Background:Intervertebral disc (IVD) degeneration is a major contributor to low back pain, yet its initiating factors remain unclear. While the individual effects of pro-inflammatory cytokines and mechanical loading on IVDs have been studied, their combined impact is poorly understood. This study investigated how dynamic compression and torsion interact with interleukin-1 beta (IL-1β) and its inhibitor, interleukin-1 receptor antagonist (IL-1Ra), using bovine IVDs in an ex vivo organ culture system. Methods:Whole bovine caudal IVDs were cultured for one week in a custom bioreactor applying diurnal dynamic compression (0.1-0.5 MPa) and torsion (±6°) under three media conditions: physiological, catabolic (10 ng/mL IL-1β), and inhibitory (10 ng/mL IL-1Ra). Static compression (0.1 MPa) served as control. 3 T magnetic resonance imaging (MRI) was used pre- and post-culture for imaging and segmentation using 3DSlicer. Subject-personalized finite element (FE) models were generated via morphing algorithms and coupled with a parallel network (PN) model to analyze metabolite transport and its impact on gene expression. Outcomes included disc height, glycosaminoglycan (GAG) content, qPCR, and cell metabolic activity. Results & Conclusions:Degenerative changes were detected in all treatment groups. Results of decreased disc height, hydration, and ACAN expression, alongside increased MMP-13, indicated that the applied loading was supraphysiological and induced catabolic responses. IL-1Ra, at the given dose, did not counteract degeneration. MRI-based FE modeling effectively captured patterns of tissue consolidation and degeneration, providing valuable insights into IVD responses under combined mechanical and inflammatory stress. This integrative platform highlights the importance of modeling complex IVD environments and may inform the design of improved anti-catabolic therapies.

Network models are convenient to represent in a mechanistic way the complexity of cell biological activity. Dynamic simulations of such networks might require approximations of equation parameters through reverse engineering, numerous and costly experimental research, and/or have limited capacity to explore cell responses to chronic, dose-dependent stimulus exposure. Here we present a mechanistic methodology allowing the simulation of interrelated cell responses of multicellular systems to multifactorial stimuli with dose-and time dependent network links. The mathematical framework is called the Parallel Networks (PN)-Methodology. It consists of a novel concept, where multicellular systems are described as many relatively small feed-forward networks acting in parallel. Each parallel network is calculated through a specifically designed, analytically resolvable algebraic equation that integrates individual regulation with dose and possible time dependency. Applied to intervertebral disc multicellular systems, virtual environments of multifactorial stimuli and multiple cell responses simulating daily moving activities, and to microgravity could successfully be created. The PN-Methodology stands for a one-of-a-kind mathematical methodology to approximate dynamics of complex multicellular systems over long periods of time at low computational costs.

BACKGROUND AND OBJECTIVE:Intervertebral disc (IVD) degeneration (IDD) is closely linked to impaired nutrient transport within the avascular disc, critically affecting cell viability and tissue health. Finite element (FE) modeling of mechano-metabolic-transport phenomena is widely used to explore disc nutrition, degeneration, and metabolic stress. However, traditional sequential coupling approaches remain limited by reduced accuracy, long runtimes, and implementation complexity. This study aimed to develop and validate an efficient and accurate multi-species transport simulation method integrated within mechanical analyses, thereby overcoming existing limitations for large-scale patient-personalized (PP) cohort simulations. METHODS:We developed a multi-species diffusion-reaction User ELement (UEL) subroutine in Abaqus that simultaneously simulates oxygen, glucose, and lactate transport coupled with mechanical deformation within a single stiffness matrix. This UEL was integrated with mechanical models through submodeling, allowing for independent time stepping and consistent mechanical baselines. Accuracy and efficiency were validated against sequential Abaqus User THermal MATerial (UMATHT) simulations using a symmetric diffusion chamber with four cell density groups (2, 4, 8, and 16 million ×10-3 cells/mm3) and a PP FE IVD model. Parameters influencing cell viability - glucose decay rate, glucose threshold, and pH-induced cell decay - were optimized for each group. RESULTS:UEL and UMATHT methods yielded nearly identical solute concentration predictions, with normalized root mean square error (NRMSE) of 0.2% and maximum relative error of 1%. Our UEL reduced computational time by over 60% (from >30 to ∼11 h). Cell viability model optimization improved the accuracy for the four cell density groups by 20.8%, 16.4%, 5.4%, and 25.0%, respectively. UEL also provided smoother and more accurate solute concentration fields by utilizing all the 20 nodes of second-order elements, surpassing UMATHT's first-order corner-node limitation. CONCLUSIONS:The proposed UEL significantly improves computational efficiency and accuracy in multi-species nutrient transport simulations. Its integration with mechanical analyses and submodeling technique enables large-scale, standardized, patient-specific studies. By openly sharing our validated Mechano-Transport subroutines (GitHub: https://github.com/bmmbUPF/abaqusIVD) and compatible FE meshes (SpineView Repository: https://ivd.spineview.upf.edu/), we support reproducibility, facilitating automated simulations using clinical magnetic resonance imaging, and promoting standardized methodologies across diverse research initiatives. Ultimately, these contributions advance patient stratification and personalized spine care.

