Muscle-driven forward dynamic active hybrid model of the lumbosacral spine: combined FEM and multibody simulation
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ABSTRACT:
SUBMITTER: Remus R
PROVIDER: S-EPMC10565495 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:In computational biomechanics, two separate types of models have been used predominantly to enhance the understanding of the mechanisms of action of the lumbosacral spine (LSS): Finite element (FE) and musculoskeletal multibody (MB) models. To combine advantages of both models, hybrid FE-MB models are an increasingly used alternative. The aim of this paper is to develop, calibrate, and validate a novel passive hybrid FE-MB open-access simulation model of a ligamentous LSS using ArtiSynth. Based on anatomical data from the Male Visible Human Project, the LSS model is constructed from the L1-S1 rigid vertebrae interconnected with hyperelastic fiber-reinforced FE intervertebral discs, ligaments, and facet joints. A mesh convergence study, sensitivity analyses, and systematic calibration were conducted with the hybrid functional spinal unit (FSU) L4/5. The predicted mechanical responses of the FSU L4/5, the lumbar spine (L1-L5), and the LSS were validated against literature data from in vivo and in vitro measurements and in silico models. Spinal mechanical responses considered when loaded with pure moments and combined loading modes were total and intervertebral range of motions, instantaneous axes and centers of rotation, facet joint contact forces, intradiscal pressures, disc bulges, and stiffnesses. Undesirable correlations with the FE mesh were minimized, the number of crisscrossed collagen fiber rings was reduced to five, and the individual influences of specific anatomical structures were adjusted to in vitro range of motions. Including intervertebral motion couplings for axial rotation and nonlinear stiffening under increasing axial compression, the predicted kinematic and structural mechanics responses were consistent with the comparative data. The results demonstrate that the hybrid simulation model is robust and efficient in reproducing valid mechanical responses to provide a starting point for upcoming optimizations and extensions, such as with active skeletal muscles.
| S-EPMC8075237 | biostudies-literature
Project description:IntroductionHelping physicians-in-training develop effective clinical reasoning skills may facilitate progression to expertise, reduce diagnostic errors, and improve patient safety. Using our previous experience, we developed a workshop that reviews musculoskeletal lumbar spine and hip conditions. This workshop also uses deductive and inductive modes of clinical reasoning and provides opportunities for learners to practice toggling from one to another while reviewing.MethodsUsing exemplar musculoskeletal vignettes, this workshop allows residents to practice engaging and toggling between both modes of information processing. This workshop also includes pre- and posttests, small-group learning, and a small-group competition.ResultsThe workshop was implemented with a group of 26 physical medicine and rehabilitation residents. Although residents did well on the pretest, the workshop improved their test performance. Residents liked the workshop and thought it improved their diagnostic ability.DiscussionA workshop that included team- and case-based learning, key features assessment, script theory, and gamification was effective in engaging residents and resulted in high resident satisfaction and the perception of increased ability to tackle clinical problems. Learning from our experience with the previous workshop resulted in significant reduction in faculty time required, and increased the number of residents who were able to complete both pre- and posttests.
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Project description:Efficient modeling approaches are necessary to accurately predict large-scale structural behavior of biomolecular systems like RNA (ribonucleic acid). Coarse-grained approximations of such complex systems can significantly reduce the computational costs of the simulation while maintaining sufficient fidelity to capture the biologically significant motions. However, given the coupling and nonlinearity of RNA systems (and effectively all biopolymers), it is expected that different parameters such as geometric and dynamic boundary conditions, and applied forces will affect the system's dynamic behavior. Consequently, static coarse-grained models (i.e., models for which the coarse graining is time invariant) are not always able to adequately sample the conformational space of the molecule. We introduce here the concept of adaptive coarse-grained molecular dynamics of RNA, which automatically adjusts the coarseness of the model, in an effort to more optimally increase simulation speed, while maintaining accuracy. Adaptivity requires two basic algorithmic developments: first, a set of integrators that seamlessly allow transitions between higher and lower fidelity models while preserving the laws of motion. Second, we propose and validate metrics for determining when and where more or less fidelity needs to be integrated into the model to allow sufficiently accurate dynamics simulation. Given the central role that multibody dynamics plays in the proposed framework, and the nominally large number of dynamic degrees of freedom being considered in these applications, a computationally efficient multibody method which lends itself well to adaptivity is essential to the success of this effort. A suite of divide-and-conquer algorithm (DCA)-based approaches is employed to this end. These algorithms have been selected and refined for this purpose because they offer a good combination of computational efficiency and modular structure.
