially, the molecular shape is inferred from the crystal structures of many small molecules that have β-(1→4) linkages. Then, conformational analyses with potential energy calculations of cellobiose are covered, followed by the use of Atoms-In-Molecules theory to learn about interactions in experimental and theoretical structures. The last section covers models of cellulose nanoparticles. Controversies addressed include the stability of twofold screw-axis conformations, the influence of different computational methods, the predictability of crystalline conformations by studies of isolated molecules, and the twisting of model cellulose crystals.

Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives

Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for rationalizing drug interactions with their pharmacological targets. Despite the obvious utility and increasing impact of computational approaches, their development is not progressing at the same speed in different fields of pharmacology. Here, we review current in silico techniques used in cardiovascular diseases (CVDs), cardiological drug discovery, and assessment of cardiotoxicity. In silico techniques are paving the way to a new era in cardiovascular medicine, but their use somewhat lags behind that in other fields.

Effect of the relative position of electrode and stellate ganglion during thermal radiofrequency ablation: a simulation study

PURPOSE

Stellate ganglion (SG) block by thermal radiofrequency ablation (RFA) is frequently conducted as a therapeutic intervention for sympathetic-maintained and neuropathic pain syndromes. RFA's partial lack of effectiveness could be partly due to the ablation zone (AZ) not completely covering the SG section and therefore preventing the 'cutting' of the afferent pathways. Our objective was to build a theoretical model to conduct computer simulations to assess the effect of the electrode position relative to the SG.

METHODS

A three-dimensional model was built including the SG and adjacent tissues (vertebrae C7-T1-T2, trachea, carotid artery and vertebral artery). RFA (90-s, 80 °C) was simulated considering a 22 G-5 mm electrode. The AZ was computed using the 50 °C isotherm.

RESULTS

An electrode displacement of 2 mm in any direction from the optimal position (centered on the SG) meant that the AZ did not fully cover the SG section. Likewise, SG size considerably affected the RFA effectiveness since the AZ fully covered the section of small but not large SGs.

CONCLUSIONS

The findings suggest that the currently used SG RFA settings (i.e., 22 G-5 mm electrode, 90-s, 80 °C) may not be appropriate due to their inability to achieve an AZ that fully covers the SG cross section under certain circumstances, such as a large SG and non-optimal positioning of the RF electrode with respect to the SG center.

Three-dimensional morphodynamic simulations of macropinocytic cups

Macropinocytosis refers to the non-specific uptake of extracellular fluid, which plays ubiquitous roles in cell growth, immune surveillance, and virus entry. Despite its widespread occurrence, it remains unclear how its initial cup-shaped plasma membrane extensions form without any external solid support, as opposed to the process of particle uptake during phagocytosis. Here, by developing a computational framework that describes the coupling between the bistable reaction-diffusion processes of active signaling patches and membrane deformation, we demonstrated that the protrusive force localized to the edge of the patches can give rise to a self-enclosing cup structure, without further assumptions of local bending or contraction. Efficient uptake requires a balance among the patch size, magnitude of protrusive force, and cortical tension. Furthermore, our model exhibits cyclic cup formation, coexistence of multiple cups, and cup-splitting, indicating that these complex morphologies self-organize via a common mutually-dependent process of reaction-diffusion and membrane deformation.

MorphoSONIC: A morphologically structured intramembrane cavitation model reveals fiber-specific neuromodulation by ultrasound

Low-Intensity Focused Ultrasound Stimulation (LIFUS) holds promise for the remote modulation of neural activity, but an incomplete mechanistic characterization hinders its clinical maturation. Here we developed a computational framework to model intramembrane cavitation (a candidate mechanism) in multi-compartment, morphologically structured neuron models, and used it to investigate ultrasound neuromodulation of peripheral nerves. We predict that by engaging membrane mechanoelectrical coupling, LIFUS exploits fiber-specific differences in membrane conductance and capacitance to selectively recruit myelinated and/or unmyelinated axons in distinct parametric subspaces, allowing to modulate their activity concurrently and independently over physiologically relevant spiking frequency ranges. These theoretical results consistently explain recent empirical findings and suggest that LIFUS can simultaneously, yet selectively, engage different neural pathways, opening up opportunities for peripheral neuromodulation currently not addressable by electrical stimulation. More generally, our framework is readily applicable to other neural targets to establish application-specific LIFUS protocols.

Gallium-binding peptides as a tool for the sustainable treatment of industrial waste streams