ABSTRACT Background Intervertebral disc degeneration (IDD) reflects a poorly characterized shift from anabolic matrix renewal to catabolic breakdown. Fragmentary experimental and modeling efforts leave many signaling routes unresolved; a consolidated map is required to guide hypothesis‐driven studies. Methods A literature‐curated regulatory network model (RNM) for human nucleus pulposus (NP) cells was assembled from PubMed and enriched with interactions from STRING, KEGG and R&D Systems. The static graph was converted to a semi‐quantitative dynamical system. Simulations examined responses to interleukin‐1β (IL‐1β) and the Toll‐like receptor (TLR) and were benchmarked against time‐resolved proteomics from 2D NP monolayers and 3D alginate cultures representing healthy and degenerated discs. Single‐node perturbations assessed the impact of targeted inhibition on catabolic output. Results The final network contains 82 proteins and 193 directed edges; 41.4% of supporting evidence derives from NP‐specific studies, the highest for any cell type. Baseline activity depicts a non‐degenerate disc, with high expression of anabolic mediators and low expression of catabolic enzymes. Both IL‐1β and TLR elicited strong c‐Jun and p65 NF‐κB activation in silico and in vitro. The model simulated IκBα depletion, whereas its increase in the experimental study shows the biological agreement among in silico and in vitro simulations. Conclusions This RNM unifies scattered IDD data into a validated dynamic framework that mirrors NP signaling. The publicly available model provides a foundation for multiscale studies linking molecular events to disc mechanics and for prioritizing therapeutic interventions.

Background:The vertebral bony endplate (BEP) plays a role in regulating both mechanical load transfer and nutrient transport to the intervertebral disc, functions strongly influenced by its microstructural porosity. Variations in porosity impact bone strength, elasticity, and solute diffusion, affecting its overall mechanical competence. Methods:A semi-automated modeling workflow was established to quantify pore geometry and connectivity of the BEP and relate them to zone-specific mechanical and transport properties using relationships previously established in the literature. Six to eight BEPs from four bovine subjects were imaged using micro-computed tomography. The central zone and two peripheral zones of each BEP were segmented, and their microstructural properties, including tortuosity, porosity, and pore radii, were quantified using the pipeline to assess BEP heterogeneity. These metrics were then used to infer the BEP's zone-dependent density, Young's modulus, compressive strength, and shear modulus. Results:The BEPs showed spatial, intra-subject, and inter-subject morphological variability, which affected their predicted mechanical and transport properties. These findings highlight the limitations of purely idealized BEP representations in silico simulations and support the incorporation of uncertainty modeling strategies that account for physiologically relevant variations in the BEP. Conclusion:This semi-automated modeling approach represents a tangible step toward more realistic in silico simulations of vertebral endplate function.