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Project description:As part of intracellular copper trafficking pathways, the human copper chaperone Hah1 delivers Cu(+) to the Wilson's Disease Protein (WDP) via weak and dynamic protein-protein interactions. WDP contains six homologous metal binding domains (MBDs) connected by flexible linkers, and these MBDs all can receive Cu(+) from Hah1. The functional roles of the MBD multiplicity in Cu(+) trafficking are not well understood. Building on our previous study of the dynamic interactions between Hah1 and the isolated fourth MBD of WDP, here we study how Hah1 interacts with MBD34, a double-domain WDP construct, using single-molecule fluorescence resonance energy transfer (smFRET) combined with vesicle trapping. By alternating the positions of the smFRET donor and acceptor, we systematically probed Hah1-MBD3, Hah1-MBD4, and MBD3-MBD4 interaction dynamics within the multidomain system. We found that the two interconverting interaction geometries were conserved in both intermolecular Hah1-MBD and intramolecular MBD-MBD interactions. The Hah1-MBD interactions within MBD34 are stabilized by an order of magnitude relative to the isolated single-MBDs, and thermodynamic and kinetic evidence suggest that Hah1 can interact with both MBDs simultaneously. The enhanced interaction stability of Hah1 with the multi-MBD system, the dynamic intramolecular MBD-MBD interactions, and the ability of Hah1 to interact with multiple MBDs simultaneously suggest an efficient and versatile mechanism for the Hah1-to-WDP pathway to transport Cu(+).
| S-EPMC3375613 | biostudies-literature
Project description:BackgroundMany computer studies have employed either dynamic simulation or metabolic flux analysis (MFA) to predict the behaviour of biochemical pathways. Dynamic simulation determines the time evolution of pathway properties in response to environmental changes, whereas MFA provides only a snapshot of pathway properties within a particular set of environmental conditions. However, owing to the large amount of kinetic data required for dynamic simulation, MFA, which requires less information, has been used to manipulate large-scale pathways to determine metabolic outcomes.ResultsHere we describe a simulation method based on cooperation between kinetics-based dynamic models and MFA-based static models. This hybrid method enables quasi-dynamic simulations of large-scale metabolic pathways, while drastically reducing the number of kinetics assays needed for dynamic simulations. The dynamic behaviour of metabolic pathways predicted by our method is almost identical to that determined by dynamic kinetic simulation.ConclusionThe discrepancies between the dynamic and the hybrid models were sufficiently small to prove that an MFA-based static module is capable of performing dynamic simulations as accurately as kinetic models. Our hybrid method reduces the number of biochemical experiments required for dynamic models of large-scale metabolic pathways by replacing suitable enzyme reactions with a static module.
| S-EPMC1262783 | biostudies-literature
Project description:Arthrodesis is a recommended treatment in advanced stages of degenerative disc disease. Despite dynamic fixations were designed to prevent abnormal motions with better physiological load transmission, improving lumbar pain and reducing stress on adjacent segments, contradictory results have been obtained. This study was designed to compare differences in the biomechanical behaviour between the healthy lumbar spine and the spine with DYNESYS and DIAM fixation, respectively, at L4-L5 level. Behaviour under flexion, extension, lateral bending and axial rotation are compared using healthy lumbar spine as reference. Three 3D finite element models of lumbar spine (healthy, DYNESYS and DIAM implemented, respectively) were developed, together a clinical follow-up of 58 patients operated on for degenerative disc disease. DYNESYS produced higher variations of motion with a maximum value for lateral bending, decreasing intradiscal pressure and facet joint forces at instrumented level, whereas screw insertion zones concentrated stress. DIAM increased movement during flexion, decreased it in another three movements, and produced stress concentration at the apophyses at instrumented level. Dynamic systems, used as single systems without vertebral fusion, could be a good alternative to degenerative disc disease for grade II and grade III of Pfirrmann.
| S-EPMC5706716 | biostudies-literature
Project description:Lumbosacral (LS) stenosis is a cause of lower back pain, loss of mission readiness, and early retirement in military working dogs (MWDs). Aims of the present two-part study were to evaluate a sample of German Shepherd MWDs using standard clinical criteria for LS pain, standard qualitative computed tomographic (CT) criteria for LS stenosis, novel quantitative CT criteria for LS stenosis, and novel behavioral classification criteria for LS pain. Data were retrieved from archives of a tertiary referral MWD hospital. Study 1 was a retrospective, observational, two-group design with a hypothesis that there would be a significant difference in the percentage of affected German Shepherd MWDs with multilevel stenosis (affecting ≥ 3 vertebrae) between LS pain groups, based on standard clinical and qualitative CT criteria. Study 2 was a retrospective, observational, cross-sectional, two- and three-group study design with a hypothesis that quantitative CT measurements would significantly differ between LS pain groups, assigned based on 3 classification systems. The 1st classification system used standard clinical criteria, while the 2nd and 3rd novel classifications included behavioral signs of LS pain. The following quantitative CT measures were recorded without knowledge of behavioral classification: vertebral foramen area, vertebral foramen volume, vertebral foramen fat area; and ratios of vertebral foramen area/vertebral body area (foramen area ratio), cranial vertebral foramen area/caudal vertebral foramen area (cranial:caudal foramen area ratio), and vertebral fat area/vertebral body area (fat area ratio). Study 1 findings did not support the hypothesis in that there was no significant difference in the percentage of dogs affected with multilevel stenosis between LS pain groups (P = 0.6567). Findings for study 2 supported the hypothesis in that dogs with LS pain were significantly more affected by multilevel stenosis (P = 0.0273). Significant differences occurred between LS pain groups in select vertebral locations for all measurements (P ≤ 0.05) except vertebral foramen area and vertebral foramen volume (P > 0.05). Comparisons using novel quantitative CT measures and behavioral classification criteria identified significant differences between LS pain groups that were not detected using standard qualitative criteria. These novel quantitative and behavioral classification criteria may be helpful in future research on causes for early retirement in German Shepherd MWDs.
