[Influence analysis of aging of spinal segment on the mechanical behavior of vertebral cortex].

The risk of vertebral cortical shell fracture increases with aging. However, it remains unclear how aging contributes to cortex fracture at present. The aim of this study is to make understanding of the mechanism of how the spinal aging influences the cortical shell strain. Two finite element (FE) models of spinal segments (mildly and fully aged) were created, and then were compared to the FE models of the healthy spinal segment. The FE models of the aged spinal segments were generated by modifying both the geometry of the intervertebral disc (IVD) and the material properties of the spinal components. To find out under which case the cortical shell strain was influenced more, we created two types of FE model comparison methods: one with changes only in the spinal material properties and the other with changes only in the IVD geometry. The results showed that the cortical shell strains increased with aging and that compared to the changes of IVD geometry, the changes of spinal material property have a higher influence on the cortical shell strains. This study may suggest that for the prevention and treatment of vertebral cortex fracture, the augmentation of the vertebral body is a more effective treatment.

Exfoliated carbon nitride nanosheets decorated with NiS as an efficient noble-metal-free visible-light-driven photocatalyst for hydrogen evolution.

A binary composite composed of two dimensional (2D) ultrathin carbon nitride (C3N4) nanosheets and NiS nanoparticles was synthesized and applied as a noble-metal-free photocatalyst for hydrogen evolution under visible light irradiation. The ultrathin nanosheets of C3N4 were obtained by a facile liquid exfoliation method and used as 2D supports for the deposition of NiS nanoparticles. In the binary composite, the ultrathin C3N4 nanosheets acted as a visible light responding semiconductor, and the NiS nanoparticles served as a noble-metal-free cocatalyst. The binary composite with an optimized composition gave a hydrogen evolution rate comparable to that of Pt modified C3N4. Moreover, compared to bulk C3N4, the exfoliated C3N4 nanosheets distinctly improve the photocatalytic performance for hydrogen evolution. The photocatalytic results combined with photoelectrochemical experiments show that C3N4 with an ultrathin structure promotes the electron-hole separation and transportation during the process of the photoinduced hydrogen evolution. This study displays a facile method to build a low-cost but effective photocatalyst for hydrogen production under visible light irradiation.

Reduced graphene oxide modified highly ordered TiO2 nanotube arrays photoelectrode with enhanced photoelectrocatalytic performance under visible-light irradiation.

In this paper, reduced graphene oxide modified highly ordered TiO2 nanotube arrays (RGO-TNTs) have been fabricated and used for photoelectrocatalytic (PEC) degradation of organic pollutants under visible light irradiation. Firstly, the RGO-TNT electrode was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman, FT-IR, X-ray photoelectron spectroscopy (XPS) and UV-vis diffuse reflectance spectroscopy. The responsive photocurrent and electrochemical impedance spectroscopy (EIS) results indicated that our present RGO-TNTs displayed superior photoresponsive and electron transfer performances compared with bare TNTs. Moreover, by comparison with bare TNT electrode, the RGO-TNT arrays showed stable and evidently improved PEC activity for degradation of methyl orange (MO) under visible light illumination. This might be attributed to the introduction of RGO, which extended the absorption edge and promoted electron-hole separation in the PEC process. Furthermore, owing to the synergetic effect of photocatalysis and electrocatalysis in the PEC process, the efficiency of PEC process (3.0 × 10(-3) min(-1)) is ca. 7.9 and 2.5 times faster than that of the electrochemical process (3.8 × 10(-4) min(-1)) and photocatalytic process (1.2 × 10(-3) min(-1)), respectively. Our investigation likely provides new opportunities for developing stable and efficient one-dimensional graphene modified TNT-based catalysts for PEC degradation of organic pollutants under visible light illumination.

Finite element model of ocular adduction with unconstrained globe translation.