The intervertebral disc (IVD) and the articular cartilage (AC) are specialized, load-bearing tissues critical for spinal flexibility and joint mobility, respectively. Both tissues are characterized by their avascular nature and abundant extracellular matrix (ECM). They heavily rely on precisely regulated anabolic and catabolic processes to maintain structural integrity and functional performance. Disturbances can contribute to IVD degeneration and osteoarthritis that represent leading global causes of disability and pose substantial challenges to healthcare systems worldwide. One of the key regulators of IVD and AC homeostasis is mechanotransduction, the process through which mechanical cues are translated into biological responses. More recently, daily oscillations of genes and proteins implicated in mechanotransduction-related intracellular pathways started to gain attention. Such oscillations are driven by circadian rhythms and seem to affect the IVD and AC in health and degeneration. Circadian rhythms regulate the oscillatory expression of genes essential for matrix homeostasis, including those involved in nutrient transport, inflammation control, and cellular metabolism. Alterations of these rhythms, due to aging, inflammation, or lifestyle, might impair tissue homeostasis. Mechanotransduction and circadian rhythms interact reciprocally, as daily patterns of mechanical stimuli can entrain circadian rhythms, and circadian rhythms modulate cellular mechanosensitivity, optimizing responses to daily activity-rest cycles. This review synthesizes recent advances in understanding these intertwined mechano-circadian interactions within IVD and AC. It discusses the implications for degenerative disease progression and highlights potential therapeutic strategies leveraging chronotherapeutics and mechanobiology to preserve tissue function and improve the management of musculoskeletal disorders.

IntroductionTotal knee replacement (TKR) is the standard surgical treatment for severely symptomatic knee osteoarthritis (KOA), but treatment decisions for patients with moderate radiographic severity (Kellgren–Lawrence grades 2–3) remain variable. Radiographic grading and patient-reported outcomes do not fully capture the functional heterogeneity between treatment pathways. Stance-phase kinematics may provide objective descriptors for stratification while remaining more accessible than kinetic measurements.MethodsSixty-six women with moderate KOA (27 scheduled for TKR, 39 managed conservatively) underwent gait analysis. Stance-phase angles for 12 variables were extracted at three sub-phases (loading response, mid-stance, terminal stance). Group differences were assessed via repeated-measures multifactorial MANOVA. Gait speed was evaluated for confounding (Rothman’s criteria) and included as a covariate in MANCOVA. Stance-phase change variables trained a Random Forest classifier with nested cross-validation and exhaustive feature selection. Associations between descriptors and clinical outcomes were examined via univariate logistic regression. Clinical construct consistency was assessed against the Osteoarthritis Initiative dataset.ResultsThe multivariate analysis identified significant Time × Treatment Group interactions for knee flexion–extension, pelvic tilt, and back flexion–extension after FDR correction. Gait speed differed between groups (TKR slower, Cohen’s d=−1.02) and met Rothman criteria; after adjustment, pelvic tilt retained the lowest uncorrected p-value with no significant Time × Velocity interaction. Random Forest reached a mean ROC-AUC of 0.81 ± 0.06, with pelvic tilt selected in 80% of folds. Six descriptors showed significant associations with joint pain (highest AUC: pelvic tilt 0.80, shoulder rotation 0.81); none reached comparable association with the remaining outcomes. Pelvic tilt showed inverse association with depression and positive association with pain. Adding velocity as a candidate feature did not change Random Forest performance, indicating redundancy with kinematic descriptors at the individual level.ConclusionStance-phase kinematics differentiate two groups of women with moderate KOA who share radiographic severity but follow different treatment pathways. Pelvic tilt emerged as the most consistent descriptor across multivariate, individual-level, and pain-related analyses, and the most robust under adjustment for gait speed. Kinematics offer an accessible alternative to kinetic measurements for describing clinical heterogeneity in moderate KOA, although integration with imaging and patient-reported outcomes is necessary before clinical implementation.