Here we provide a proof of principle for an application-oriented concept for the peptide-based recovery of gallium in industrial wastewater, which was supported by biosorption studies with a real wastewater sample. We investigated the interaction of the gallium-binding peptides TMHHAAIAHPPH, NYLPHQSSSPSR, SQALSTSRQDLR, HTQHIQSDDHLA, and NDLQRHRLTAGP with gallium and arsenic through different experimental and computational approaches. Data obtained from isothermal titration microcalorimetry indicated a competitive influence by the presence of acetate ions with an exothermic contribution to the otherwise endothermic peptide gallium interactions. For peptide HTQHIQSDDHLA, a stabilizing influence of acetate ions on the metal peptide interaction was found. Peptide NYLPHQSSSPSR showed the highest affinity for gallium in ITC studies. Computational modeling of peptide NYLPHQSSSPSR was used to determine interaction parameters and to explain a possible binding mechanism. Furthermore, the peptides were immobilized on polystyrene beads. Thus, we created a novel and exceptionally robust peptide-based material for the biosorption of gallium from an aqueous solution. Data obtained from isothermal titration microcalorimetry indicated a competitive influence by the presence of acetate ions with an exothermic contribution to the otherwise endothermic peptide gallium interactions. For peptide HTQHIQSDDHLA, a stabilizing influence of acetate ions on the metal peptide interaction was found. Peptide NYLPHQSSSPSR showed the highest affinity for gallium in ITC studies. Computational modeling of peptide NYLPHQSSSPSR was used to determine interaction parameters and to explain a possible binding mechanism. Furthermore, the peptides were immobilized on polystyrene beads. Thus, we created a novel and exceptionally robust peptide-based material for the biosorption of gallium from an aqueous solution.

Short pulsed microwave ablation: computer modeling and ex vivo experiments

PURPOSE

To study the differences between continuous and short-pulse mode microwave ablation (MWA).

METHODS

We built a computational model for MWA including a 200 mm long and 14 G antenna from Amica-Gen and solved an electromagnetic-thermal coupled problem using COMSOL Multiphysics. We compared the coagulation zone (CZ) sizes created with pulsed and continuous modes under ex vivo and in vivo conditions. The model was used to compare long vs. short pulses, and 1000 W high-powered short pulses. Ex vivo experiments were conducted to validate the model.

RESULTS

The computational models predicted the axial diameter of the CZ with an error of 2-3% and overestimated the transverse diameter by 9-11%. For short pulses, the ex vivo computer modeling results showed a trend toward larger CZ when duty cycles decreases. In general, short pulsed mode yielded higher CZ diameters and volumes than continuous mode, but the differences were not significant (<5%), as in terms of CZ sphericity. The same trends were observed in the simulations mimicking in vivo conditions. Both CZ diameter and sphericity were similar with short and long pulses. Short 1000 W pulses produced smaller sphericity and similar CZ sizes under in vivo and ex vivo conditions.

CONCLUSIONS

The characteristics of the CZ created by continuous and pulsed MWA show no significant differences from ex vivo experiments and computer simulations. The proposed idea of enlarging coagulation zones and improving their sphericity in pulsed mode was not evident in this study.

Proposal for the binding mode of the 23-mer inhibitory peptide to myostatin

Inhibition of myostatin is a promising strategy for the treatment of amyotrophic disorders. Previously, we identified a minimum 23-mer peptide spanning positions 21-43 of a mouse myostatin precursor-derived prodomain and identified the nine key residues for effective myostatin inhibition through Ala scanning. We also reported the 23-mer peptides that show the propensity to form an α-helical structure around positions 32-36. Here, based on these findings, we conducted a docking simulation of a peptide-myostatin interaction. The results showed that by α-helix restraint docking of the 30-41 main chain, we obtained a proposed binding mode in which all nine of the key residues interact with myostatin. By analyzing the binding mode of four proposed docking models, we identified six of the myostatin residues that play an important role in the interaction with the peptide. This result provides a valuable insight into the relationship between myostatin and peptide interaction sites and may help in the design of future inhibitors.

Left-axis deviation in patients with nonischemic heart failure and left bundle branch block is a purely electrical phenomenon

BACKGROUND

Possible mechanisms of left-axis deviation (LAD) in the setting of left bundle branch block (LBBB) include differences in cardiac electrophysiology, structure, or anatomic axis.

OBJECTIVE

The purpose of this study was to clarify the mechanism(s) responsible for LAD in patients with LBBB.

METHODS

Twenty-nine patients with nonischemic cardiomyopathies and LBBB underwent noninvasive electrocardiographic imaging (ECGi), cardiac computed tomography, and magnetic resonance imaging in order to define ventricular electrical activation, characterize cardiac structure, and determine the cardiac anatomic axis.

RESULTS

Sixteen patients had a normal QRS axis (NA) (mean axis 8° ± 23°), whereas 13 patients had LAD (mean axis -48° ± 13°; P <.001). Total activation times were longer in the LAD group (112 ± 25 ms vs 91 ± 14 ms; P = .01) due to delayed activation of the basal anterolateral region (107 ± 10 ms vs 81 ± 17 ms; P <.001). Left ventricular (LV) activation in patients with LAD was from apex to base, in contrast to a circumferential pattern of activation in patients with NA. Apex-to-base delay was longer in the LA group (95 ± 13 ms vs 64 ± 21 ms; P <.001) and correlated with QRS frontal axis (R² = 0.67; P <.001). Both groups were comparable with regard to LV end-diastolic volume (295 ± 84 mL vs LAD 310 ± 91 mL; P = .69), LV mass (177 ± 33 g vs LAD 180 ± 37 g; P = .83), and anatomic axis.

CONCLUSION

LAD in LBBB appears to be due to electrophysiological abnormalities rather than structural factors or cardiac anatomic axis.

Fractional and fractal processes applied to cryptocurrencies price series

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The dynamics of the cryptocurrencies is analyzed.

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Several tools for investigating the dynamic behavior of the cryptocurrencies are adopted.

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Characteristics of the cryptocurrencies price time series such as persistence, randomness, predictability and chaoticity are addressed.

