A combined experimental and finite element analysis of the human elbow under loads of daily living

Elbow trauma is often accompanied by a loss of independence in daily self-care activities, negatively affecting patients' quality of life. Finite element models can help gaining profound knowledge about native human joint mechanics, which is crucial to adequately restore joint functionality after severe injuries. Therefore, a finite element model of the elbow is required that includes both the radio-capitellar and ulno-trochlear joint and is subjected to loads realistic for activities of daily living. Since no such model has been published, we aim to fill this gap. For comparison, 8 intact cadaveric elbows were subjected to loads of up to 1000 N, after they were placed in an extended position. At each load step, the displacement of the proximal humerus relative to the distal base plate was measured with optical tracking markers and the joint pressure was measured with a pressure mapping sensor. Analogously, eight finite element models were created based on subject-specific CT scans of the corresponding elbow specimens. The CT scans were registered to the positions of tantalum beads in the experiment. The optically measured displacements were applied as boundary conditions. We demonstrated that the workflow can predict the experimental contact pressure distribution with a moderate correlation, the experimental peak pressures in the correct joints and the experimental stiffness with moderate to excellent correlation. The predictions of peak pressure magnitude, contact area and load share on the radius require improvement by precise representation of the cartilage geometry and soft tissues in the model, and proper initial contact in the experiment.

Computational modeling of multiple myeloma interactions with resident bone marrow cells

The interaction of multiple myeloma with bone marrow resident cells plays a key role in tumor progression and the development of drug resistance. The tumor cell response involves contact-mediated and paracrine interactions. The heterogeneity of myeloma cells and bone marrow cells makes it difficult to reproduce this environment in in-vitro experiments. The use of in-silico established tools can help to understand these complex problems. In this article, we present a computational model based on the finite element method to define the interactions of multiple myeloma cells with resident bone marrow cells. This model includes cell migration, which is controlled by stress-strain equilibrium, and cell processes such as proliferation, differentiation, and apoptosis. A series of computational experiments were performed to validate the proposed model. Cell proliferation by the growth factor IGF-1 is studied for different concentrations ranging from 0-10 ng/mL. Cell motility is studied for different concentrations of VEGF and fibronectin in the range of 0-100 ng/mL. Finally, cells were simulated under a combination of IGF-1 and VEGF stimuli whose concentrations are considered to be dependent on the cancer-associated fibroblasts in the extracellular matrix. Results show a good agreement with previous in-vitro results. Multiple myeloma growth and migration are shown to correlate linearly to the IGF-1 stimuli. These stimuli are coupled with the mechanical environment, which also improves cell growth. Moreover, cell migration depends on the fiber and VEGF concentration in the extracellular matrix. Finally, our computational model shows myeloma cells trigger mesenchymal stem cells to differentiate into cancer-associated fibroblasts, in a dose-dependent manner.

Automatic modeling of dynamic drug-hERG channel interactions using three voltage protocols and machine learning techniques: A simulation study

BACKGROUND

Assessment of drug cardiac safety is critical in the development of new compounds and is commonly addressed by evaluating the half-maximal blocking concentration of the potassium human ether-à-go-go related gene (hERG) channels. However, recent works have evidenced that the modelling of drug-binding dynamics to hERG can help to improve early cardiac safety assessment. Our goal is to develop a methodology to automatically generate Markovian models of the drug-hERG channel interactions.

METHODS

The training and the test sets consisted of 20800 and 5200 virtual drugs, respectively, distributed into 104 groups with different affinities and kinetics to the conformational states of the channel. In our system, drugs may bind to any state (individually or simultaneously), with different degrees of preference for a conformational state and the change of the conformational state of the drug bound channels may be restricted or allowed. To model such a wide range of possibilities, 12 Markovian chains are considered. Our approach uses the response of the drugs to our three previously developed voltage clamp protocols, which enhance the differences in the probabilities of occupying a certain conformational state of the channel (open, closed and inactivated). The computing tool is comprised of a classifier and a parameter optimizer and uses linear interpolation, support vector machines and a simplex method for function minimization.