Background:Proximal Junctional Failure (PJF) is a common complication in Adult Spine Deformity (ASD) surgeries, often leading to reoperations. While revision surgeries with osteotomies carry high complication rate of 34.8%, alternatives such as hardware proximal extension may increase PJF risk in patients with severe Global Alignment and Proportion (GAP) scores. Implant Density Reduction (IDR) has emerged to mitigate PJF risk. This study assessed the impact of IDR on PJF risk and explored sub-optimal strategies. Methods:Two patient-personalized Finite Element (FE) models were used and expanded into a virtual cohort. Implant Density (ID), rod material, bone quality, and GAP were systematically varied. Thoracolumbar FE models were developed using structured Statistical Shape Modeling (SSM). Biomechanical metrics of Intervertebral Disk (IVD) fiber strain, Screw Pull-out Force (SPF), and rod stress, were evaluated. Trade-off analyses could determine sub-optimal configurations avoiding PJF. Results:IDR significantly decreased IVD strain (up to -70%) and improved screw stability (up to +142%), for patients with titanium (Ti) rods and normal bone. However, IDR effectiveness was limited for cases with GAP ≥12, osteoporotic bone, and Cobalt-Chromium (Cr-Co) rods. No IDR strategy could prevent PJF for cases with GAP 12 or 13, regardless of rod type. For cases with GAP 11 and Upper Instrumented Vertebra (UIV) at T10, IDR was effective with only Ti rods. For cases with GAP 13 and UIV at T3, none of IDRs, independent of rod material, offered benefit. Notably, Ti rods may support IDR-based risk reduction in borderline cases, such as GAP 12, UIV at T3. Conclusions:IDR is a promising strategy to lower PJF risk in high-risk spine revision cases, though its effectiveness depends on surgical and anatomical factors. This study provides an in-silico tool to support personalized surgical planning and guide future clinical trials aimed at reducing reoperations and healthcare costs.

Introduction Clinical trial protocols in intervertebral disc degeneration (IDD) vary in quality, which may influence trial outcomes. While the SPIRIT checklist provides a standard for protocol quality, adherence varies. This study aimed to assess IDD trial protocols’ adherence to SPIRIT guidelines and to use machine learning to identify which checklist items predict trial completion, thereby providing evidence-based guidance for improving trial success. Methods We conducted a predictive modelling study of 18 randomized controlled IDD trials from ClinicalTrials.gov. Trial success was defined as "completed" (n=11) versus "failed" (n=7). Adherence was assessed using a validated 64-item SPIRIT checklist. Four machine learning algorithms were employed with cross-validation due to the small sample size. Feature selection on the highest performing model identified the SPIRIT items the most predictive of clinical trial completion, with SHAP values used for model interpretability. Results Adherence varied widely across protocols (range: 11.1%–100%). The XGBoost model achieved the highest performance (ROC-AUC: 0.839, Average Precision: 0.773). The most predictive SPIRIT items related to statistical planning, safety oversight, and operational procedures, including handling of missing data, data monitoring committee structure, and plans for managing adverse events. These findings suggest that specific protocol components, particularly those related to analysis and safety planning, are associated with trial completion in IDD research. Improving adherence to these areas may help reduce trial failure and enhance research efficiency. Discussion Our machine learning analysis successfully identified critical SPIRIT items strongly associated with trial completion in IDD research. The predominance of statistical methodology, safety monitoring, and operational planning items as top predictors provides actionable guidance for protocol development. Prioritizing analytical planning, comprehensive safety oversight, and detailed operational protocols can significantly improve trial success rates and reduce research waste in IDD clinical research.

Background and Objective : The finite element method is widely used for studying the intervertebral disc at the organ level due to its ability to model complex geometries. An indispensable requirement for proper modelling of the intervertebral disc is a reliable porohyperelastic framework that captures the elaborate underlying mechanics. The increased complexity of such models requires significant computational power that is available within high-performance computing systems. The objective of this study is to present such a framework, validated both against literature and experiments, aiming to enable intervertebral disc research to benefit from state-of-the-art computational resources. Methods: In the context of this work, we implement a biphasic model that captures the mechanical response of the intricate, tissue-dependent models of the solid phase along with the hydrostatic pressure effects of the fluid phase. The tissue-dependent models involve the hyperelastic ground substance, fibrillar reinforcement, and osmotic swelling. The derived porohyperelastic, staggered scheme is implemented in Alya, a finite element code targeted at high-performance computing applications. The formulation is subsequently verified and validated by comparing the results of consolidation simulations with literature data for simulations and experiments using either generic or patient-specific geometries. Additionally, in-house experiments are replicated, evaluating the model’s ability to simulate alternating loading. Finally, the implementation’s circadian response is compared to previous implementation of similar material models in commercial software. Results: Results align well with experimental and literature findings in terms of disc height reduction (4% error), intradiscal pressure (14% error) and disc bulging. Validating the patient-specific geometry results in 4% and 7% deviation in measuring height loss. Simulations show excellent agreement with in-house experimental results, with less than 1% error regarding height reduction. Finally, the comparison to similar, published, earlier implementation in commercial software unveils excellent agreement of less than 1% error for the water content during circadian simulations. Simulation times are reported at 4 min per circadian cycle in the supercomputer Marenostrum V. Conclusions: This work presents a clear and validated formulation for simulating porohyperelastic materials based on assumptions that comply with the non-linear elasticity theory. The implementation in Alya enables intervertebral disc research to benefit from high-performance computing systems.

