Effects of an integrative warm-up method on the range of motion, core stability, and quality of squat performance of young adults.

Introduction: This research aims to determine the effects of an integrative warm-up method on the range of motion in joints of the lower extremities, the strength of the stabilizer trunk muscles, and the quality of the basic movement patterns in older adolescents. Methods: The study sample consisted of 88 male students (age 20.1 ± 0.5). They were randomly divided into four groups: one control group (CG) (n = 17; 180.8 ± 7.9 cm; 82.3 ± 8.3 kg) and three experimental groups (EG): EG1 (n = 23; 180.9 ± 7.0 cm; 78.5 ± 9.5 kg), EG2 (n = 31; 182.2 cm ± 7.3 cm; 79.5 ± 11.5 kg), and EG3 (n = 17; 183.3 ± 4.9 cm; 77.5 ± 11.8 kg). The participants were subjected to a 6-week experimental treatment: EG1 once, EG2 twice, and EG3 three times a week. The experimental treatment consisted of four sub-phases representing the integrative warm-up Method: 1) Inhibition (self-myofascial release using a foam roller); 2) Lengthening (Static stretching in a maximum range of motion position); 3) Activation (Positional isometrics muscle activation of the trunk and gluteus); 4) Integration (Integrated all the previous phases into one complex movement pattern). Based on the covariance analysis (ANCOVA), statistically significant treatment effects were observed and positive changes were determined in all experimental groups. Results: The differences between groups were observed in the following variables: Overhead Squat Assessment (p = 0.000; ηp2=0.318), range of motion of left hip flexion (p = 0.000; ηp2=0.371), range of motion of right hip flexion (p = 0.000; ηp2=0.051) and range of motion of right hip extension (p = 0.051; ηp2=0.088), Double Leg Lowering Test (F = 2.411; p = 0.014; ηp2=0.014) and range of combined motion (plantar and dorsiflexion) of left ankle joint (p = 0.000; ηp2=0.299). There was no significant difference in the Plank Test (F = 1.007; p = 1.007; ηp2=0.035), range of combined motion (plantar and dorsiflexion) of right ankle joint (p = 0.088; ηp2=0.170) and range of motion of left hip extension (p = 0.158; ηp2=0.060). The participants of CG statistically significantly differed from EG1, EG2, and EG3 in the squat performance after the applied treatment. Discussion: The effect of the treatment was the occurrence of a transformational processes in almost all measured variables. It can be concluded that the integrative method is effective and applicable in practice for both young adults and recreational athletes.

On the generality of the finite element modeling physical fields in biological systems by the multiscale smeared concept (Kojic transport model).

The biomechanical and biochemical processes in the biological systems of living organisms are extremely complex. Advances in understanding these processes are mainly achieved by laboratory and clinical investigations, but in recent decades they are supported by computational modeling. Besides enormous efforts and achievements in this modeling, there still is a need for new methods that can be used in everyday research and medical practice. In this report, we give a view of the generality of the finite element methodology introduced by the first author and supported by his collaborators. It is based on the multiscale smeared physical fields, termed as Kojic Transport Model (KTM), published in several journal papers and summarized in a recent book (Kojic et al., 2022) [1]. We review relevant literature to demonstrate the distinctions and advantages of our methodology and indicate possible further applications. We refer to our published results by a selection of a few examples which include modeling of partitioning, blood flow, molecular transport within the pancreas, multiscale-multiphysics model of coupling electrical field and ion concentration, and a model of convective-diffusive transport within the lung parenchyma. Two new examples include a model of convective-diffusive transport within a growing tumor, and drug release from nanofibers with fiber degradation.

Modeling critical interaction for metastasis between circulating tumor cells (CTCs) and platelets adhered to the capillary wall.