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With exception of for the Bitcoin, the other five cryptocurrencies analyzed are mean reverting.

Introduction

Cryptocurrencies have been attracting the attention from media, investors, regulators and academia during the last years. In spite of some scepticism in the financial area, cryptocurrencies are a relevant subject of academic research.

Objectives

In this paper, several tools are adopted as an instrument that can help market agents and investors to more clearly assess the cryptocurrencies price dynamics and, thus, guide investment decisions more assertively while mitigating risks.

Methods

We consider three methods, namely the Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) and Detrended Fluctuation Analysis, and three indices given by the Hurst and Lyapunov exponents or the Fractal Dimension. This information allows assessing the behaviour of the time series, such as their persistence, randomness, predictability and chaoticity.

Results

The results suggest that, except for the Bitcoin, the other cryptocurrencies exhibit the characteristic of mean reverting, showing a lower predictability when compared to the Bitcoin. The results for the Bitcoin also indicate a persistent behavior that is related to the long memory effect.

Conclusions

The ARFIMA reveals better predictive performance than the ARIMA for all cryptocurrencies. Indeed, the obtained residual values for the ARFIMA are smaller for the auto and partial auto correlations functions, as well as for confidence intervals.

Temperature-dependent regulation of electron transport and ATP synthesis in chloroplasts in vitro and in silico

The significance of temperature-dependent regulation of photosynthetic apparatus (PSA) is determined by the fact that plant temperature changes with environmental temperature. In this work, we present a brief overview of temperature-dependent regulation of photosynthetic processes in class B chloroplasts (thylakoids) and analyze these processes using a computer model that takes into account the key stages of electron and proton transport coupled to ATP synthesis. The rate constants of partial reactions were parametrized on the basis of experimental temperature dependences of partial photosynthetic processes: (1) photosystem II (PSII) turnover and plastoquinone (PQ) reduction, (2) the plastoquinol (PQH₂) oxidation by the cytochrome (Cyt) b₆f complex, (3) the ATP synthase activity, and (4) the proton leak from the thylakoid lumen. We consider that PQH₂ oxidation is the rate-limiting step in the intersystem electron transport. The parametrization of the rate constants of these processes is based on earlier experimental data demonstrating strong correlations between the functional and structural properties of thylakoid membranes that were probed with the lipid-soluble spin labels embedded into the membranes. Within the framework of our model, we could adequately describe a number of experimental temperature dependences of photosynthetic reactions in thylakoids. Computer modeling of electron and proton transport coupled to ATP synthesis supports the notion that PQH₂ oxidation by the Cyt b₆f complex and proton pumping into the lumen are the basic temperature-dependent processes that determine the overall electron flux from PSII to molecular oxygen and the net ATP synthesis upon variations of temperature. The model describes two branches of the temperature dependence of the post-illumination reduction of [Formula: see text] characterized by different activation energies (about 60 and ≤ 3.5 kJ mol^-1). The model predicts the bell-like temperature dependence of ATP formation, which arises from the balance of several factors: (1) the thermo-induced acceleration of electron transport through the Cyt b₆f complex, (2) deactivation of PSII photochemistry at sufficiently high temperatures, and (3) acceleration of the passive proton outflow from the thylakoid lumen bypassing the ATP synthase complex. The model describes the temperature dependence of experimentally measured parameter P/2e, determined as the ratio between the rates of ATP synthesis and pseudocyclic electron transport (H₂O → PSII → PSI → O₂).

Construction of a sustainable/controlled-release nano-container of non-toxic corrosion inhibitors for the water-based siliconized film: Estimating the host-guest interactions/desorption of inclusion complexes of cerium acetylacetonate (CeA) with beta-cyclodextrin (β-CD) via detailed electronic/atomic-scale computer modeling and experimental methods

Utilization of the coatings with self-healing anti-corrosion activities is one of the most promising routes for the development of advanced anti-corrosion coatings. In the present work, the green/sustainable corrosion inhibitive compounds based on the cerium acetylacetonate (CeA) was loaded into a beta-cyclodextrin (β-CD) nano-container (with negligible hazardous impacts) and through combined computer modeling and experimental approaches, the host-guest interactions/desorptions of the inclusion complexes of CeA with beta-cyclodextrin (β-CD) were assessed. The inhibition performance of the β-CD-CeA inclusion complex was investigated by electrochemical and surface experiments in a saline solution (NaCl, 3.5 wt.%). The particles were analyzed by Raman, XRD, FT-IR, and UV-vis spectroscopies. Additionally, the thermal properties in the 30-600 °C temperature range were examined by employing TGA/DTG test, and via the ICP analysis, the concentration of the released inorganic compounds in the electrolyte was studied. Achievements demonstrated 24 ppm Ce element existence after introducing β-CD-CeA inclusion complexes (during 24 h) in NaCl 3.5 wt.% solution. The analysis of Tafel curves proved that the prepared β-CD-CeA inclusion complex could inhibit the metallic substrate corrosion following the mixed cathodic and anodic mechanisms. The EIS investigation disclosed about 82 % inhibition degree after 48 h of metal immersion in the solution containing β-CD-CeA extract. The EIS analysis clarified that the silane coating (SC) resistance was enhanced noticeably by introducing the β-CD-CeA particles into the SC matrix. Using detailed-level (i.e., electronic and atomic) computer modeling techniques applying density functional theory (DFT), Mote Carlo (MC) and molecular dynamics (MD), the active sites, and the adsorption propensity of CeA complexes over the steel-based metallic adsorbents were explored. These modelings evidenced the CeA complexes interfacial adsorption on the steel.