ABSTRACT Background Intervertebral disc (IVD) degeneration is characterized by a disruption of the balance between anabolic and catabolic cellular processes. Within the nucleus pulposus (NP), this involves increased levels of the pro‐inflammatory cytokines interleukin 1beta (IL1B) and tumor necrosis factor (TNF) and an upregulation of the protease families matrix metalloproteinase (MMP) and a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS). Primary inhibitors of these proteases are the tissue inhibitors of matrix metalloproteinases (TIMP). This work aims at contributing to a better understanding of the dynamics among proteases, TIMP, and pro‐inflammatory cytokines within the complex, multifactorial environment of the NP. Methods The Parallel Network (PN)‐Methodology was used to estimate relative mRNA expressions of TIMP1–3, MMP3, and ADAMTS4 for five simulated human activities: walking, sitting, jogging, hiking with 20 kg extra weight, and exposure to high vibration. Simulations were executed for nutrient conditions in non‐ and early‐degenerated IVD approximations. To estimate the impact of cytokines, the PN‐Methodology inferred relative protein levels for IL1B and TNF, reintegrated as secondary stimuli into the network. Results TIMP1 and TIMP2 expressions were found to be overall lower than TIMP3 expression. In the absence of pro‐inflammatory cytokines, MMP3 and/or ADAMTS4 expressions were strongly downregulated in all conditions but vibration and hiking with extra weight. Pro‐inflammatory cytokine exposure resulted in an impaired inhibition of MMP3, rather than of ADAMTS4, progressively rising with increasing nutrient deprivation. TNF mRNA was less expressed than IL1B. However, at the protein level, TNF was mainly responsible for the catabolic shift in the simulated pro‐inflammatory environment. Overall, results agreed with previous experimental findings. Conclusions The PN‐Methodology successfully allowed the exploration of the relative dynamics of TIMP and protease regulations in different mechanical, nutritional, and inflammatory environments in the NP. It shall stand as a comprehensive tool to integrate in vitro model results in IVD research and approximate NP cell activities in complex multifactorial environments.

STUDY DESIGN:Retrospective and cross-sectional study. OBJECTIVE:The study aims to develop an open software for lumbar spine image analysis enabling no-code approach to lumbar spine segmentation, grading, and intervertebral Disc Height Index (DHI) calculations with robust evaluation of the application on 6 external data sets from diverse geographical regions. BACKGROUND:The data sets used include NFBC1966 (Finland), HKDDC (Hong Kong), TwinsUK (UK), CETIR (Spain), NCSD (Hungary), SPIDER (Netherlands), and Mendeley (global). Thirty participants from each data set were sampled for external evaluation, and NFBC1966 was used for training. Annotation was performed on T2-weighted mid-sagittal slices of vertebral bodies L1 to S1 and intervertebral discs L1/2 to L5/S1. MATERIALS AND METHODS:Open Lumbar Spine Image Analysis (OLSIA) application was developed to include no-code approach tools for automated segmentation, grading, DHI calculation, and batch processing capabilities by integrating the deep learning (DL) models. DL models were trained on the NFBC1966 data set with augmentation (histogram clipping, median filtering, and geometric scaling) to improve generalization. Interrater agreement was assessed using dice similarity coefficient (DSC), Bland-Altman (BA) analysis for DHI measurements and a paired t test for statistical significance. RESULTS:Our application demonstrated 222-fold improvement in processing time compared with performing manually lumbar spine segmentation, grading and DHI calculation tasks. OLSIA's segmentation performance exhibited close correspondence with the interrater agreement across all 6 external data sets. Interrater reliability was high (mean DSC >90). Although paired t test on DHI measurements is significant ( P < 0.05), the mean difference (0.02) of DHI from the BA plots indicates low systematic bias. CONCLUSION:We introduced OLSIA, a user-friendly interface for lumbar spine segmentation, grading, and intervertebral DHI calculation. OLSIA empowers researchers from diverse backgrounds to efficiently use the no-code tools to accelerate their radiomics and lumbar spine image analysis workflows.