Details of the anatomy and behavior of the structures responsible for human eye movements have been extensively elaborated since the first modern biomechanical models were introduced. Based on these findings, a finite element model of human ocular adduction is developed based on connective anatomy and measured optic nerve (ON) properties, as well as active contractility of bilaminar extraocular muscles (EOMs), but incorporating the novel feature that globe translation is not otherwise constrained so that realistic kinematics can be simulated. Anatomy of the hemisymmetric model is defined by magnetic resonance imaging. The globe is modeled as suspended by anatomically realistic connective tissues, orbital fat, and contiguous ON. The model incorporates a material subroutine that implements active EOM contraction based on fiber twitch characteristics. Starting from the initial condition of 26° adduction, the medial rectus (MR) muscle was commanded to contract as the lateral rectus (LR) relaxed. We alternatively modeled absence or presence of orbital fat. During pursuit-like adduction from 26 to 32°, the globe translated 0.52 mm posteriorly and 0.1 mm medially with orbital fat present, but 1.2 mm posteriorly and 0.1 mm medially without fat. Maximum principal strains in the optic disk and peripapillary reached 0.05-0.06, and von-Mises stress 96 kPa. Tension in the MR orbital layer was ~ 24 g-force after 6° adduction, but only ~ 3 gm-f in the whole LR. This physiologically plausible simulation of EOM activation in an anatomically realistic globe suspensory system demonstrates that orbital connective tissues and fat are integral to the biomechanics of adduction, including loading by the ON.

Effect of blast orientation, multi-point blasts, and repetitive blasts on brain injury.

Explosions in the battlefield can result in brain damage. Research on the effects of shock waves on brain tissue mainly focuses on the effects of single-orientation blast waves, while there have been few studies on the dynamic response of the human brain to directional explosions in different planes, multi-point explosions and repetitive explosions. Therefore, the brain tissue response and the intracranial pressure (ICP) caused by different blast loadings were numerically simulated using the CONWEP method. In the study of the blast in different directions, the lateral explosion blast wave was found to cause greater ICP than did blasts from other directions. When multi-point explosions occurred in the sagittal plane simultaneously, the ICP in the temporal lobe increased by 37.8 % and the ICP in the parietal lobe decreased by 17.6 %. When multi-point explosions occurred in the horizontal plane, the ICP in the frontal lobe increased by 61.8 % and the ICP in the temporal lobe increased by 12.2 %. In a study of repetitive explosions, the maximum ICP of the second blast increased by 40.6 % over that of the first blast, and that of the third blast increased by 61.2 % over that of the second blast. The ICP on the brain tissue from repetitive blasts can exceed 200 % of that of a single explosion blast wave.

Computational modelling of articular joints with biphasic cartilage: recent advances, challenges and opportunities.

Biphasic models have been widely used to simulate the time-dependent biomechanical response of soft tissues. Modelling techniques of joints with biphasic weight-bearing soft tissues have been markedly improved over the last decade, enhancing our understanding of the function, degenerative mechanism and outcomes of interventions of joints. This paper reviews the recent advances, challenges and opportunities in computational models of joints with biphasic weight-bearing soft tissues. The review begins with an introduction of the function and degeneration of joints from a biomechanical aspect. Different constitutive models of articular cartilage, in particular biphasic materials, are illustrated in the context of the study of contact mechanics in joints. Approaches, advances and major findings of biphasic models of the hip and knee are presented, followed by a discussion of the challenges awaiting to be addressed, including the convergence issue, high computational cost and inadequate validation. Finally, opportunities and clinical insights in the areas of subject-specific modeling and tissue engineering are provided and discussed.

Predicting the effective compressive modulus of human cancellous bone using the convolutional neural network method.

The efficient prediction of biomechanical properties of bone plays an important role in the assessment of bone quality. However, the present techniques are either of low accuracy or of high complexity for the clinical application. The present study aims to investigate the predictive ability of the evolving convolutional neural network (CNN) technique in predicting the effective compressive modulus of porous bone structures. The T11/T12/L1 segments of thirty-five female cadavers were scanned using the HR-pQCT scanner and the images obtained from it were used to generate 10896 2 D bone samples, in which only the cancellous bony parts were processed and investigated. The corresponding 10896 heterogeneous finite-element (FE) models were generated, and then a CNN model was constructed and trained using the predictions of the FE analysis as the ground truths. Then the remaining 260 bone samples generated from the initial HR-pQCT images were used to test the predictive power of the CNN model. The results show that the coefficient of the determinant (R2) from the linear correlation between the CNN and FE predicted elastic modulus is 0.95, which is much higher than that from the correlation between the BMD and the FE predictions (R2 = 0.65). Furthermore, the 95th and 50th percentiles of relative prediction error are below 0.28 and 0.09, respectively. In the conclusion, the CNN model can efficiently predict the effective compressive modulus of human cancellous bone and can be used as a promising and clinically applicable method to evaluate the mechanical quality of porous bone.

The genes regulating sensitivity of tumor cells to T cell-mediated killing: could they be potential personalized immunotherapeutic targets in head and neck squamous cell carcinoma?