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Project description:PurposeCurrently, the intra-operative visualization of vessels during endovascular aneurysm repair (EVAR) relies on contrast-based imaging modalities. Moreover, traditional image fusion techniques lack a continuous and automatic update of the vessel configuration, which changes due to the insertion of stiff guidewires. The purpose of this work is to develop and evaluate a novel approach to improve image fusion, that takes into account the deformations, combining electromagnetic (EM) tracking technology and finite element modeling (FEM).MethodsTo assess whether EM tracking can improve the prediction of the numerical simulations, a patient-specific model of abdominal aorta was segmented and manufactured. A database of simulations with different insertion angles was created. Then, an ad hoc sensorized tool with three embedded EM sensors was designed, enabling tracking of the sensors' positions during the insertion phase. Finally, the corresponding cone beam computed tomography (CBCT) images were acquired and processed to obtain the ground truth aortic deformations of the manufactured model.ResultsAmong the simulations in the database, the one minimizing the in silico versus in vitro discrepancy in terms of sensors' positions gave the most accurate aortic displacement results.ConclusionsThe proposed approach suggests that the EM tracking technology could be used not only to follow the tool, but also to minimize the error in the predicted aortic roadmap, thus paving the way for a safer EVAR navigation.
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Project description:Fragility fractures are a major socioeconomic problem. A non-invasive, computationally-efficient method for the identification of fracture risk scenarios under the representation of neuro-musculoskeletal dynamics does not exist. We introduce a computational workflow that integrates modally-reduced, quantitative CT-based finite-element models into neuro-musculoskeletal flexible multibody simulation (NfMBS) for early bone fracture risk assessment. Our workflow quantifies the bone strength via the osteogenic stresses and strains that arise due to the physiological-like loading of the bone under the representation of patient-specific neuro-musculoskeletal dynamics. This allows for non-invasive, computationally-efficient dynamic analysis over the enormous parameter space of fracture risk scenarios, while requiring only sparse clinical data. Experimental validation on a fresh human femur specimen together with femur strength computations that were consistent with literature findings provide confidence in the workflow: The simulation of an entire squat took only 38 s CPU-time. Owing to the loss (16% cortical, 33% trabecular) of bone mineral density (BMD), the strain measure that is associated with bone fracture increased by 31.4%; and yielded an elevated risk of a femoral hip fracture. Our novel workflow could offer clinicians with decision-making guidance by enabling the first combined in-silico analysis tool using NfMBS and BMD measurements for optimized bone fracture risk assessment.
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Project description:Over the last years, large scale proteomics studies have generated a wealth of information of biomolecular complexes. Adding the structural dimension to the resulting interactomes represents a major challenge that classical structural experimental methods alone will have difficulties to confront. To meet this challenge, complementary modeling techniques such as docking are thus needed. Among the current docking methods, HADDOCK (High Ambiguity-Driven DOCKing) distinguishes itself from others by the use of experimental and/or bioinformatics data to drive the modeling process and has shown a strong performance in the critical assessment of prediction of interactions (CAPRI), a blind experiment for the prediction of interactions. Although most docking programs are limited to binary complexes, HADDOCK can deal with multiple molecules (up to six), a capability that will be required to build large macromolecular assemblies. We present here a novel web interface of HADDOCK that allows the user to dock up to six biomolecules simultaneously. This interface allows the inclusion of a large variety of both experimental and/or bioinformatics data and supports several types of cyclic and dihedral symmetries in the docking of multibody assemblies. The server was tested on a benchmark of six cases, containing five symmetric homo-oligomeric protein complexes and one symmetric protein-DNA complex. Our results reveal that, in the presence of either bioinformatics and/or experimental data, HADDOCK shows an excellent performance: in all cases, HADDOCK was able to generate good to high quality solutions and ranked them at the top, demonstrating its ability to model symmetric multicomponent assemblies. Docking methods can thus play an important role in adding the structural dimension to interactomes. However, although the current docking methodologies were successful for a vast range of cases, considering the variety and complexity of macromolecular assemblies, inclusion of some kind of experimental information (e.g. from mass spectrometry, nuclear magnetic resonance, cryoelectron microscopy, etc.) will remain highly desirable to obtain reliable results.
| S-EPMC2938057 | biostudies-literature