Intervertebral disc degeneration (IDD) results from an imbalance between anabolic and catabolic processes in the extracellular matrix (ECM). Due to complex biochemical interactions, a comprehensive understanding is needed. This study presents a regulatory network model (RNM) for nucleus pulposus cells (NPC), representing normal intervertebral disc (IVD) conditions. The RNM includes 33 proteins, and 153 interactions based on literature, incorporating key NPC regulatory mechanisms. A semi-quantitative approach calculates the basal steady state, accurately reflecting normal NPC activity. Model validation through published studies replicated pro-catabolic and pro-anabolic shifts, emphasizing the roles of transforming growth factor beta (TGF-β) and interleukin-1 receptor antagonist (IL-1Ra) in ECM regulation. This IVD RNM is a valuable tool for predicting IDD progression, offering insights into ECM degradation mechanisms and guiding experimental research on IVD health and degeneration.

Intervertebral disc (IVD) degeneration (IDD) disrupts extracellular matrix homeostasis, through intricate biological processes. A prior protein-protein interaction network for nucleus pulposus cells (NPC) predicted cytokine responses but lacked resolution to capture intracellular signaling dynamics. Understanding these pathways is crucial for uncovering regulatory mechanisms and developing targeted IDD therapies. We reviewed 260 peer-reviewed papers to identify key signaling pathways in IVD regulation, focusing on NPC and general cell data. Supplemented by Reactome and KEGG databases, we selected key ligands (BMP, GDF5, IGF1, IL-1β, IL-1Ra, IL-4, IL-6, IL-10, IL-12, IL-17, IFN-?, TGF-β, TNF) and traced their signaling pathways from receptor binding to nuclear translocation. The knowledge-based network was translated into a dynamic model, through semi-quantitative interpolations of Boolean simulations. The model was assessed against 3D (trauma) and monolayer (degenerate IVD) human NPC cultures, under IL-1β stimulation. The network model included 67 signaling proteins and transcription factors, with 185 interactions, representing a non-degenerate state of NPC IVD (Fig. 1A), with high levels of: structural proteins (ACAN, COL2A); growth factors (TGF-β, GDF5, IGF1); transcription factor Sox9; SMAD2/3, a downstream mediator of TGF-β signaling. Simultaneously, it predicted low levels of: pro-inflammatory cytokines (IL-1β, IL-6, IL-17, IL-12, IFN-?, TNF); degrading enzymes (ADAMTS4/5, VEGF, MMPs); chemokines (CCL3/4); stress-activated kinases (p38, JNK); mediators of pro-inflammatory responses (TLR); transcription factor AP-1; p65 subunit of NF-?B. Upon IL-1β perturbation, model simulations agreed with the in vitro results that showed increased expression of c-Jun and p65 in 3D and monolayer culture. When activating TGF-β in the IL-1β stimulated network, ACAN, COL2A, GDF5, IL-1Ra, Sox9, Akt1 and SIRT1 were increased. The network model uniquely incorporated intracellular signaling dynamics and represented successfully the expected homeostatic NPC state in a non-degenerate IVD. Simulations aligned with experimental studies in human NPCs from IVD which demonstrated increased expression of c-Jun and p65 subunit of NF-?B following IL-1β stimulation, passing a key falsification test. Simulated rescue by TGF-β was, furthermore, consistent with prior findings in mouse and rat models. While experimental studies in human NPC remain necessary to explore signaling proteins for IDD treatments, this model offers a valuable tool to understand mechanistically intricate signaling mechanisms and guide IVD research.