An epidemic model SIPHERD and its application for prediction of the spread of COVID-19 infection in India

Originating from Wuhan, China, in late 2019, and with a gradual spread in the last few months, COVID-19 has become a pandemic crossing 9 million confirmed positive cases and 450 thousand deaths. India is not only an overpopulated country but has a high population density as well, and at present, a high-risk nation where COVID-19 infection can go out of control. In this paper, we employ a compartmental epidemic model SIPHERD for COVID-19 and predict the total number of confirmed, active and death cases, and daily new cases. We analyze the impact of lockdown and the number of tests conducted per day on the prediction and bring out the scenarios in which the infection can be controlled faster. Our findings indicate that increasing the tests per day at a rapid pace (10k per day increase), stringent measures on social-distancing for the coming months and strict lockdown in the month of July all have a significant impact on the disease spread.

Thermal impact of replacing constant voltage by low-frequency sine wave voltage in RF ablation computer modeling

BACKGROUND AND OBJECTIVES

A constant voltage (DC voltage) is usually used in radiofrequency ablation (RFA) computer models to mimic the radiofrequency voltage. However, in some cases a low frequency sine wave voltage (AC voltage) may be used instead. Our objective was to assess the thermal impact of replacing DC voltage by low-frequency AC voltage in RFA computer modeling.

METHODS

A 2D model was used consisting of an ablation electrode placed perpendicular to the tissue fragment. The Finite Element method was used to solve a coupled electric-thermal problem. Quasi-static electrical approximation was implemented in two ways (both with equivalent electrical power): (1) by a constant voltage of 25 V in the ablation electrode (DC voltage), and (2) applying a sine waveform with peak amplitude of 25√2 V (AC voltage). The frequency of the sine signal (f_AC) varied from 0.5 Hz to 50 Hz.

RESULTS

Sine wave thermal oscillations (at twice the f_AC frequency) were observed in the case of AC voltage, in addition to the temperature obtained by DC voltage. The amplitude of the oscillations: (1) increased with temperature, remaining more or less constant after 30 s; (2) was of up to ±3 °C for very low f_AC values (0.5 Hz); and (3) was reduced at higher f_AC values and with distance from the electrode (almost negligible for distances > 5 mm). The evolution of maximum lesion depth and width were almost identical with both DC and AC.

CONCLUSIONS

Although reducing f_AC reduces the computation time, thermal oscillations appear at points near the electrode, which suggests that a minimum value of f_AC should be used. Replacing DC voltage by low-frequency AC voltage does not appear to have an impact on the lesion depth.

Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective

The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model’s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the development and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee’s multidisciplinary membership, followed by a large stakeholder community survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing implementations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.

Chronic defects in intraspinal mechanisms of spike encoding by spinal motoneurons following chemotherapy

Chemotherapy-induced sensorimotor disabilities, including gait and balance disorders, as well as physical fatigue often persist for months and sometimes years into disease free survival from cancer. While associated with impaired sensory function, chronic sensorimotor disorders might also depend on chemotherapy-induced defects in other neuron types. In this report, we extend consideration to motoneurons, which, if chronically impaired, would necessarily degrade movement behavior. The present study was undertaken to determine whether motoneurons qualify as candidate contributors to chronic sensorimotor disability independently from sensory impairment. We tested this possibility in vivo from rats 5 weeks following human-scaled treatment with one of the platinum-based compounds, oxaliplatin, widely used in chemotherapy for a variety of cancers. Action potential firing of spinal motoneurons responding to different fixed levels of electrode-current injection was measured in order to assess the neurons' intrinsic capacity for stimulus encoding. The encoding of stimulus duration and intensity corroborated in untreated control rats was severely degraded in oxaliplatin treated rats, in which motoneurons invariably exhibited erratic firing that was unsustained, unpredictable from one stimulus trial to the next, and unresponsive to changes in current strength. Direct measurements of interspike oscillations in membrane voltage combined with computer modeling pointed to aberrations in subthreshold conductances as a plausible contributor to impaired firing behavior. These findings authenticate impaired spike encoding as a candidate contributor to, in the case of motoneurons, deficits in mobility and fatigue. Aberrant firing also becomes a deficit worthy of testing in other CNS neurons as a potential contributor to perceptual and cognitive disorders induced by chemotherapy in patients.

A PD-1 peptide antagonist exhibits potent anti-tumor and immune regulatory activity

Antibodies blocking the PD-1/PD-L1 pathway have achieved great success. However, some disadvantages of antibodies have been found, which limit their clinical applications. Peptide antagonists are alternatives to antibodies in PD-1/PD-L1 blockage, but successful studies in this area are limited. A PD-1 targeting peptide, P-F4, was identified using phage display. P-F4 bound PD-1 with an affinity of 0.119 μM, inhibited PD-1/PD-L1 interaction at the cellular level and modulated T cell activity in vitro. We have overcome the poor solubility and rapid degradation problems of this peptide by packaging P-F4 in nanoparticles. In vivo experiments demonstrated that P-F4 nanoparticles could strongly inhibit tumor growth in a CT26 mouse model. Further research revealed that treatment of P-F4 nanoparticles increased CD8+T cells and reduced Tregs in the tumor microenvironment and tumor-draining lymph nodes. It was shown that treatment of P-F4 nanoparticles also increased lymphocytic activities, including proliferation, cytokine secretion and cytolytic activity. Moreover, computer modeling suggested that the P-F4 binding site to PD-1 overlaps with the PD-L1 binding surface. In this study, a peptide candidate for cancer immunotherapy was provided, and its working mechanisms were studied.