BACKGROUND AND OBJECTIVE: We used a 2D fluid-solid interaction (FSI) model to investigate the critical conditions for the arrest of the CTCs traveling through the narrowed capillary with a platelet attached to the capillary wall. This computational model allows us to determine the deformations and the progression of the passage of the CTC through different types of microvessels with platelet included. METHODS: The modeling process is obtained using the strong coupling approach following the remeshing procedure. Also, the 1D FE rope element for simulating active ligand-receptor bonds is implemented in our computational tool (described below). RESULTS: A relationship between the CTCs properties (size and stiffness), the platelet size and stiffness, and the ligand-receptor interaction intensity, on one side, and the time in contact between the CTCs and platelet and conditions for the cell arrest, on the other side, are determined. The model is further validated in vitro by using a microfluidic device with metastatic breast tumor cells. CONCLUSIONS: The computational framework that is presented, with accompanying results, can be used as a powerful tool to study biomechanical conditions for CTCs arrest in interaction with platelets, giving a prognosis of disease progression.

In silico evaluation of pharmacokinetic parameters, delivery, distribution and anticoagulative effects of new 4,7-dihydroxycoumarin derivative.

In this study, pharmacological profiling and investigation of the anticoagulant activity of the newly synthesized coumarin derivative: (E)-3-(1-((4-hydroxy-3-methoxyphenyl)amino)ethylidene)-2,4-dioxochroman-7-yl acetate (L) were performed. The obtained results were compared with the parameters obtained for Warfarin (WF), which is a standard good oral anticoagulant. The estimated high binding affinity of L toward plasma proteins (PPS% value is > 90%) justifies the investigation of binding affinity and comparative analysis of L and WF to Human Serum Albumin (HSA) using the spectrofluorimetric method (296, 303 and 310 K) as well as molecular docking and molecular dynamics simulations. Compound L shows a very good binding affinity especially to the active site of WF (the active site I -subdomain IIA), quenching HSA fluorescence by a static process. Also, the finite element smeared model (Kojic Transport Model, KTM), which includes blood vessels and tissue, was implemented to compute the convective-diffusion transport of L and WF within the liver. Finally, compound L shows a high degree of inhibitory activity toward the VKOR receptor comparable to the inhibitory activity of WF. Stabilization and limited flexibility of amino acid residues in the active site of the VKOR after binding of L and WF indicates a very good inhibitory potential of compound L. The high affinity of the L for the VKOR enzyme (Vitamin K antagonist), as well as the structural similarity to commercial anticoagulants (WF), provide a basis for further studies and potential application in the treatment of venous thrombosis, pulmonary embolism and ischemic heart disease.Communicated by Ramaswamy H. Sarma.

Machine learning and physical based modeling for cardiac hypertrophy.

Background and objective: Predicting the long-term expansion and remodeling of the left ventricle in patients is challenging task but it has the potential to be clinically very useful. Methods: In our study, we present machine learning models based on random forests, gradient boosting, and neural networks, used to track cardiac hypertrophy. We collected data from multiple patients, and then the model was trained using the patient's medical history and present level of cardiac health. We also demonstrate a physical-based model, using the finite element procedure to simulate the development of cardiac hypertrophy. Results: Our models were used to forecast the evolution of hypertrophy over six years. The machine learning model and finite element model provided similar results. Conclusions: The finite element model is much slower, but it's more accurate compared to the machine learning model since it's based on physical laws guiding the hypertrophy process. On the other hand, the machine learning model is fast but the results can be less trustworthy in some cases. Both of our models, enable us to monitor the development of the disease. Because of its speed machine learning model is more likely to be used in clinical practice. Further improvements to our machine learning model could be achieved by collecting data from finite element simulations, adding them to the dataset, and retraining the model. This can result in a fast and more accurate model combining the advantages of physical-based and machine learning modeling.

Cardiac hypertrophy simulations using parametric and echocardiography-based left ventricle model with shell finite elements.