OBJECTIVE: To identify the pivotal genes, specifically the STTK genes, that govern the sensitivity of tumor cells to T cell-mediated killing in Head and Neck Squamous Cell Carcinoma (HNSC). METHODS: The differentially expressed genes (DEGs) in HNSC and STTK genes were overlapped to obtain the DE-STTK genes. Univariate and LASSO regression analyses were conducted to identify the pivotal DE-STTK genes that serve as hubs in HNSC (i.e., hub DE-STTK genes). The risk model was established to divide HNSC tumor samples into high- and low-risk groups based on the hub DE-STTK genes. Further investigations were carried out by examing the expression level, prognostic values, diagnostic values, enriched signaling pathways, correlation with tumor mutation burden (TMB), and association with tumor immune infiltration cells (TIICs). RESULTS: A total of 71 genes were found to be overlapped between DEGs in HNSC and STTK genes. Lasso regression analysis identified 9 hub genes which were MYF6, AATF, AURKA, CXCL9, DPM2, MYO1B, NCBP2, TNFRSF12A, and TRAF1. The network analysis of hub DE-STTK genes-pathway reveals that these 9 hub genes exhibit enrichment in multiple signaling pathways, including toll-like receptor signaling, TNF signaling, NF-kappa B signaling, cytokine-cytokine receptor interaction, spliceosome, mRNA surveillance pathway, nucleocytoplasmic transport, GPI-anchor biosynthesis, as well as N-Glycan biosynthesis. The Pearson correlation analysis showed that the majority of correlations between 9 hub DE-STTK genes and immune cells were positive. CONCLUSION: The 9 identified hub DE-STTK genes (MYF6, AATF, AURKA, CXCL9, DPM2, MYO1B, NCBP2, TNFRSF12A, and TRAF1) are presumptively implicated in the modulation of tumor immunity in HNSC. These genes, along with their enriched pathways, hold promise as potential personalized immunotherapeutic targets for the treatment of HNSC, thereby offering novel avenues for therapeutic intervention in this malignancy.

Characteristic analysis of bio-oil from penicillin fermentation residue by catalytic pyrolysis.

The hazardous waste penicillin fermentation residue (PR) is a huge hazard to the environment. The bio-oil produced by the pyrolysis of the penicillin fermentation residue has the potential to become a biofuel in the future. This paper studied the pyrolysis characteristics of PR at 400°C ∼700°C. According to the weight loss and weight loss rate of PR, the whole process of pyrolysis can be divided into three stages for analysis: dehydration and volatilization, initial pyrolysis, and pyrolytic char formation. The experimental results showed that the yield of the liquid phase is the highest (33.11%) at 600°C. GC-MS analysis results showed that high temperature is beneficial to reduce the generation of oxygenated hydrocarbons (73%∼31%) and the yield of nitrogenous compounds gradually increased (19%∼43%); the yield of hydrocarbons was low in 400°C∼600°C pyrolysis (2%∼5%) but significantly increased around 700°C (22%). In the temperature range of 400°C to 700°C, the proportion of C5-C13 in bio-oil gradually increased (26%-64%), and the proportion of C14-C22 gradually decreased (47%-16%). The catalyst can increase the proportion of hydrocarbons in the bio-oil component. And the Fe2O3/HZSM-5 mixed catalyst has a significant reduction effect on oxygen-containing hydrocarbons and nitrogen-containing compounds.

Biomechanical study of internal fixation methods for femoral neck fractures based on Pauwels angle.