Small-conductance Ca2+-activated K+ channels promote J-wave syndrome and phase 2 reentry

BACKGROUND

Small-conductance Ca²⁺-activated potassium (SK) channels play complex roles in cardiac arrhythmogenesis. SK channels colocalize with L-type Ca²⁺ channels, yet how this colocalization affects cardiac arrhythmogenesis is unknown.

OBJECTIVE

The purpose of this study was to investigate the role of colocalization of SK channels with L-type Ca²⁺ channels in promoting J-wave syndrome and ventricular arrhythmias.

METHODS

We carried out computer simulations of single-cell and tissue models. SK channels in the model were assigned to preferentially sense Ca²⁺ in the bulk cytosol, subsarcolemmal space, or junctional cleft.

RESULTS

When SK channels sense Ca²⁺ in the bulk cytosol, the SK current (I_SK) rises and decays slowly during an action potential, the action potential duration (APD) decreases as the maximum conductance increases, no complex APD dynamics and phase 2 reentry can be induced by I_SK. When SK channels sense Ca²⁺ in the subsarcolemmal space or junctional cleft, I_SK can rise and decay rapidly during an action potential in a spike-like pattern because of spiky Ca²⁺ transients in these compartments, which can cause spike-and-dome action potential morphology, APD alternans, J-wave elevation, and phase 2 reentry. Our results can account for the experimental finding that activation of I_SK induced J-wave syndrome and phase 2 reentry in rabbit hearts.

CONCLUSION

Colocalization of SK channels with L-type Ca²⁺ channels so that they preferentially sense Ca²⁺ in the subsarcolemmal or junctional space may result in a spiky I_SK, which can functionally play a similar role of the transient outward K⁺ current in promoting J-wave syndrome and ventricular arrhythmias.

Identification and validation of a novel dual small-molecule TLR2/8 antagonist

Toll-like receptor 2 (TLR2) and TLR8 are involved in the recognition of bacterial and viral components and are linked not only to protective antimicrobial immunity but also to inflammatory diseases. Recently, increasing attention has been paid to the receptor crosstalk between TLR2 and TLR8 to fine-tune innate immune responses. In this study, we report a novel dual TLR2/TLR8 antagonist, compound 24 that was developed by a modeling-guided synthesis approach. The modulator was optimized from the previously reported 1,3-benzothiazole derivative, compound 8. Compound 24 was pharmacologically characterized for the ability to inhibit TLR2- and TLR8-mediated responses in TLR-overexpressing reporter cells and THP-1 macrophages. The modulator showed high efficacy with IC₅₀ values in the low micromolar range for both TLRs, selectivity towards other TLRs and low cytotoxicity. At TLR2, a slight predominance for the TLR2/1 heterodimer was found in reporter cells selectively expressing TLR2/1 or TLR2/6 heterodimers. Concentration ratio analysis in the presence of Pam₃CSK₄ or Pam₂CSK₄ indicated non-competitive antagonist behavior at hTLR2. In computational docking studies, a plausible alternative binding mode of compound 24 was predicted for both TLR2 and TLR8. Our results provide evidence that it is feasible to simultaneously and selectively target endosomal- and surface-located TLRs. We identified a small-molecule dual TLR2/8 antagonist that may serve as a valuable pharmacological tool to decipher the role of TLR2/8 co-signaling in inflammation.

Human Purkinje in silico model enables mechanistic investigations into automaticity and pro-arrhythmic abnormalities

Cardiac Purkinje cells (PCs) are implicated in lethal arrhythmias caused by cardiac diseases, mutations, and drug action. However, the pro-arrhythmic mechanisms in PCs are not entirely understood, particularly in humans, as most investigations are conducted in animals. The aims of this study are to present a novel human PCs electrophysiology biophysically-detailed computational model, and to disentangle ionic mechanisms of human Purkinje-related electrophysiology, pacemaker activity and arrhythmogenicity. The new Trovato2020 model incorporates detailed Purkinje-specific ionic currents and Ca²⁺ handling, and was developed, calibrated and validated using human experimental data acquired at multiple frequencies, both in control conditions and following drug application. Multiscale investigations were performed in a Purkinje cell, in fibre and using an experimentally-calibrated population of PCs to evaluate biological variability. Simulations demonstrate the human Purkinje Trovato2020 model is the first one to yield: (i) all key AP features consistent with human Purkinje recordings; (ii) Automaticity with funny current up-regulation (iii) EADs at slow pacing and with 85% hERG block; (iv) DADs following fast pacing; (v) conduction velocity of 160 cm/s in a Purkinje fibre, as reported in human. The human in silico PCs population highlights that: (1) EADs are caused by I_CaL reactivation in PCs with large inward currents; (2) DADs and triggered APs occur in PCs experiencing Ca²⁺ accumulation, at fast pacing, caused by large L-type calcium current and small Na⁺/Ca²⁺ exchanger. The novel human Purkinje model unlocks further investigations into the role of cardiac Purkinje in ventricular arrhythmias through computer modeling and multiscale simulations.