In our paper, we simulated cardiac hypertrophy with the use of shell elements in parametric and echocardiography-based left ventricle (LV) models. The hypertrophy has an impact on the change in the wall thickness, displacement field and the overall functioning of the heart. We computed both eccentric and concentric hypertrophy effects and tracked changes in the ventricle shape and wall thickness. Thickening of the wall was developed under the influence of concentric hypertrophy, while the eccentric hypertrophy produces wall thinning. To model passive stresses we used the recently developed material modal based on the Holzapfel experiments. Also, our specific shell composite finite element models for heart mechanics are much smaller and simpler to use with respect to conventional 3D models. Furthermore, the presented modeling approach of the echocardiography-based LV can serve as the basis for practical applications since it relies on the true patient-specific geometry and experimental constitutive relationships. Our model gives an insight into hypertrophy development in realistic heart geometries, and it has the potential to test medical hypotheses regarding hypertrophy evolution in a healthy and heart with a disease, under the influence of different conditions and parameters.

A novel composite smeared finite element for mechanics (CSFEM): Some applications.

BACKGROUND: Mechanical forces at the micro-scale level have been recognized as an important factor determining various biological functions. The study of cell or tissue mechanics is critical to understand problems in physiology and disease development. OBJECTIVE: The complexity of computational models and efforts made for their development in the past required significant robustness and different approaches in the modeling process. METHOD: For the purpose of modeling process simplifications, the smeared mechanics concept was introduced by M. Kojic as a general concept for modeling the deformation of composite continua. A composite smeared finite element for mechanics (CSFEM) was formulated which consists of the supporting medium and immersed subdomains of deformable continua with mutual interactions. Interaction is modeled using 1D contact elements (for both tangential and normal directions), where the interaction takes into account appropriate material parameters as well as the contact areas. RESULTS: In this paper we have presented verification examples with applications of the CSFEMs that include the pancreatic tumor tissue, nano-indentation model and tumor growth model. CONCLUSION: We have described CSFEM and contact elements between compartments that can interact. Accuracy and applicability are determined on two verification and tumor growth examples.

SILICOFCM platform, multiscale modeling of left ventricle from echocardiographic images and drug influence for cardiomyopathy disease.

BACKGROUND AND OBJECTIVE: In silico clinical trials are the future of medicine and virtual testing and simulation are the future of medical engineering. The use of a computational platform can reduce costs and time required for developing new models of medical devices and drugs. The computational platform, which is one of the main results of the SILICOFCM project, was developed using state-of-the-art finite element modeling for macro simulation of fluid-structure interaction with micro modeling at the molecular level for drug interaction with the cardiac cells. SILICOFCM platform is using for risk prediction and optimal drug therapy of familial cardiomyopathy in a specific patient. METHODS: In order to obtain 3D image reconstruction, the U-net architecture was used to determine geometric parameters for the left ventricle which were extracted from the echocardiographic apical and M-mode views. A micro-mechanics cellular model which includes three kinetic processes of sarcomeric proteins interactions was developed. It allows simulation of the drugs which are divided into three major groups defined by the principal action of each drug. Fluid-solid coupling for the left ventricle was presented. A nonlinear material model of the heart wall that was developed by using constitutive curves which include the stress-strain relationship was used. RESULTS: The results obtained with the parametric model of the left ventricle where pressure-volume (PV) diagrams depend on the change of Ca2+ were presented. It directly affects the ejection fraction. The presented approach with the variation of the left ventricle (LV) geometry and simulations which include the influence of different parameters on the PV diagrams are directly interlinked with drug effects on the heart function. It includes different drugs such as Entresto and Digoxin that directly affect the cardiac PV diagrams and ejection fraction. CONCLUSIONS: Computational platforms such as the SILICOFCM platform are novel tools for risk prediction of cardiac disease in a specific patient that will certainly open a new avenue for in silico clinical trials in the future.

Huxley muscle model surrogates for high-speed multi-scale simulations of cardiac contraction.