RESULTS

We propose a novel methodology that automatically generates dynamic drug models using Markov model formulations and that elucidates the states where the drug binds and unbinds and the preferential binding state using data obtained from simple voltage clamp protocols that captures the preferential state-dependent binding properties, the relative affinities, trapping and non-trapping dynamics and the onset of I_Kr block. Overall, the tool correctly predicted the class of 92.04% of the drugs and the model provided by the tool accurately fitted the response of the target compound, the mean accuracy being 97.53%. Moreover, generation of the dynamic model of an I_Kr blocker from its response to our voltage clamp protocols usually takes less than an hour on a common desktop computer.

CONCLUSION

Our methodology could be very useful to model and simulate dynamic drug-hERG channel interactions. It would contribute to the improvement of the preclinical assessment of the proarrhythmic risk of drugs that inhibit I_Kr and the efficacy of antiarrhythmic I_Kr blockers.

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.

Comprehensive computational analysis of the crimping procedure of PLLA BVS: effects of material viscous-plastic and temperature dependent behavior

Recently, researchers focused their attention on the use of polymeric bioresorbable vascular scaffolds (BVSs) as alternative to permanent metallic drug-eluting stents (DESs) for the treatment of atherosclerotic coronary arteries. Due to the different mechanical properties, polymeric stents, if compared to DESs, are characterized by larger strut size and specific design. It implies that during the crimping phase, BVSs undergo higher deformation and the packing of the struts, making this process potentially critical for the onset of damage. In this work, a computational study on the crimping procedure of a PLLA stent, inspired by the Absorb GT1 (Abbott Vascular) design, is performed, with the aim of evaluating how different strategies (loading steps, velocities and temperatures) can influence the results in terms of damage risk and final crimped diameter. For these simulations, an elastic-viscous-plastic model was adopted, based on experimental results, obtained from tensile testing of PLLA specimens loaded according to ad hoc experimental protocols. Furthermore, the results of these simulations were compared with those obtained by neglecting strain rate and temperature dependence in the material model (as often done in the literature), showing how this lead to significant differences in the prediction of the crimped diameter and internal stress state.

Removal of beneficial insertion effects prevent the long-term persistence of transposable elements within simulated asexual populations

Background

Transposable elements are significant components of most organism’s genomes, yet the reasons why their abundances vary significantly among species is poorly understood. A recent study has suggested that even in the absence of traditional molecular evolutionary explanations, transposon proliferation may occur through a process known as ‘transposon engineering’. However, their model used a fixed beneficial transposon insertion frequency of 20%, which we believe to be unrealistically high.

Results

Reducing this beneficial insertion frequency, while keeping all other parameters identical, prevented transposon proliferation.

Conclusions

We conclude that the author’s original findings are better explained through the action of positive selection rather than ‘transposon engineering’, with beneficial insertion effects remaining important during transposon proliferation events.

NLScore: a novel quantitative algorithm based on 3 dimensional structural determinants to predict the probability of nuclear localization in proteins containing classical nuclear localization signals

The presence of a nuclear localization signal (NLS) in proteins can be inferred by the presence of a stretch of basic amino acids (KRKK). These NLSs are termed classical NLS (cNLS). However, only a fraction of proteins containing the cNLS pattern are transported into the nucleus by binding to importin α. Hence, there must exist, additional structural determinants that guide the appropriate interaction between putative NLSs containing cargo and importin α. Using 52 protein structures containing cNLS obtained from RCSB PDB, we assembled a training set and a validation set such that both sets were comprised of a combination of proteins with proven nuclear localization and ones that were non-nuclear. We modeled the interface between cargoes containing cNLS and importin α. We conducted rigid body docking and produced induced-fit modes by allowing both side chain and the backbone to be flexible. The output of these studies and additional determinants such as energy of interaction, atomic contacts, hydrophilic interaction, cationic interaction, and penetration of the cargo protein were used to derive a 26 parameter quantitative structure activity relationship based regression equation. This was further optimized by a step-wise backward elimination approach to derive a 15 parameter score. This NLScore was not only able to correctly classify confirmed nuclear and non-nuclear localized proteins but it was able to perform better than currently implemented algorithms like NucPred, Euk-mPLoc 2.0, cNls Mapper, and NLStradamus. Leave-one-out cross validation (LOOCV) showed that NLScore correctly predicted 78.6% and 81.6% of non-nuclear and nuclear proteins respectively. Graphical abstract NLScore: a novel quantitative algorithm based on 3 dimensional structural determinants to predict the probability of nuclear localization in proteins.