Use of Computational Modeling to Study Joint Degeneration: A Review

Knee-Loading Predictions with Neural Networks Improve Finite Element Modeling Classifications of Knee Osteoarthritis: Data from the Osteoarthritis Initiative

Physics-based modeling methods have the potential to investigate the mechanical factors associated with knee osteoarthritis (OA) and predict the future radiographic condition of the joint. However, it remains unclear what level of detail is optimal in these methods to achieve accurate prediction results in cohort studies. In this work, we extended a template-based finite element (FE) method to include the lateral and medial compartments of the tibiofemoral joint and simulated the mechanical responses of 97 knees under three conditions of gait loading. Furthermore, the effects of variations in cartilage thickness and failure equation on predicted cartilage degeneration were investigated. Our results showed that using neural network-based estimations of peak knee loading provided classification performances of 0.70 (AUC, p < 0.05) in distinguishing between knees that developed severe OA or mild OA and knees that did not develop OA eight years after a healthy radiographic baseline. However, FE models incorporating subject-specific femoral and tibial cartilage thickness did not improve this classification performance, suggesting there exists an optimal point between personalized loading and geometry for discrimination purposes. In summary, we proposed a modeling framework that streamlines the rapid generation of individualized knee models achieving promising classification performance while avoiding motion capture and cartilage image segmentation.

Pyroptosis in Osteoarthritis: Molecular Mechanisms and Therapeutic Implications

Osteoarthritis (OA) is a chronic disease that causes pain and functional impairment by affecting joint tissue. Its global impact is noteworthy, causing significant economic losses and property damage. Despite extensive research, the underlying pathogenesis of OA remain an area of ongoing investigation. It has recently been discovered that the OA progression is significantly influenced by pyroptosis. Pyroptosis is a complex process that involves three pathways culminating in the assembly of Gasdermin-D (GSDMD)-N-terminal (GSDMD-NT) into pores through aggregation on the plasma membrane. The aggregation of GSDMD-NT proteins stimulates the release of inflammatory mediators, such as Interleukin-1β (IL-1β), Interleukin-18 (IL-18), and Matrix Metallopeptidase 13 (MMP13), ultimately leading to cellular lysis. The pyroptosis process in specific cells, including synovial macrophages, fibroblast-like synoviocytes (FLS), chondrocytes, and subchondral osteoblasts, contributs factor to the development of OA. Currently, the specific cells that undergo pyroptosis first are not yet fully understood, and it remains unknown whether pyroptosis in one cell can trigger the same process in other cells. Therefore, targeting pyroptosis could potentially offer a novel treatment approach for OA patients. We present a comprehensive analysis of the molecular mechanisms and key features of pyroptosis. We also outline the current research progress on various aspects, including synovial tissue, articular cartilage, extracellular matrix (ECM), and subchondral bone, with a focus on pyroptosis. The aim is to provide theoretical references for the effective management of OA.

Validation and evaluation of subject-specific finite element models of the pediatric knee

Finite element (FE) models have been widely used to investigate knee joint biomechanics. Most of these models have been developed to study adult knees, neglecting pediatric populations. In this study, an atlas-based approach was employed to develop subject-specific FE models of the knee for eight typically developing pediatric individuals. Initially, validation simulations were performed at four passive tibiofemoral joint (TFJ) flexion angles, and the resulting TFJ and patellofemoral joint (PFJ) kinematics were compared to corresponding patient-matched measurements derived from magnetic resonance imaging (MRI). A neuromusculoskeletal-(NMSK)-FE pipeline was then used to simulate knee biomechanics during stance phase of walking gait for each participant to evaluate model simulation of a common motor task. Validation simulations demonstrated minimal error and strong correlations between FE-predicted and MRI-measured TFJ and PFJ kinematics (ensemble average of root mean square errors < 5 mm for translations and < 4.1° for rotations). The FE-predicted kinematics were strongly correlated with published reports (ensemble average of Pearson's correlation coefficients (ρ) > 0.9 for translations and ρ > 0.8 for rotations), except for TFJ mediolateral translation and abduction/adduction rotation. For walking gait, NMSK-FE model-predicted knee kinematics, contact areas, and contact pressures were consistent with experimental reports from literature. The strong agreement between model predictions and experimental reports underscores the capability of sequentially linked NMSK-FE models to accurately predict pediatric knee kinematics, as well as complex contact pressure distributions across the TFJ articulations. These models hold promise as effective tools for parametric analyses, population-based clinical studies, and enhancing our understanding of various pediatric knee injury mechanisms. They also support intervention design and prediction of surgical outcomes in pediatric populations.