The computational requirements of the Huxley-type muscle models are substantially higher than those of Hill-type models, making large-scale simulations impractical or even impossible to use. We constructed a data-driven surrogate model that operates similarly to the original Huxley muscle model but consumes less computational time and memory to enable efficient usage in multiscale simulations of the cardiac cycle. The data was collected from numerical simulations to train deep neural networks so that the neural networks' behavior resembles that of the Huxley model. Since the Huxley muscle model is history-dependent, time series analysis is required to take the previous states of the muscle model into account. Recurrent and temporal convolutional neural networks are typically used for time series analysis. These networks were trained to produce stress and instantaneous stiffness. Once the networks have been trained, we compared the similarity of the produced stresses and achieved speed-up to the original Huxley model, which indicates the potential of the surrogate model to replace the model efficiently. We presented the creation procedure of the surrogate model and integration of the surrogate model into the finite element solver. Based on similarities between the surrogate model and the original model in several types of numerical experiments, and also achieved speed-up of an order of magnitude, it can be concluded that the surrogate model has the potential to replace the original model within multiscale simulations. Finally, we used our surrogate model to simulate a full cardiac cycle in order to demonstrate the application of the surrogate model in larger-scale problems.

Integration of Surrogate Huxley Muscle Model into Finite Element Solver for Simulation of the Cardiac Cycle.

Clinicians can use biomechanical simulations of cardiac functioning to evaluate various real and fictional events. Our present understanding of the molecular processes behind muscle contraction has inspired Huxley-like muscle models. Huxley-type muscle models, unlike Hill-type muscle models, are capable of modeling non-uniform and unstable contractions. Huxley's computational requirements, on the other hand, are substantially higher than those of Hill-type models, making large-scale simulations impractical to use. We created a data-driven surrogate model that acts similarly to the original Huxley muscle model but requires substantially less processing power in order to make the Huxley muscle models easier to use in computer simulations. We gathered data from multiple numerical simulations and trained a deep neural network based on gated-recurrent units. Once we accomplished satisfying precision, we integrated the surrogate model into our finite element solver and simulated a full cardiac cycle. Clinical Relevance- This enables clinicians to track the effects of changes in muscles at the microscale to the cardiac contraction (macroscale).

InSilc Computational Tool for In Silico Optimization of Drug-Eluting Bioresorbable Vascular Scaffolds.

Stents made by different manufacturers must meet the requirements of standard in vitro mechanical tests performed under different physiological conditions in order to be validated. In addition to in vitro research, there is a need for in silico numerical simulations that can help during the stent prototyping phase. In silico simulations have the ability to give the same stent responses as well as the potential to reduce costs and time needed to carry out experimental tests. The goal of this paper is to show the achievements of the computational platform created as a result of the EU-funded project InSilc, used for numerical testing of most standard tests for validation of preproduction bioresorbable vascular scaffolds (BVSs). Within the platform, an ad hoc simulation protocol has been developed based on the finite element (FE) analysis program PAK and user interface software CAD Field and Solid. Two different designs of two different stents have been numerically simulated using this integrated tool, and the results have been demonstrated. The following standard tests have been performed: longitudinal tensile strength, local compression, kinking, and flex 1-3. Strut thickness and additional pocket holes (slots) in two different scaffolds have been used as representative parameters for comparing the mechanical characteristics of the stents (AB-BVS vs. AB-BVS-thinner and PLLA-prot vs. PLLA-plot-slot). The AB-BVS-thinner prototype shows better overall stress distribution than the AB-BVS, while the PLLA-prot shows better overall stress distribution in comparison to the PLLA-plot-slot. In all cases, the values of the maximum effective stresses are below 220 MPa-the value obtained by in vitro experiment. Despite the presented results, additional considerations should be included before the proposed software can be used as a validation tool for stent prototyping.

In Silico Clinical Trials for Cardiovascular Disease.

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.

Attenuated Microcirculation in Small Metastatic Tumors in Murine Liver.