Objective: To select the most appropriate internal fixation method based on the Pauwels angle, in order to provide a new concept for clinical accurate treatment of femoral neck fractures (FNFs). Methods: FNFs models of Pauwels 30 ° ; 40 ° ; 50 ° ; 60 ° were created respectively. For Pauwels ≤ 50 ° , 1, 2 and 3 Cannulated Compression Screws (CCS) and Porous Tantalum Screws (PTS) were used to fix the fracture for the models. For Pauwels 60 ° , 3CCS and Medial Buttress Plate (MBP) combined with 1, 2 and 3CCS were used to fix the fracture. Based on the results of the finite element (FE) analysis, the biomechanical properties of each model were compared by analyzing and evaluating the following four parameters: maximal stress of the bone (MBS), maximal stress of the implants (MIS), maximal displacement of bone (MBD), interfragmentary motion (IFM). Results: At Pauwels 30 ° , the larger parameters were found in 1CCS, which was 94.8 MPa (MBS), 307.7 MPa (MIS), 0.86 mm (MBD) and 0.36 mm (IFM). In 2CCS group, the parameters were 86.1 MPa (MBS), 254.4 MPa (MIS), 0.73 mm (MBD) and 0.27 mm (IFM), which were similar to those of PTS. At Pauwels 40 ° ; 50 ° , with the increase of the number of used CCS, accordingly, the parameters decreased. Particularly, the MIS (Pauwels 50 ° ) of 1CCS was 1,195.3 MPa, but the other were less than the yield range of the materials. At Pauwels 60 ° , the MBS of 3CCS group was 128.6 Mpa, which had the risk of failure. In 2CCS + MBP group, the parameters were 124.2 MPa (MBS), 602.5 MPa (MIS), 0.75 mm (MBD) and 0.48 mm (IFM), The model stability was significantly enhanced after adding MBP. Conclusion: Pauwels type Ⅰ (< 30 ° ) fractures can reduce the number of CCS, and PTS is an appropriate alternative treatment. For Pauwels type Ⅱ fractures ( 30 ° ∼ 50 ° ), the 3CCS fixation method is still recommended. For Pauwels type Ⅲ fractures (> 50 ° ), it is recommended to add MBP to the medial femoral neck and combine with 2CCS to establish a satisfactory fracture healing environment.

Modeling of active skeletal muscles: a 3D continuum approach incorporating multiple muscle interactions.

Skeletal muscles have a highly organized hierarchical structure, whose main function is to generate forces for movement and stability. To understand the complex heterogeneous behaviors of muscles, computational modeling has advanced as a non-invasive approach to evaluate relevant mechanical quantities. Aiming to improve musculoskeletal predictions, this paper presents a framework for modeling 3D deformable muscles that includes continuum constitutive representation, parametric determination, model validation, fiber distribution estimation, and integration of multiple muscles into a system level for joint motion simulation. The passive and active muscle properties were modeled based on the strain energy approach with Hill-type hyperelastic constitutive laws. A parametric study was conducted to validate the model using experimental datasets of passive and active rabbit leg muscles. The active muscle model with calibrated material parameters was then implemented to simulate knee bending during a squat with multiple quadriceps muscles. A computational fluid dynamics (CFD) fiber simulation approach was utilized to estimate the fiber arrangements for each muscle, and a cohesive contact approach was applied to simulate the interactions among muscles. The single muscle simulation results showed that both passive and active muscle elongation responses matched the range of the testing data. The dynamic simulation of knee flexion and extension showed the predictive capability of the model for estimating the active quadriceps responses, which indicates that the presented modeling pipeline is effective and stable for simulating multiple muscle configurations. This work provided an effective framework of a 3D continuum muscle model for complex muscle behavior simulation, which will facilitate additional computational and experimental studies of skeletal muscle mechanics. This study will offer valuable insight into the future development of multiscale neuromuscular models and applications of these models to a wide variety of relevant areas such as biomechanics and clinical research.

Retraction notice to influence of nucleotomy on the load sharing in the spinal facet joint under the loading scenarios of different human postures.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. The authors have failed to cite or to acknowledge prior work appropriately. (The author of that prior work preferred for its details not to be included in this notice.) One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Re-use of any data should be appropriately cited.

A Critical Review of Additive Manufacturing Techniques and Associated Biomaterials Used in Bone Tissue Engineering.

With the ability to fabricate complex structures while meeting individual needs, additive manufacturing (AM) offers unprecedented opportunities for bone tissue engineering in the biomedical field. However, traditional metal implants have many adverse effects due to their poor integration with host tissues, and therefore new material implants with porous structures are gradually being developed that are suitable for clinical medical applications. From the perspectives of additive manufacturing technology and materials, this article discusses a suitable manufacturing process for ideal materials for biological bone tissue engineering. It begins with a review of the methods and applicable materials in existing additive manufacturing technologies and their applications in biomedicine, introducing the advantages and disadvantages of various AM technologies. The properties of materials including metals and polymers, commonly used AM technologies, recent developments, and their applications in bone tissue engineering are discussed in detail and summarized. In addition, the main challenges for different metallic and polymer materials, such as biodegradability, anisotropy, growth factors to promote the osteogenic capacity, and enhancement of mechanical properties are also introduced. Finally, the development prospects for AM technologies and biomaterials in bone tissue engineering are considered.

Influence of the parameters of the convolutional neural network model in predicting the effective compressive modulus of porous structure.