Towards a Transferable Modeling Method of the Knee to Distinguish Between Future Healthy Joints from Osteoarthritic Joints: Data from the Osteoarthritis Initiative

Computational models can be used to predict the onset and progression of knee osteoarthritis. Ensuring the transferability of these approaches among computational frameworks is urgent for their reliability. In this work, we assessed the transferability of a template-based modeling strategy, based on the finite element (FE) method, by implementing it on two different FE softwares and comparing their results and conclusions. For that, we simulated the knee joint cartilage biomechanics of 154 knees using healthy baseline conditions and predicted the degeneration that occurred after 8 years of follow-up. For comparisons, we grouped the knees using their Kellgren-Lawrence grade at the 8-year follow-up time and the simulated volume of cartilage tissue that exceeded age-dependent thresholds of maximum principal stress. We considered the medial compartment of the knee in the FE models and used ABAQUS and FEBio FE softwares for simulations. The two FE softwares detected different volumes of overstressed tissue in corresponding knee samples (p < 0.01). However, both programs correctly distinguished between the joints that remained healthy and those that developed severe osteoarthritis after the follow-up (AUC = 0.73). These results indicate that different software implementations of a template-based modeling method similarly classify future knee osteoarthritis grades, motivating further evaluations using simpler cartilage constitutive models and additional studies on the reproducibility of these modeling strategies.

Signatures of disease progression in knee osteoarthritis: insights from an integrated multi-scale modeling approach, a proof of concept

Introduction: Knee osteoarthritis (KOA) is characterized by articular cartilage degeneration. It has been widely accepted that the mechanical joint environment plays a significant role in the onset and progression of this disease. In silico models have been used to study the interplay between mechanical loading and cartilage degeneration, hereby relying mainly on two key mechanoregulatory factors indicative of collagen degradation and proteoglycans depletion. These factors are the strain in collagen fibril direction (SFD) and maximum shear strain (MSS) respectively. Methods: In this study, a multi-scale in silico modeling approach was used based on a synergy between musculoskeletal and finite element modeling to evaluate the SFD and MSS. These strains were evaluated during gait based on subject-specific gait analysis data collected at baseline (before a 2-year follow-up) for a healthy and progressive early-stage KOA subject with similar demographics. Results: The results show that both SFD and MSS factors allowed distinguishing between a healthy subject and a KOA subject, showing progression at 2 years follow-up, at the instance of peak contact force as well as during the stance phase of the gait cycle. At the peak of the stance phase, the SFD were found to be more elevated in the KOA patient with the median being 0.82% higher in the lateral and 0.4% higher in the medial compartment of the tibial cartilage compared to the healthy subject. Similarly, for the MSS, the median strains were found to be 3.6% higher in the lateral and 0.7% higher in the medial tibial compartment of the KOA patient compared to the healthy subject. Based on these intersubject SFD and MSS differences, we were additionally able to identify that the tibial compartment of the KOA subject at risk of progression. Conclusion/discussion: We confirmed the mechanoregulatory factors as potential biomarkers to discriminate patients at risk of disease progression. Future studies should evaluate the sensitivity of the mechanoregulatory factors calculated based on this multi-scale modeling workflow in larger patient and control cohorts.

The role of apoptosis in the pathogenesis of osteoarthritis

PURPOSE

Apoptosis is an important physiological process, making a great difference to development and tissue homeostasis. Osteoarthritis (OA) is a chronic joint disease characterized by degeneration and destruction of articular cartilage and bone hyperplasia. This purpose of this study is to provide an updated review of the role of apoptosis in the pathogenesis of osteoarthritis.

METHODS

A comprehensive review of the literature on osteoarthritis and apoptosis was performed, which mainly focused on the regulatory factors and signaling pathways associated with chondrocyte apoptosis in osteoarthritis and other pathogenic mechanisms involved in chondrocyte apoptosis.