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A human in silico AP model was developed to investigate arrhythmia in cardiac Purkinje.

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The new Purkinje model enables multiscale investigations from single cell to tissue.

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Populations of human Purkinje models reproduce and explain experimental variability.

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Ca²⁺-current reactivation triggers EADs in virtual Purkinje cells with weak repolarisation reserve.

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Ca²⁺ accumulation caused by increased Ca²⁺ and NCX currents triggers DADs.

Can motor function uncertainty and local instability within upper-extremity dual-tasking predict amnestic mild cognitive impairment and early-stage Alzheimer's disease?

In this study, we examined the uncertainty and local instability of motor function for cognitive impairment screening using a previously validated upper-extremity function (UEF). This approach was established based upon the fact that elders with an impaired executive function have trouble in the simultaneous execution of a motor and a cognitive task (dual-tasking). Older adults aged 65 years and older were recruited and stratified into 1) cognitive normal (CN), 2) amnestic MCI of the Alzheimer's type (aMCI), and 3) early-stage Alzheimer's Disease (AD). Participants performed normal-paced repetitive elbow flexion without counting and while counting backward by ones and threes. The influence of cognitive task on motor function was measured using uncertainty (measured by Shannon entropy), and local instability (measured by the largest Lyapunov exponent) of elbow flexion and compared between cognitive groups using ANOVAs, while adjusting for age, sex, and BMI. We developed logistic ordinal regression models for predicting cognitive groups based on these nonlinear measures. A total of 81 participants were recruited, including 35 CN (age = 83.8 ± 6.9), 30 aMCI (age = 83.9 ± 6.9), and 16 early AD (age = 83.2 ± 6.6). Uncertainty of motor function demonstrated the strongest associations with cognitive impairment, with an effect size of 0.52, 0.88, and 0.51 for CN vs. aMCI, CN vs. AD, and aMCI vs. AD comparisons, respectively. Ordinal logistic models predicted cognitive impairment (aMCI and AD combined) with a sensitivity and specificity of 0.82. The findings accentuate the potential of employing nonlinear dynamical features of motor functions during dual-tasking, especially uncertainty, in detecting cognitive impairment.

Visualizing mesoderm and neural crest cell dynamics during chick head morphogenesis

Vertebrate head morphogenesis involves carefully-orchestrated tissue growth and cell movements of the mesoderm and neural crest to form the distinct craniofacial pattern. To better understand structural birth defects, it is important that we characterize the dynamics of these processes and learn how they rely on each other. Here we examine this question during chick head morphogenesis using time-lapse imaging, computational modeling, and experiments. We find that head mesodermal cells in culture move in random directions as individuals and move faster in the presence of neural crest cells. In vivo, mesodermal cells migrate in a directed manner and maintain neighbor relationships; neural crest cells travel through the mesoderm at a faster speed. The mesoderm grows with a non-uniform spatio-temporal profile determined by BrdU labeling during the period of faster and more-directed neural crest collective migration through this domain. We use computer simulations to probe the robustness of neural crest stream formation by varying the spatio-temporal growth profile of the mesoderm. We follow this with experimental manipulations that either stop mesoderm growth or prevent neural crest migration and observe changes in the non-manipulated cell population, implying a dynamic feedback between tissue growth and neural crest cell signaling to confer robustness to the system. Overall, we present a novel descriptive analysis of mesoderm and neural crest cell dynamics that reveals the coordination and co-dependence of these two cell populations during head morphogenesis.

Production of an environmentally stable anti-corrosion film based on Esfand seed extract molecules-metal cations: Integrated experimental and computer modeling approaches

This study aims at finding a suitable alternative for traditional and hazardous organic/inorganic corrosion inhibitors. In this study, the aqueous extract of Esfand seed (ESE) was used as a unique green source of nitrogen-based organic compound with great capability of the steel corrosion inhibition in a saline solution. Surface and electrochemical analyses were carried out by Ultraviolet-visible spectroscopy (UV), Fourier-transform infrared spectroscopy (FT-IR), Grazing incidence X-ray diffraction (GIXRD), field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDS), atomic force microscopy (AFM), electrochemical impedance spectroscopy (EIS), and polarization methods. Furthermore, the adsorption of inhibitors on the steel surface was explored by Monte Carlo (MC), molecular dynamics (MD) and quantum mechanics (QM) methods. The electrochemical studies established the effectiveness of the zinc cations addition to the ESE containing solution on its inhibition efficiency. The sample inhibited by 300 ppm ESE + 700 ppm Zn showed the highest anti-corrosion properties. The inhibition efficiency of this sample was reached 98.8% after 264 h which is much higher than those reported in the previous studies. QM computations proved the formation of complexes via donor-acceptor action. MC and MD simulations supported the inhibitors adsorption on the steel.