Metastatic cancer disease is the major cause of death in cancer patients. Because those small secondary tumors are clinically hardly detectable in their early stages, little is known about drug biodistribution and permeation into those metastatic tumors potentially contributing to insufficient clinical success against metastatic disease. Our recent studies indicated that breast cancer liver metastases may have compromised perfusion of intratumoral capillaries hindering the delivery of therapeutics for yet unknown reasons. To understand the microcirculation of small liver metastases, we have utilized computational simulations to study perfusion and oxygen concentration fields in and around the metastases smaller than 700 µm in size at the locations of portal vessels, central vein, and liver lobule acinus. Despite tumor vascularization, the results show that blood flow in those tumors can be substantially reduced indicating the presence of inadequate blood pressure gradients across tumors. A low blood pressure may contribute to the collapsed intratumoral capillary lumen limiting tumor perfusion that phenomenologically corroborates with our previously published in vivo studies. Tumors that are smaller than the liver lobule size and originating at different lobule locations may possess a different microcirculation environment and tumor perfusion. The acinus and portal vessel locations in the lobule were found to be the most beneficial to tumor growth based on tumor access to blood flow and intratumoral oxygen. These findings suggest that microcirculation states of small metastatic tumors can potentially contribute to physiological barriers preventing efficient delivery of therapeutic substances into small tumors.

Application of in silico Platform for the Development and Optimization of Fully Bioresorbable Vascular Scaffold Designs.

Bioresorbable vascular scaffolds (BVS), made either from polymers or from metals, are promising materials for treating coronary artery disease through the processes of percutaneous transluminal coronary angioplasty. Despite the opinion that bioresorbable polymers are more promising for coronary stents, their long-term advantages over metallic alloys have not yet been demonstrated. The development of new polymer-based BVS or optimization of the existing ones requires engineers to perform many very expensive mechanical tests to identify optimal structural geometry and material characteristics. in silico mechanical testing opens the possibility for a fast and low-cost process of analysis of all the mechanical characteristics and also provides the possibility to compare two or more competing designs. In this study, we used a recently introduced material model of poly-l-lactic acid (PLLA) fully bioresorbable vascular scaffold and recently empowered numerical InSilc platform to perform in silico mechanicals tests of two different stent designs with different material and geometrical characteristics. The result of inflation, radial compression, three-point bending, and two-plate crush tests shows that numerical procedures with true experimental constitutive relationships could provide reliable conclusions and a significant contribution to the optimization and design of bioresorbable polymer-based stents.

In vitro and in silico testing of partially and fully bioresorbable vascular scaffold.

Coronary artery disease (CAD), one of the leading causes of death globally, occurs due to the growth of atherosclerotic plaques in the coronary arteries, causing lesions which restrict the flow of blood to the myocardium. Percutaneous transluminal coronary angioplasty (PTCA), including balloon angioplasty and coronary stent deployment is a standard clinical invasive treatment for CAD. Coronary stents are delivered using a balloon catheter inserted across the lesion. The balloon is inflated to a nominal pressure, opening the occluded artery, deploying the stent and improving the flow of blood to the myocardium. All stent manufacturers have to perform standard in vitro mechanical testing under different physiological conditions. In this study, partially and fully bioresorbable vascular scaffolds (BVS) from Boston Scientific Limited have been examined in vitro and in silico for three different test methods: inflation, radial compression and crush resistance. We formulated a material model for poly-L-lactic acid (PLLA) and implemented it into our in-house software tool. A comparison of the different experimental results is presented in the form of graphs showing displacement-force curves, diameter - load curves or diameter - pressure curves. There is a strong correlation between simulation and real experiments with a coefficient of determination (R2) > 0.99 and a correlation coefficient (R) > 0.99. This preliminary study has shown that in-silico tests can mimic the applicable ISO standards for mechanical in vitro stent testing, providing the opportunity to use data generated using in-silico testing to partially or fully replacing the mechanical testing required for regulatory submission.

Preparation and modeling of three-layered PCL/PLGA/PCL fibrous scaffolds for prolonged drug release.