RESULTS

Inflammatory mediators such as reactive oxygen species (ROS), nitric oxide (NO), IL-1β, tumor necrosis factor-α (TNF-α), and Fas are closely related to chondrocyte apoptosis. NF-κB signaling pathway, Wnt signaling pathway, and Notch signaling pathway activate proteins and gene targets that promote or inhibit the progression of osteoarthritis disease, including chondrocyte apoptosis and ECM degradation. Long non-coding RNAs (LncRNAs) and microRNAs (microRNAs) have gradually replaced single and localized research methods and become the main research approaches. In addition, the relationship between cellular senescence, autophagy, and apoptosis was also briefly explained.

CONCLUSION

This review offers a better molecular delineation of apoptotic processes that may help in designing new therapeutic options for OA treatment.

Network-based modelling of mechano-inflammatory chondrocyte regulation in early osteoarthritis

Osteoarthritis (OA) is a debilitating joint disease characterized by articular cartilage degradation, inflammation and pain. An extensive range of in vivo and in vitro studies evidences that mechanical loads induce changes in chondrocyte gene expression, through a process known as mechanotransduction. It involves cascades of complex molecular interactions that convert physical signals into cellular response(s) that favor either chondroprotection or cartilage destruction. Systematic representations of those interactions can positively inform early strategies for OA management, and dynamic modelling allows semi-quantitative representations of the steady states of complex biological system according to imposed initial conditions. Yet, mechanotransduction is rarely integrated. Hence, a novel mechano-sensitive network-based model is proposed, in the form of a continuous dynamical system: an interactome of a set of 118 nodes, i.e., mechano-sensitive cellular receptors, second messengers, transcription factors and proteins, related among each other through a specific topology of 358 directed edges is developed. Results show that under physio-osmotic initial conditions, an anabolic state is reached, whereas initial perturbations caused by pro-inflammatory and injurious mechanical loads leads to a catabolic profile of node expression. More specifically, healthy chondrocyte markers (Sox9 and CITED2) are fully expressed under physio-osmotic conditions, and reduced under inflammation, or injurious loadings. In contrast, NF-κB and Runx₂, characteristic of an osteoarthritic chondrocyte, become activated under inflammation or excessive loading regimes. A literature-based evaluation shows that the model can replicate 94% of the experiments tested. Sensitivity analysis based on a factorial design of a treatment shows that inflammation has the strongest influence on chondrocyte metabolism, along with a significant deleterious effect of static compressive loads. At the same time, anti-inflammatory therapies appear as the most promising ones, though the restoration of structural protein production seems to remain a major challenge even in beneficial mechanical environments. The newly developed mechano-sensitive network model for chondrocyte activity reveals a unique potential to reflect load-induced chondroprotection or articular cartilage degradation in different mechano-chemical-environments.

Assessment of reporting practices and reproducibility potential of a cohort of published studies in computational knee biomechanics

Reproducible research serves as a pillar of the scientific method and is a foundation for scientific advancement. However, estimates for irreproducibility of preclinical science range from 75% to 90%. The importance of reproducible science has not been assessed in the context of mechanics-based modeling of human joints such as the knee, despite this being an area that has seen dramatic growth. Framed in the context of five experienced teams currently documenting knee modeling procedures, the aim of this study was to evaluate reporting and the perceived potential for reproducibility across studies the teams viewed as important contributions to the literature. A cohort of studies was selected by polling, which resulted in an assessment of nine studies as opposed to a broader analysis across the literature. Using a published checklist for reporting of modeling features, the cohort was evaluated for both "reporting" and their potential to be "reproduced," which was delineated into six major modeling categories and three subcategories. Logistic regression analysis revealed that for individual modeling categories, the proportion of "reported" occurrences ranged from 0.31, 95% confidence interval (CI) [0.23, 0.41] to 0.77, 95% CI: [0.68, 0.86]. The proportion of whether a category was perceived as "reproducible" ranged from 0.22, 95% CI: [0.15, 0.31] to 0.44, 95% CI: [0.35, 0.55]. The relatively low ratios highlight an opportunity to improve reporting and reproducibility of knee modeling studies. Ongoing efforts, including our findings, contribute to a dialogue that facilitates adoption of practices that provide both credibility and translation possibilities.