In recent years, the convolutional neural network (CNN) technique has emerged as an efficient new method for designing porous structure, but a CNN model generally contains a large number of parameters, each of which could influence the predictive ability of the CNN model. Furthermore, there is no consensus on the setting of each parameter in the CNN model. Therefore, the present study aimed to investigate the sensitivity of the parameters in the CNN model for the prediction of the mechanical property of porous structures. 10,500 samples of porous structure were randomly generated, and their effective compressive moduli obtained from finite element analysis were used as the ground truths to construct and train a CNN model. 8,000 of the samples were used to train the CNN model, 2000 samples were used for the cross-validation of the CNN model and the remaining 500 new structures, which did not participate in the CNN training process, were used to test the predictive power of the CNN model. The sensitivity of the number of convolutional layers, the number of convolution kernels, the number of pooling layers, the number of fully connected layers and the optimizer in the CNN model were then investigated. The results showed that the optimizer has the largest influence on the training speed, while the fully connected layer has the least impact on the training speed. Additionally, the pooling layer has the largest impact on the predictive ability while the optimizer has the least impact on the predictive ability. In conclusion, the parameters of the CNN model play an important role in the performance of the CNN model and the parameter sensitivity analysis can help optimize the CNN model to increase the computational efficiency.

Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model.

The design of bionic bone scaffolds to mimic the behaviors of native bone tissue is crucial in clinical application, but such design is very challenging due to the complex behaviors of native bone tissues. In the present study, bionic bone scaffolds with the anisotropic mechanical properties similar to those of native bone tissues were successfully designed using a novel self-learning convolutional neural network (CNN) framework. The anisotropic mechanical property of bone was first calculated from the CT images of bone tissues. The CNN model constructed was trained and validated using the predictions from the heterogonous finite element (FE) models. The CNN model was then used to design the scaffold with the elasticity matrix matched to that of the replaced bone tissues. For the comparison, the bone scaffold was also designed using the conventional method. The results showed that the mechanical properties of scaffolds designed using the CNN model are closer to those of native bone tissues. In conclusion, the self-learning CNN framework can be used to design the anisotropic bone scaffolds and has a great potential in the clinical application.

Comparison of the design maps of TPMS based bone scaffolds using a computational modeling framework simultaneously considering various conditions.

In recent years, the triply periodic minimal surface (TPMS)-based scaffolds have been served as one of the crucial types of structures for biological replacements, the energy absorber, etc. Meanwhile, the development of additive manufacturing (AM) has facilitated the production of TPMS scaffolds with complex microstructures. However, the design maps of TPMS scaffolds, especially considering the AM constraints, remain unclear, which has hindered the design and application of TPMS scaffolds. The aims of the present study were to develop an efficient computational modeling framework for investigating the design maps of TPMS scaffolds simultaneously considering the AM constraints, the biological requirements, and the structural anisotropy. To demonstrate the computational framework, five widely-used topologies of the TPMS-based scaffolds (i.e. the Diamond, the Gyroid, the Fischer-Koch S, the F-RD, and the Schwarz P) were used, whose design maps for the surface-to-volume ratio and the effective elastic modulus were also investigated. The results showed that as the porosities increase, the design ranges of the surface-to-volume ratios decreases for all the structures. Compared with the effect of the constraint for the pore size, the minimal structural thickness for AM constraint has a greater effect on the surface-to-volume ratio. Regarding the elastic modulus, in the region of low porosity (approximately 0.5-0.7), the range for the effective elastic modulus of Schwarz P is the widest (approximately 2.24-32.6 GPa), but the Gyroid can achieve both high porosity and low effective elastic modulus (e.g. 0.61 GPa at the porosity of 0.90). These results and the method developed in the present study provided important basis and guidance for the design and application of the TPMS-based porous structures.

Cupric porphyrin frameworks on multi-junction silicon photocathodes to expedite the kinetics of CO2 turnover.

Photoelectrochemical CO2 reduction utilizing silicon-based photocathodes offers a promising route to directly store solar energy in chemical bonds, provoking the development of heterogeneous molecular catalysts with high turnover rates. Herein, an in situ surface transformation strategy is adopted to grow metal-organic frameworks (MOFs) on Si-based photocathodes, serving as catalytic scaffolds for boosting both the kinetics and selectivity of CO2 reduction. Benefitting from the multi-junctional configuration for enhanced charge separation and the porous MOF scaffold enriching redox-active metalloporphyrin sites, the Si photocathode demonstrates a high CO faradaic efficiency of 87% at a photocurrent density of 10.2 mA cm-2, which is among the best seen for heterogeneous molecular catalysts. This study highlights the exploitation of reticular chemistry and macrocycle complexes as Earth-abundant alternatives for catalyzing artificial photosynthesis.