Oxygen in the Tumor Microenvironment: Mathematical and Numerical Modeling

There are many reasons to try to achieve a good grasp of the distribution of oxygen in the tumor microenvironment. The lack of oxygen - hypoxia - is a main actor in the evolution of tumors and in their growth and appears to be just as important in tumor invasion and metastasis. Mathematical models of the distribution of oxygen in tumors which are based on reaction-diffusion equations provide partial but qualitatively significant descriptions of the measured oxygen concentrations in the tumor microenvironment, especially when they incorporate important elements of the blood vessel network such as the blood vessel size and spatial distribution and the pulsation of local pressure due to blood circulation. Here, we review our mathematical and numerical approaches to the distribution of oxygen that yield insights both on the role of the distribution of blood vessel density and size and on the fluctuations of blood pressure.

Do we really need three-dimensional convex inguinal hernia meshes?

PURPOSE

The aim of our study was to assess the anatomical variability in the curvature of the muscles in inguinal region and to determine the necessity of using three-dimensional convex-shape hernia meshes in spite of typical flat ones.

METHODS

The group analyzed consisted of 180 patients with no abnormalities in inguinal region: 30 males and 30 females for every subgroup, with decreased, normal, and increased BMI. For every patient a 3D software model of muscles in the inguinal region was built, based on the segmentation of pelvic CT images and its outer surface determining the shape of the hernia mesh was created. Correlation was tested between the diameter of sphere describing the shape, the height of mesh top, and BMI. The optimal number and diameter of prefabricated hernia mesh set was obtained.

RESULTS

Only a moderate correlation (r = - 0.32) was found between the diameter of sphere and BMI in females and between the height of the mesh top and BMI in females (r = 0.43) and in the whole group of patients (r = 0.33). Accepting fitting error < 5 mm for 62 from 180 cases there was no need to use a convex-shape hernia mesh, for the other cases one of 3 prefabricated hernia meshes with diameters: 854 mm, 434 mm, 298 mm was sufficient.

CONCLUSIONS

For about one-third of patients a commonly used flat hernia mesh is adequate, for the rest of the cases one of 3 convex-shape meshes is sufficient.

Applications of Computer Modeling and Simulation in Cartilage Tissue Engineering

BACKGROUND

Advances in cartilage tissue engineering have demonstrated noteworthy potential for developing cartilage for implantation onto sites impacted by joint degeneration and injury. To supplement resource-intensive in vivo and in vitro studies required for cartilage tissue engineering, computational models and simulations can assist in enhancing experimental design.

METHODS

Research articles pertinent to cartilage tissue engineering and computer modeling were identified, reviewed, and summarized. Various applications of computer modeling for cartilage tissue engineering are highlighted, limitations of in silico modeling are addressed, and suggestions for future work are enumerated.

RESULTS

Computational modeling can help better characterize shear stresses generated by bioreactor fluid flow, refine scaffold geometry, customize the mechanical properties of engineered cartilage tissue, and model rates of cell growth and dynamics. Thus, results from in silico studies can help resourcefully enhance in vitro and in vivo studies; however, the limitations of these studies, such as the underlying assumptions and simplifications applied in each model, should always be addressed and justified where applicable. In silico models should also seek validation and verification when possible.

CONCLUSION

Future studies may adopt similar approaches to supplement in vitro trials and further investigate effects of mechanical stimulation on chondrocyte and stem cell dynamics. Additionally, as precision medicine, machine learning, and powerful open-source software become more popular and accessible, applications of multi-scale and multiphysics computational models in cartilage tissue engineering are expected to increase.

Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study

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In this paper, we present how, by combining electrocardiogram and imaging data, machine learning and high performance computing simulations, we identified four phenotypes in hypertrophic cardiomyopathy (HCM), with differences in arrhythmic risk, and provided two distinct possible mechanisms that may explain the heterogeneity of HCM manifestation.

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This led to a better HCM patient stratification and understanding of the underlying disease mechanisms, providing a step further towards tailored HCM patient management and treatment.

Most patients with hypertrophic cardiomyopathy (HCM), the most common genetic cardiac disease, remain asymptomatic, but others may suffer from sudden cardiac death. A better identification of those patients at risk, together with a better understanding of the mechanisms leading to arrhythmia, are crucial to target high-risk patients and provide them with appropriate treatment. However, this currently remains a challenge. In this paper, we present a successful example of implementing computational techniques for clinically-relevant applications. By combining electrocardiogram and imaging data, machine learning and high performance computing simulations, we identified four phenotypes in HCM, with differences in arrhythmic risk, and provided two distinct possible mechanisms that may explain the heterogeneity of HCM manifestation. This led to a better HCM patient stratification and understanding of the underlying disease mechanisms, providing a step further towards tailored HCM patient management and treatment.

Assembly of α-synuclein aggregates on phospholipid bilayers

The spontaneous self-assembly of α-synuclein (α-syn) into aggregates of different morphologies is associated with the development of Parkinson's disease. However, the mechanism behind the spontaneous assembly remains elusive. The current study shows a novel effect of phospholipid bilayers on the assembly of the α-syn aggregates. Using time-lapse atomic force microscopy, it was discovered that α-syn assembles into aggregates on bilayer surfaces, even at the nanomolar concentration range. The efficiency of the aggregation process depends on the membrane composition, with the greatest efficiency observed for of 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-l-serine (POPS). Importantly, assembled aggregates can dissociate from the surface, suggesting that on-surface aggregation is a mechanism by which pathological aggregates may be produced. Computational modeling revealed that dimers of α-syn assembled rapidly, through the membrane-bound monomer on POPS bilayer, due to an aggregation-prone orientation of α-syn. Interaction of α-syn with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) leads to a binding mode that does not induce a fast assembly of the dimer. Based on these findings, we propose a model in which the interaction of α-syn with membranes plays a critical role initiating the formation of α-syn aggregates and the overall aggregation process.