Oxymatrine Protects Chondrocytes against IL-1β-triggered Apoptosis in Vitro and Inhibits Osteoarthritis in Mice Model

Background

Osteoarthritis (OA) is a multifactorial disease with various risk factors, resulting in the degeneration of articular cartilage and whole joints. However, to date, no effective disease-modifying therapy for OA has been developed. Oxymatrine (OMT) is associated with many pharmacological effects, including anti-inflammatory, antiapoptotic, and antioxidative properties. However, the role of OMT in OA remains unclear.

Materials and Methods

An IL-1β-induced chondrocyte model and anterior cruciate ligament transection (ACLT)-induced murine model of OA were constructed. The effect of OMT on chondrocyte viability was assessed using the CCK-8 assay. The protein level was assessed by Western blot analysis, and the apoptosis rate was assessed by flow cytometry in vitro and TUNEL staining in OA model mice. The effect of OMT on the degradation of articular cartilage in ACLT-induced OA mice was assessed by histological analysis.

Results

OMT at 0–2 mg/mL showed no conspicuous cytotoxicity on chondrocytes after 24 hours of incubation. OMT at 0.5, 1, and 2 mg/mL inhibited IL-1β-triggered apoptosis, upregulated MMP13, MMP9, and Col X, and upregulated Col II in chondrocytes in vitro. OMT represses the NF-κB signaling cascade in IL-1β-triggered chondrocytes in vitro. In an in vivo study, OMT decreased the apoptosis rate of chondrocytes and exerted a protective effect against the degradation of articular cartilage in ACLT-triggered OA mice.

Conclusion

OMT plays a protective role against chondrocyte injury induced by IL-1β in vitro or ACLT in vivo. OMT may play a role in chondrocytes during OA by inhibiting NF-κB signaling by decreasing the phosphorylation of p65 and IκB. OMT treatment may be a promising chondroprotective approach to delay OA cartilage progression.

A method for labelling lesions for machine learning and some new observations on osteochondrosis in computed tomographic scans of four pig joints

Background

Osteochondrosis is a major cause of leg weakness in pigs. Selection against osteochondrosis is currently based on manual scoring of computed tomographic (CT) scans for the presence of osteochondrosis manifesta lesions. It would be advantageous if osteochondrosis could be diagnosed automatically, through artificial intelligence methods using machine learning. The aim of this study was to describe a method for labelling articular osteochondrosis lesions in CT scans of four pig joints to guide development of future machine learning algorithms, and to report new observations made during the labelling process. The shoulder, elbow, stifle and hock joints were evaluated in CT scans of 201 pigs.

Results

Six thousand two hundred fifty osteochondrosis manifesta and cyst-like lesions were labelled in 201 pigs representing a total volume of 211,721.83 mm³. The per-joint prevalence of osteochondrosis ranged from 64.7% in the hock to 100% in the stifle joint. The lowest number of lesions was found in the hock joint at 208 lesions, and the highest number of lesions was found in the stifle joint at 4306 lesions. The mean volume per lesion ranged from 26.21 mm³ in the shoulder to 42.06 mm³ in the elbow joint. Pigs with the highest number of lesions had small lesions, whereas pigs with few lesions frequently had large lesions, that have the potential to become clinically significant. In the stifle joint, lesion number had a moderate negative correlation with mean lesion volume at r = − 0.54, p < 0.001.

Conclusions

The described labelling method is an important step towards developing a machine learning algorithm that will enable automated diagnosis of osteochondrosis manifesta and cyst-like lesions. Both lesion number and volume should be considered during breeding selection. The apparent inverse relationship between lesion number and volume warrants further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-022-03426-x.

Antiosteoporotic Nanohydroxyapatite Zoledronate Scaffold Seeded with Bone Marrow Mesenchymal Stromal Cells for Bone Regeneration: A 3D In Vitro Model