Mechanism of the traumatic brain injury induced by blast wave using the energy assessment method.

Although blast-induced traumatic brain injury (b-TBI) is well recognized for its significance in the military population, the unique mechanisms of primary b-TBI remain undefined. The aim of the present study is to unveil the mechanism of the traumatic brain injury induced by the shock wave using the energy assessment method. First, the magnetic resonance images of the human head were processed to establish the finite element (FE) model of the human head including the skin, the skull, the brain and other structures. The simulation of the shock wave was implemented using the Coupled Eulerian-Lagrange (CEL) method, a fluid-solid coupling model of explosive shock wave-head. The coupled model was used to simulate the situations of head subjected to the frontal direction, the side direction and the rear direction by explosive shock wave. The predictions of the FE results were validated by comparing them with previous studies. Then, the degree of b-TBI in three different directions i.e., the frontal direction, the side direction and the rear direction, was assessed to investigate the influence of the direction of the shock wave on the b-TBI. Finally, the various energies including strain energy, kinetic energy, viscous energy and creep energy were extracted from the FE calculations for investigating the mechanism of b-TBI. The results showed that under the scenario of the shock wave, the brain injury was caused by the combined actions of acceleration of the head and the deformation of the skin& facial muscle tissues, the brain and the cerebrospinal fluid (CSF). In addition, it was also found that the dissipation capacity of the head was not sensitive to the impact direction. The present study provides important data and theoretical basis for the mechanism of b-TBI.

Au-activated N motifs in non-coherent cupric porphyrin metal organic frameworks for promoting and stabilizing ethylene production.

Direct implementation of metal-organic frameworks as the catalyst for CO2 electroreduction has been challenging due to issues such as poor conductivity, stability, and limited > 2e- products. In this study, Au nanoneedles are impregnated into a cupric porphyrin-based metal-organic framework by exploiting ligand carboxylates as the Au3+ -reducing agent, simultaneously cleaving the ligand-node linkage. Surprisingly, despite the lack of a coherent structure, the Au-inserted framework affords a superb ethylene selectivity up to 52.5% in Faradaic efficiency, ranking among the best for metal-organic frameworks reported in the literature. Through operando X-ray, infrared spectroscopies and density functional theory calculations, the enhanced ethylene selectivity is attributed to Au-activated nitrogen motifs in coordination with the Cu centers for C-C coupling at the metalloporphyrin sites. Furthermore, the Au-inserted catalyst demonstrates both improved structural and catalytic stability, ascribed to the altered charge conduction path that bypasses the incoherent framework. This study underlines the modulation of reticular metalloporphyrin structure by metal impregnation for steering the CO2 reduction reaction pathway.

Influence of nucleotomy on the load sharing in the spinal facet joint under the loading scenarios of different human postures.

One-in-five people suffer from chronic low back pain (LBP). The incidence of this disease has doubled since 1950s and affects not only the elderly, but also the young population. However, the mechanism of LBP is still unknown. A possible location where the LBP may develop is the facet joint and it has been revealed that the intervertebral disc (IVD) nucleotomy may be a trigger for LBP. The aim of the present study was to investigate the influence of IVD nucleotomy on the load sharing in the spinal facet joint under the loading scenarios of different postures. Finite element (FE) models of the intact and nucleotomised L4 - L5 spinal segments were generated from the clinical CT images. Seven human postures, including upright, 5° extension, 5° flexion, ± 6° lateral bending and ± 2° axial rotation, were simulated. The resultant forces in the fact joint were compared between the intact and the nucleotomised cases. It was revealed that the IVD nucleotomy significantly increased the forces in the facet joints under the loading scenarios of upright, 5° extension and 5° flexion. The IVD nucleotomy increased the force in the ipsilateral facet joint but decreased the force on the contralateral side under the loading scenarios of ± 2° axial rotation. However, the IVD nucleotomy made little influence on the resultant forces in both facet joints in the postures of ± 6° lateral bending. In conclusion, the IVD nucleotomy can cause an increase in the overall force in the facet joint, and thus may serve as a possible explanation for the LBP and a main contributing factor for the pain complaints.