IntroductionIntervertebral disc (IVD) degeneration is a primary contributor to low back pain, with nutritional stress due to the IVD’s avascularity recognized as a key factor. Solute transport within the disc relies predominantly on diffusion, which is governed by tissue morphology and mechanical deformation. However, the interplay between disc geometry, poro-mechanical strain, diffusion, and degeneration remains incompletely characterized. Previous specimen-specific models have captured inter-subject variability in metabolite transport, but the isolated effects of disc height and degeneration-dependent material composition have not been systematically assessed. Moreover, although strain-dependent diffusion coefficients are commonly modeled as porosity functions, the role of intra-element diffusivity gradients (∇D), arising under large deformation, has been largely overlooked.MethodsThe present study focuses on poro-mechanical finite element (FE) models of three patient-personalized L4-L5 lumbar IVD geometries, representing varying heights categorized as thin, medium, and tall IVDs. Three days of physiological mechanical load cycles, comprising 8 hours of rest and 16 hours of activity, were simulated, under both ’healthy’ (Pfirrmann grade 1) and degenerated (Pfirrmann grade 3) tissue conditions.ResultsSimulation outcomes demonstrated that a one-third reduction in disc height (relative to medium height) led to >30% increases in oxygen and glucose concentrations and ≥20% decreases in lactate levels, particularly in the nucleus and anterior regions. Conversely, a one-third height increase resulted in >30% reductions in oxygen and glucose and a corresponding rise in lactate levels. These deviations were more pronounced in degenerated tissues, highlighting the synergistic role of morphology and matrix integrity in determining metabolic homeostasis. Importantly, the inclusion of ∇D in the diffusion-reaction model produced negligible changes in solute concentration profiles.DiscussionThese findings underscore the predominant influence of disc geometry and matrix composition on IVD metabolic homeostasis, suggesting limited relevance of the (∇D) term in practical simulations. Simplified diffusion models, without (∇D), may be sufficient for future IVD mechano-transport FE modeling.

Little is known about cartilaginous endplate (CEP) mechanobiology or how it changes in a catabolic microenvironment, partly due to difficulties in conducting mechanotransduction in vitro. Recent studies have found blended collagen–agarose hydrogels to offer improved mechanotransduction in chondrocytes compared to agarose alone. It was hypothesized that blended collagen–agarose hydrogels would be sufficient to improve the mechanobiological response in CEP cells relative to that in agarose alone, while maintaining the chondrocyte phenotype and ability to respond to pro-inflammatory stimulation. Thus, human CEP cells were seeded into blended 2% agarose and 2 mg/mL type I collagen hydrogels, followed by culture with dynamic compression up to 7% and stimulation with TNF. Results confirmed CEP cells retained a rounded phenotype and high cell viability during culture in blended collagen–agarose hydrogels. Additionally, TNF induced a catabolic response through downregulation of pericellular marker COL6A1 and anabolic markers ACAN and COL2A1. No significant changes were seen due to dynamic compression, suggesting addition of collagen to agarose was not sufficient to induce mechanotransduction in human CEP cells in this study. However, blended collagen–agarose hydrogels increased stiffness by 4× and gene expression of key cartilage marker SOX9 and physioosmotic mechanosensor TRPV4, offering an improvement on agarose alone.

Introduction:Dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis. Advances in 2D-3D modelling to generate patient-specific 3D-DXA models out of DXA images enable accurate volumetric representations of the femur, with potential for fracture risk prediction when combined with finite element (FE) analyses. This study evaluates the ability of 3D-DXA-based FE models to discriminate hip fractures under side-fall loading. Methods:We used a retrospective case-control study including 128 women, 64 of whom suffered a hip fracture. Mechanical descriptors, including strength, nonlinear deformation, residual displacement, and energy absorption under elastic-plastic assumptions, were derived from force-displacement curves. Results:The area under the receiver operating characteristic curve (AUROC) showed that strength and trabecular volumetric bone mineral density (vBMD) equally discriminated between fracture and control subjects. Residual displacement due to plastic strain accumulation at failure emerged as a key descriptor which, when combined with strength, significantly improved fracture discrimination (ΔAUROC = 0.11 vs. areal bone mineral density (aBMD); ΔAUROC = 0.08 vs. trabecular vBMD). Discussion:These findings highlight the potential of 3D-DXA and FE modelling to improve fracture assessment within current DXA-based clinical workflows.