Background: Bisphosphonates are widely employed drugs for the treatment of pathologies with high bone resorption, such as osteoporosis, and display a great affinity for calcium ions and apatitic substrates. Here, we aimed to investigate the potentiality of zoledronate functionalized hydroxyapatite nanocrystals (HAZOL) to promote bone regeneration by stimulating adhesion, viability, metabolic activity and osteogenic commitment of human bone marrow derived mesenchymal stromal cells (hMSCs). Methods: we adopted an advanced three-dimensional (3D) in vitro fracture healing model to study porous scaffolds: hMSCs were seeded onto the scaffolds that, after three days, were cut in halves and unseeded scaffolds were placed between the two halves. Scaffold characterization by X-ray diffraction, transmission and scanning electron microscopy analyses and cell morphology, viability, osteogenic differentiation and extracellular matrix deposition were evaluated after 3, 7 and 10 days of culture. Results: Electron microscopy showed a porous and interconnected structure and a uniform cell layer spread onto scaffolds. Scaffolds were able to support cell growth and cells progressively colonized the whole inserts in absence of cytotoxic effects. Osteogenic commitment and gene expression of hMSCs were enhanced with higher expressions of ALPL, COL1A1, BGLAP, RUNX2 and Osterix genes. Conclusion: Although some limitations affect the present study (e.g., the lack of longer experimental times, of mechanical stimulus or pathological microenvironment), the obtained results with the adopted experimental setup suggested that zoledronate functionalized scaffolds (GHAZOL) might sustain not only cell proliferation, but positively influence osteogenic differentiation and activity if employed in bone fracture healing.

Gender-Related Aspects in Osteoarthritis Development and Progression: A Review

Osteoarthritis (OA) is a common degenerative joint disease treated mostly symptomatically before approaching its definitive treatment, joint arthroplasty. The rapidly growing prevalence of OA highlights the urgent need for a more efficient treatment strategy and boosts research into the mechanisms of OA incidence and progression. As a multifactorial disease, many aspects have been investigated as contributors to OA onset and progression. Differences in gender appear to play a role in the natural history of the disease, since female sex is known to increase the susceptibility to its development. The aim of the present review is to investigate the cues associated with gender by analyzing various hormonal, anatomical, molecular, and biomechanical parameters, as well as their differences between sexes. Our findings reveal the possible implications of gender in OA onset and progression and provide evidence for gaps in the current state of art, thus suggesting future research directions.

Evidence-Based Network Modelling to Simulate Nucleus Pulposus Multicellular Activity in Different Nutritional and Pro-Inflammatory Environments

Initiation of intervertebral disc degeneration is thought to be biologically driven. This reflects a process, where biochemical and mechanical stimuli affect cell activity (CA) that compromise the tissue strength over time. Experimental research enhanced our understanding about the effect of such stimuli on different CA, such as protein synthesis or mRNA expression. However, it is still unclear how cells respond to their native environment that consists of a “cocktail” of different stimuli that might locally vary. This work presents an interdisciplinary approach of experimental and in silico research to approximate Nucleus Pulposus CA within multifactorial biochemical environments. Thereby, the biochemical key stimuli glucose, pH, and the proinflammatory cytokines TNF-α and IL1β were considered that were experimentally shown to critically affect CA. To this end, a Nucleus Pulposus multicellular system was modelled. It integrated experimental findings from in vitro studies of human or bovine Nucleus Pulposus cells, to relate the individual effects of targeted stimuli to alterations in CA. Unknown stimulus-CA relationships were obtained through own experimental 3D cultures of bovine Nucleus Pulposus cells in alginate beads. Translation of experimental findings into suitable parameters for network modelling approaches was achieved thanks to a new numerical approach to estimate the individual sensitivity of a CA to each stimulus type. Hence, the effect of each stimulus type on a specific CA was assessed and integrated to approximate a multifactorial stimulus environment. Tackled CA were the mRNA expressions of Aggrecan, Collagen types I & II, MMP3, and ADAMTS4. CA was assessed for four different proinflammatory cell states; non-inflamed and inflamed for IL1β, TNF-α or both IL1β&TNF-α. Inflamed cell clusters were eventually predicted in a multicellular 3D agent-based model. Experimental results showed that glucose had no significant impact on proinflammatory cytokine or ADAMTS4 mRNA expression, whereas TNF-α caused a significant catabolic shift in most explored CA. In silico results showed that the presented methodology to estimate the sensitivity of a CA to a stimulus type importantly improved qualitative model predictions. However, more stimuli and/or further experimental knowledge need to be integrated, especially regarding predictions about the possible progression of inflammatory environments under adverse nutritional conditions. Tackling the multicellular level is a new and promising approach to estimate manifold responses of intervertebral disc cells. Such a top-down high-level network modelling approach allows to obtain information about relevant stimulus environments for a specific CA and could be shown to be suitable to tackle complex biological systems, including different proinflammatory cell states. The development of this methodology required a close interaction with experimental research. Thereby, specific experimental needs were derived from systematic in silico approaches and obtained results were directly used to enhance model predictions, which reflects a novelty in this research field. Eventually, the presented methodology provides modelling solutions suitable for multiscale approaches to contribute to a better understanding about dynamics over multiple spatial scales. Future work should focus on an amplification of the stimulus environment by integrating more key relevant stimuli, such as mechanical loading parameters, in order to better approximate native physiological environments.