The authors present the preparation procedure and a computational model of a three-layered fibrous scaffold for prolonged drug release. The scaffold, produced by emulsion/sequential electrospinning, consists of a poly(D,L-lactic-co-glycolic acid) (PLGA) fiber layer sandwiched between two poly(ε-caprolactone) (PCL) layers. Experimental results of drug release rates from the scaffold are compared with the results of the recently introduced computational finite element (FE) models for diffusive drug release from nanofibers to the three-dimensional (3D) surrounding medium. Two different FE models are used: (1) a 3D discretized continuum and fibers represented by a simple radial one-dimensional (1D) finite elements, and (2) a 3D continuum discretized by composite smeared finite elements (CSFEs) containing the fiber smeared and surrounding domains. Both models include the effects of polymer degradation and hydrophobicity (as partitioning) of the drug at the fiber/surrounding interface. The CSFE model includes a volumetric fraction of fibers and diameter distribution, and is additionally enhanced by using correction function to improve the accuracy of the model. The computational results are validated on Rhodamine B (fluorescent drug l) and other hydrophilic drugs. Agreement with experimental results proves that numerical models can serve as efficient tools for drug release to the surrounding porous medium or biological tissue. It is demonstrated that the introduced three-layered scaffold delays the drug release process and can be used for the time-controlled release of drugs in postoperative therapy.

Tumor Site-Dependent Transport Properties Determine Nanotherapeutics Delivery and Its Efficacy.

Insufficient delivery of systemically administered anticancer drugs to tumors can compromise therapeutic efficacy and develop drug delivery-based therapeutic resistance. Nanotherapeutics such as PEGylated liposomal doxorubicin (PLD) are designed to preferentially accumulate in tumors utilizing enhanced permeation and retention effect. However, their antitumor effects and resulting clinical outcomes are modest and heterogeneous among tumors. Here, we aimed to investigate whether the amount and efficacy of PLD delivered to tumors are tumor site dependent. We established orthotopic primary tumor or liver metastases models of murine breast cancer using 4 T1 cells. PLD showed significant therapeutic effects against tumors that grew in primary mammary sites but not in the liver. We found that differences in therapeutic efficacy were not because of the intrinsic biological resistance of cancer cells but rather were associated with tumor site-dependent differences in transport properties, such as the amount of PLD delivery, blood vessel function, relative vascular permeability, and mechanical pressure in tumors. Thus, transport properties in tumor is site dependent and can be used as phenotypic surrogate markers for tumor drug delivery and therapeutic efficacy.

Coupling tumor growth and bio distribution models.

We couple a tumor growth model embedded in a microenvironment, with a bio distribution model able to simulate a whole organ. The growth model yields the evolution of tumor cell population, of the differential pressure between cell populations, of porosity of ECM, of consumption of nutrients due to tumor growth, of angiogenesis, and related growth factors as function of the locally available nutrient. The bio distribution model on the other hand operates on a frozen geometry but yields a much refined distribution of nutrient and other molecules. The combination of both models will enable simulating the growth of a tumor in a whole organ, including a realistic distribution of therapeutic agents and allow hence to evaluate the efficacy of these agents.

Smeared Multiscale Finite Element Models for Mass Transport and Electrophysiology Coupled to Muscle Mechanics.

Mass transport represents the most fundamental process in living organisms. It includes delivery of nutrients, oxygen, drugs, and other substances from the vascular system to tissue and transport of waste and other products from cells back to vascular and lymphatic network and organs. Furthermore, movement is achieved by mechanical forces generated by muscles in coordination with the nervous system. The signals coming from the brain, which have the character of electrical waves, produce activation within muscle cells. Therefore, from a physics perspective, there exist a number of physical fields within the body, such as velocities of transport, pressures, concentrations of substances, and electrical potential, which is directly coupled to biochemical processes of transforming the chemical into mechanical energy and further internal forces for motion. The overall problems of mass transport and electrophysiology coupled to mechanics can be investigated theoretically by developing appropriate computational models. Due to the enormous complexity of the biological system, it would be almost impossible to establish a detailed computational model for the physical fields related to mass transport, electrophysiology, and coupled fields. To make computational models feasible for applications, we here summarize a concept of smeared physical fields, with coupling among them, and muscle mechanics, which includes dependence on the electrical potential. Accuracy of the smeared computational models, also with coupling to muscle mechanics, is illustrated with simple example, while their applicability is demonstrated on a liver model with tumors present. The last example shows that the introduced methodology is applicable to large biological systems.