Lateral extra-articular reconstruction length changes during weightbearing knee flexion and pivot shift: A simulation study

INTRODUCTION

Variations in the length of lateral extra-articular reconstruction (LER) have been widely investigated during knee flexion but there is no information about length changes during pivot shift. This study sought to assess the changes in LER tension during weightbearing knee flexion in a normal knee and in a computer-simulated pivot-shift scenario.

HYPOTHESIS

Placing the femoral tunnel posterior and proximal to the lateral femoral epicondyle allows the LER to tighten early in the flexion range during weightbearing (squatting motion) and simulated pivot-shift.

MATERIAL AND METHODS

A computer model was used to simulate weightbearing knee flexion and pivot shift scenarios. Changes in LER tension were calculated in both scenarios by estimating the distance between six femoral attachment sites (posterior and proximal to the lateral femoral epicondyle) and two tibial tunnel locations: Gerdy's tubercle (GT) and the anterolateral ligament (ALL) anatomic attachment site.

RESULTS

Independent of the location of the femoral and tibial tunnels, the LER tightened by up to 22% of its resting length during the early portion of weightbearing knee flexion and then relaxed from 40° to 60° of knee flexion. The ALL tibial tunnel position allowed complete LER relaxation at 60° flexion whereas LER using the GT tibial tunnel position remained tighter. In the simulated pivot-shift test, and for all femoral tunnel locations, the LER tightened by 20% to 34% of its resting value for the GT tibial tunnel position and by 11% to 26% for the ALL tibial tunnel position.

DISCUSSION

During weightbearing knee flexion, placing the femoral tunnel proximal and posterior to the femoral epicondyle was associated with LER tightening in the early degrees of flexion and LER relaxation between 40 and 60° flexion. LER tightening occurred during a simulated pivot-shift test supporting the concept that a posterior and proximal femoral LER tunnel position is most effective during weightbearing knee flexion and altered knee kinematics.

Integrated lung tissue mechanics one piece at a time: Computational modeling across the scales of biology

The lung is a delicately balanced and highly integrated mechanical system. Lung tissue is continuously exposed to the environment via the air we breathe, making it susceptible to damage. As a consequence, respiratory diseases present a huge burden on society and their prevalence continues to rise. Emergent function is produced not only by the sum of the function of its individual components but also by the complex feedback and interactions occurring across the biological scales - from genes to proteins, cells, tissue and whole organ - and back again. Computational modeling provides the necessary framework for pulling apart and putting back together the pieces of the body and organ systems so that we can fully understand how they function in both health and disease. In this review, we discuss models of lung tissue mechanics spanning from the protein level (the extracellular matrix) through to the level of cells, tissue and whole organ, many of which have been developed in isolation. This is a vital step in the process but to understand the emergent behavior of the lung, we must work towards integrating these component parts and accounting for feedback across the scales, such as mechanotransduction. These interactions will be key to unlocking the mechanisms occurring in disease and in seeking new pharmacological targets and improving personalized healthcare.

Health improvement of human hair and their reshaping using recombinant keratin K31

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We expressed in E. coli, refolded and purified keratin K31, which is known to be a major protein degraded and eluted in hair damage.

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Analysis of the recombinant protein by MALDI-TOF, CD spectroscopy and molecular modelling showed its similarity to the native protein.

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Marked improvements in thickness, strength and smoothness on application of this protein on damaged hair has been shown for the first time.

Hair, being one of the most important components of the beauty care processes, attracts the use of a variety of hair treating cosmetics. Conventional hair treating cosmetics are not satisfactory for one reason or the other. Commercially used keratins are isolated from wool or chicken feathers. As these lack complete sequence identity with human hair keratin, are likely to be less efficient than the human hair keratin. K31, a type I acidic keratin, is a major protein of human hair keratin complex and it is essential for maintaining the hair tensile strength. In this study keratin K31 (46 kDa) gene was expressed in Escherichia coli at a level of approximately 35% of the total cell proteins. The protein, however, was expressed as insoluble inclusion bodies. The expressed protein was refolded and purified by FPLC using an anion-exchange column. The CD analysis results showed that the K31 was properly refolded. MALDI-TOF mass spectroscopy showed the characteristics expected for human K31 keratin. The refolded and partially purified K31 protein, when applied on chemically damaged hairs, increased the diameter of the hair up to 49%. The mechanical strength of the bleached hair increased by almost 2 fold after a single treatment of K31. The protein also straightened curly hair efficiently on a single treatment for one hour. Application of K31 resulted in marked improvements in smoothness, diameter and mechanical strength of the damaged hair.

3D chest wall reconstruction

Computer modeling and 3D printing has found wide-scale applicability in pre- and intraoperative meticulous planning of surgery. Dr. Harsh Singh from Christchurch Hospital, New Zealand, discusses its current and future role in chest wall reconstruction.