Guide to mechanical characterization of articular cartilage and hydrogel constructs based on a systematic in silico parameter sensitivity analysis

Osteoarthritis is a whole joint disease with cartilage degeneration being an important manifestation. Tissue engineering treatment is a solution for repairing cartilage defects by implantation of chondrocyte-laden hydrogel constructs within the defect. In silico models have recently been introduced to simulate and optimize the design of these constructs. These models require accurate knowledge on the mechanical properties of the hydrogel constructs and cartilage explants, which are challenging to obtain due to their anisotropic structure and time-dependent behaviour. We performed a systematic in silico parameter sensitivity analysis to find the most efficient unconfined compression testing protocols for mechanical characterization of hydrogel constructs and cartilage explants, with a minimum number of tests but maximum identifiability of the material parameters. The construct and explant were thereby modelled as porohyperelastic and fibril-reinforced poroelastic materials, respectively. Three commonly used loading regimes were simulated in Abaqus (ramp, relaxation and dynamic loading) with varying compressive strain magnitudes and rates. From these virtual experiments, the resulting material parameters were obtained for each combination using a numerical inverse identification scheme. For hydrogels, maximum sensitivity to the different material parameters was found when using a single step ramp loading (20% compression with 10%/s rate) followed by 15 min relaxation. For cartilage explants, a two-stepped ramp loading (10% compression with 10%/s rate and 10% compression with 1%/s rate), each step followed by 15 min relaxation, yielded the maximum sensitivity to the different material parameters. With these protocols, the material parameters could be retrieved with the lowest amount of uncertainty (hydrogel: < 2% and cartilage: < 6%). These specific results and the overall methodology can be used to optimize mechanical testing protocols to yield reliable material parameters for in silico models of cartilage and hydrogel constructs.

The mechanical influence of bone spicules in the osteochondral junction: A finite element modelling study

While much has been done to study how cartilage responds to mechanical loading, as well as modelling such responses, arguably less has been accomplished around the mechanics of the cartilage-bone junction. Previously, it has been reported that the presence of bony spicules invading the zone of calcified cartilage, preceded the formation of new subchondral bone and the advancing of the cement line (Thambyah and Broom in Osteoarthr Cartil 17:456-463, 2009). In this study, the morphology and frequency of bone spicules in the cartilage-bone interface of osteochondral beams subjected to three-point bending were modelled, and the results are discussed within the context of biomechanical theories on bone formation. It was found that the stress and strain magnitudes, and their distribution were sensitive to the presence and number of spicules. Spicule numbers and shape were shown to affect the strain energy density (SED) distribution in the areas of the cement line adjacent to spicules. Stresses, strains and SED analyses thus provided evidence that the mechanical environment with the addition of spicules promotes bone formation in the cartilage-bone junction.

Multiscale modeling in disease

Multiscale computational modeling aims to connect the complex networks of effects at different length and/or time scales. For example, these networks often include intracellular molecular signaling, crosstalk, and other interactions between neighboring cell populations, and higher levels of emergent phenomena across different regions of tissues and among collections of tissues or organs interacting with each other in the whole body. Recent applications of multiscale modeling across intracellular, cellular, and/or tissue levels are highlighted here. These models incorporated the roles of biochemical and biomechanical modulation in processes that are implicated in the mechanisms of several diseases including fibrosis, joint and bone diseases, respiratory infectious diseases, and cancers.