Intervertebral disc (IVD) degeneration is the leading cause of low back pain in young adults, and the cartilaginous endplate (CEP) is likely to play a key role in early IVD degeneration. To elucidate the effects of pro-inflammatory cytokines on the mechanobiology of the CEP, human CEP cells were seeded into 2% agarose, dynamically compressed up to 7%, and stimulated with tumor necrosis factor (TNF). It was hypothesized that dynamic compression would be sufficient to induce anabolism, while stimulation with TNF would induce catabolism. TNF was sufficient to induce a catabolic, time-dependent response in human CEP cells through downregulation of anabolic gene expression and increased secretion of pro-inflammatory proteins associated with herniated discs, bacteria inhibition, and pain. However, 7% strain or scaffold material, agarose, may not lead to full activation of integrins and downregulation of pro-inflammatory pathways, demonstrated in part through the unchanged gene expression of integrin subunits α5 and β1.

Osteoporotic hip fracture represents a high social and economic burden in western countries. Pharmacological treatments aim to limit/reverse the loss of bone mineral density (BMD). BMD is monitored through dual energy X-ray absorptiometry (DXA). Biomechanical analysis, through 3D-DXA finite element (FE) femur models, has been shown to potentially improve fracture risk prediction. Yet, the capability of 3D-DXA FE simulations to capture the effects of pharmacological treatments on bone strength remains unexplored. Thus, this study aims to evaluate simulated changes in bone strength in subjects with different osteoporosis treatments using 3D-DXA FE models. A cohort of 155 subjects was used to generate the patient-specific FE models. Osteoporosis treatments included Alendronate (AL, n=54), Denosumab (DMAB, n=33), Teriparatide (TPTD, n=31), and Naïve (NAÏVE, n=37). Bone was modelled as BMD-dependent elasto-plastic material. Lateral fall was simulated, and bone FE-strength changes from baseline were assessed. Integral FE-strength significantly increased by 3.1% and 4.0% in the AL and DMAB groups, respectively. Trabecular and cortical FE-strength significantly increased by 2.2% and 1.9%, respectively with DMAB. Load-bearing capacity increased in both the cortical and trabecular bone of the femoral neck with DMAB and AL, while it only increased in the trabecular bone with TPTD. 3D-DXA FE analysis might help clinicians to better monitor the effects of pharmacological treatments and potentially improve personalised treatment plans for subjects with osteoporosis.

ABSTRACT Background This study investigates the native presence and potential anabolic effects of interleukin (IL)‐4 and IL‐10 in the human intervertebral disc (IVD). Methods Human nucleus pulposus (NP) cells cultured in 3D from trauma and degenerate IVDs and NP explants were stimulated with 10 ng/mL IL‐4, IL‐10, or each in combination with 1 ng/mL IL‐1β stimulation. The role of IL‐4 and IL‐10 in the IVD was evaluated using immunohistochemistry, gene expression, and Luminex multiplex immunoassay proteomics (73 secreted) and phosphoproteomics (21 phosphorylated proteins). Results IL‐4, IL‐4R, and IL‐10R expression and localization in human cartilage endplate tissue were demonstrated for the first time. No significant gene expression changes were noted under IL‐4 or IL‐10 stimulation. However, IL‐1β stimulation significantly increased MMP3, COX2, TIMP1, and TRPV4 expression in NP cells from trauma IVDs. Combined IL‐4 and IL‐1β treatment induced a significant increase in protein secretion of IL‐1α, IL‐7, IL‐16, IL‐17F, IL‐18, IFNγ, TNF, ST2, PROK1, bFGF2, and stem cell factor exclusively in NP cells from degenerated IVDs. Conversely, the secretome profile of explants revealed an IL‐4–mediated decrease in CXCL13 following treatment with IL‐1β. Combined IL‐10 and IL‐1β treatment increased neurotrophic growth factor secretion compared with IL‐10 baseline. Conclusions The NP cell phenotype affects the pleiotropic role of IL‐4, which can induce a pro‐inflammatory response in the presence of catabolic stimuli and enhance the effects of IL‐1β in degenerated IVDs. Environmental factors, including 3D culture and hypoxia, may alter IL‐4's role. Finally, IL‐10's potential neurotrophic effects under catabolic stimuli warrant further investigation to clarify its role in IVD degeneration.