Towards the Experimentally-Informed In Silico Nozzle Design Optimization for Extrusion-Based Bioprinting of Shear-Thinning Hydrogels

Research in bioprinting is booming due to its potential in addressing several manufacturing challenges in regenerative medicine. However, there are still many hurdles to overcome to guarantee cell survival and good printability. For the 3D extrusion-based bioprinting, cell viability is amongst one of the lowest of all the bioprinting techniques and is strongly influenced by various factors including the shear stress in the print nozzle. The goal of this study is to quantify, by means of in silico modeling, the mechanical environment experienced by the bioink during the printing process. Two ubiquitous nozzle shapes, conical and blunted, were considered, as well as three common hydrogels with material properties spanning from almost Newtonian to highly shear-thinning materials following the power-law behavior: Alginate-Gelatin, Alginate and PF127. Comprehensive in silico testing of all combinations of nozzle geometry variations and hydrogels was achieved by combining a design of experiments approach (DoE) with a computational fluid dynamics (CFD) of the printing process, analyzed through a machine learning approach named Gaussian Process. Available experimental results were used to validate the CFD model and justify the use of shear stress as a surrogate for cell survival in this study. The lower and middle nozzle radius, lower nozzle length and the material properties, alone and combined, were identified as the major influencing factors affecting shear stress, and therefore cell viability, during printing. These results were successfully compared with those of reported experiments testing viability for different nozzle geometry parameters under constant flow rate or constant pressure. The in silico 3D bioprinting platform developed in this study offers the potential to assist and accelerate further development of 3D bioprinting.

An in silico Framework of Cartilage Degeneration That Integrates Fibril Reorientation and Degradation Along With Altered Hydration and Fixed Charge Density Loss

Injurious mechanical loading of articular cartilage and associated lesions compromise the mechanical and structural integrity of joints and contribute to the onset and progression of cartilage degeneration leading to osteoarthritis (OA). Despite extensive in vitro and in vivo research, it remains unclear how the changes in cartilage composition and structure that occur during cartilage degeneration after injury, interact. Recently, in silico techniques provide a unique integrated platform to investigate the causal mechanisms by which the local mechanical environment of injured cartilage drives cartilage degeneration. Here, we introduce a novel integrated Cartilage Adaptive REorientation Degeneration (CARED) algorithm to predict the interaction between degenerative variations in main cartilage constituents, namely collagen fibril disorganization and degradation, proteoglycan (PG) loss, and change in water content. The algorithm iteratively interacts with a finite element (FE) model of a cartilage explant, with and without variable depth to full-thickness defects. In these FE models, intact and injured explants were subjected to normal (2 MPa unconfined compression in 0.1 s) and injurious mechanical loading (4 MPa unconfined compression in 0.1 s). Depending on the mechanical response of the FE model, the collagen fibril orientation and density, PG and water content were iteratively updated. In the CARED model, fixed charge density (FCD) loss and increased water content were related to decrease in PG content. Our model predictions were consistent with earlier experimental studies. In the intact explant model, minimal degenerative changes were observed under normal loading, while the injurious loading caused a reorientation of collagen fibrils toward the direction perpendicular to the surface, intense collagen degradation at the surface, and intense PG loss in the superficial and middle zones. In the injured explant models, normal loading induced intense collagen degradation, collagen reorientation, and PG depletion both on the surface and around the lesion. Our results confirm that the cartilage lesion depth is a crucial parameter affecting tissue degeneration, even under physiological loading conditions. The results suggest that potential fibril reorientation might prevent or slow down fibril degradation under conditions in which the tissue mechanical homeostasis is perturbed like the presence of defects or injurious loading.

An alternative ex vivo method to evaluate the osseointegration of Ti-6Al-4V alloy also combined with collagen

Due to the increasing number of orthopedic implantation surgery and advancements in biomaterial manufacturing, chemistry and topography, there is an increasing need of reliable and rapid methods for the preclinical investigation of osseointegration and bone ingrowth. Implant surface composition and topography increase osteogenicity, osteoinductivity, osteoconductivity and osseointegration of a prosthesis. Among the biomaterials used to manufacture an orthopedic prosthesis, titanium alloy (Ti-6Al-4V) is the most used. Type I collagen (COLL I) induces cell function, adhesion, differentiation and bone extracellular matrix component secretion and it is reported to improve osseointegration if immobilized on the alloy surface. The aim of the present study was to evaluate the feasibility of an alternative ex vivo model, developed by culturing rabbit cortical bone segments with Ti-6Al-4V alloy cylinders (Ti-POR), fabricated through the process of electron beam melting (EBM), to evaluate osseointegration. In addition, a comparison was made with Ti-POR coated with COLL I (Ti-POR-COLL) to evaluate osseointegration in terms of bone-to-implant contact (BIC) and new bone formation (nBAr/TAr) at 30, 60 and 90 d of culture. After 30 and 60 d of culture, BIC and nBAr/TAr resulted significantly higher in Ti-POR-COLL implants than in Ti-POR. No differences have been found at 90 d of culture. With the developed model it was possible to distinguish the biomaterial properties and behavior. This study defined and confirmed for the first time the validity of the alternative ex vivo method to evaluate osseointegration and that COLL I improves osseointegration and bone growth of Ti-6Al-4V fabricated through EBM.

Multiscale Regulation of the Intervertebral Disc: Achievements in Experimental, In Silico, and Regenerative Research

Intervertebral disc (IVD) degeneration is a major risk factor of low back pain. It is defined by a progressive loss of the IVD structure and functionality, leading to severe impairments with restricted treatment options due to the highly demanding mechanical exposure of the IVD. Degenerative changes in the IVD usually increase with age but at an accelerated rate in some individuals. To understand the initiation and progression of this disease, it is crucial to identify key top-down and bottom-up regulations' processes, across the cell, tissue, and organ levels, in health and disease. Owing to unremitting investigation of experimental research, the comprehension of detailed cell signaling pathways and their effect on matrix turnover significantly rose. Likewise, in silico research substantially contributed to a holistic understanding of spatiotemporal effects and complex, multifactorial interactions within the IVD. Together with important achievements in the research of biomaterials, manifold promising approaches for regenerative treatment options were presented over the last years. This review provides an integrative analysis of the current knowledge about (1) the multiscale function and regulation of the IVD in health and disease, (2) the possible regenerative strategies, and (3) the in silico models that shall eventually support the development of advanced therapies.

Tools for computational analysis of moving boundary problems in cellular mechanobiology

A cell's ability to change shape is one of the most fundamental biological processes and is essential for maintaining healthy organisms. When the ability to control shape goes awry, it often results in a diseased system. As such, it is important to understand the mechanisms that allow a cell to sense and respond to its environment so as to maintain cellular shape homeostasis. Because of the inherent complexity of the system, computational models that are based on sound theoretical understanding of the biochemistry and biomechanics and that use experimentally measured parameters are an essential tool. These models involve an inherent feedback, whereby shape is determined by the action of regulatory signals whose spatial distribution depends on the shape. To carry out computational simulations of these moving boundary problems requires special computational techniques. A variety of alternative approaches, depending on the type and scale of question being asked, have been used to simulate various biological processes, including cell motility, division, mechanosensation, and cell engulfment. In general, these models consider the forces that act on the system (both internally generated, or externally imposed) and the mechanical properties of the cell that resist these forces. Moving forward, making these techniques more accessible to the non-expert will help improve interdisciplinary research thereby providing new insight into important biological processes that affect human health. This article is categorized under: Cancer > Cancer>Computational Models Cancer > Cancer>Molecular and Cellular Physiology.

Classification, Categorization, and Algorithms for Articular Cartilage Defects

There is a critical unmet need in the clinical implementation of valid preventative and therapeutic strategies for patients with articular cartilage pathology based on the significant gap in understanding of the relationships between diagnostic data, disease progression, patient-related variables, and symptoms. In this article, the current state of classification and categorization for articular cartilage pathology is discussed with particular focus on machine learning methods and the authors propose a bedside-bench-bedside approach with highly quantitative techniques as a solution to these hurdles. Leveraging computational learning with available data toward articular cartilage pathology patient phenotyping holds promise for clinical research and will likely be an important tool to identify translational solutions into evidence-based clinical applications to benefit patients. Recommendations for successful implementation of these approaches include using standardized definitions of articular cartilage, to include characterization of depth, size, location, and number; using measurements that minimize subjectivity or validated patient-reported outcome measures; considering not just the articular cartilage pathology but the whole joint, and the patient perception and perspective. Application of this approach through a multistep process by a multidisciplinary team of clinicians and scientists holds promise for validating disease mechanism-based phenotypes toward clinically relevant understanding of articular cartilage pathology for evidence-based application to orthopaedic practice.