Aims. Osteoporosis (OP) is a metabolic bone disease, characterized by a decrease in bone mineral density (BMD). However, the research of regulatory variants has been limited for BMD. In this study, we aimed to explore novel regulatory genetic variants associated with BMD. Methods. We conducted an integrative analysis of BMD genome-wide association study (GWAS) and regulatory single nucleotide polymorphism (rSNP) annotation information. Firstly, the discovery GWAS dataset and replication GWAS dataset were integrated with rSNP annotation database to obtain BMD associated SNP regulatory elements and SNP regulatory element-target gene (E-G) pairs, respectively. Then, the common genes were further subjected to HumanNet v2 to explore the biological effects. Results. Through discovery and replication integrative analysis for BMD GWAS and rSNP annotation database, we identified 36 common BMD-associated genes for BMD irrespective of regulatory elements, such as FAM3C (p. discovery GWAS. = 1.21 × 10. -25. , p. replication GWAS. = 1.80 × 10. -12. ), CCDC170 (p. discovery GWAS. = 1.23 × 10. -11. , p. replication GWAS. = 3.22 × 10. -9. ), and SOX6 (p. discovery GWAS. = 4.41 × 10. -15. , p. replication GWAS. = 6.57 × 10. -14. ). Then, for the 36 common target genes, multiple gene ontology (GO) terms were detected for BMD such as positive regulation of cartilage development (p = 9.27 × 10. -3. ) and positive regulation of chondrocyte differentiation (p = 9.27 × 10. -3. ). Conclusion. We explored the potential roles of rSNP in the genetic mechanisms of BMD and identified multiple candidate genes. Our study results support the implication of regulatory genetic variants in the development of OP. Cite this article: Bone Joint Res 2023;12(2):147–154

Human error is usually evaluated using statistical descriptions during radiographic annotation. The technological advances popularized the “non-human” landmarking techniques, such as deep learning, in which the error is presented in a confidence format that is not comparable to that of the human method. The region-based landmark definition makes an arbitrary “ground truth” point impossible. The differences in patients’ anatomies, radiograph qualities, and scales make the horizontal comparison difficult. There is a demand to quantify the manual landmarking error in a probability format. Taking the measurement of pelvic tilt (PT) as an example, this study recruited 115 sagittal pelvic radiographs for the measurement of two PTs. We proposed a method to unify the scale of images that allows horizontal comparisons of landmarks and calculated the maximum possible error using a density vector. Traditional descriptive statistics were also applied. All measurements showed excellent reliabilities (intraclass correlation coefficients > 0.9). Eighty-four measurements (6.09%) were qualified as wrong landmarks that failed to label the correct locations. Directional bias (systematic error) was identified due to cognitive differences between observers. By removing wrong labels and rotated pelves, the analysis quantified the error density as a “good doctor” performance and found 6.77°-11.76° maximum PT disagreement with 95% data points. The landmarks with excellent reliability still have a chance (at least 6.09% in our case) of making wrong landmark decisions. Identifying skeletal contours is at least 24.64% more accurate than estimating landmark locations. The landmark at a clear skeletal contour is more likely to generate systematic errors. Due to landmark ambiguity, a very careful surgeon measuring PT could make a maximum 11.76° random difference in 95% of cases, serving as a “good doctor benchmark” to qualify good landmarking techniques

The relationship of degeneration to symptoms has been questioned. MRI detects apparently similar disc degeneration and degenerative changes in subjects both with and without back pain. We aimed to overcome these problems by re-annotating MRIs from asymptomatic and symptomatic groups onto the same grading system. We analysed disc degeneration in pre-existing large MRI datasets. Their MRIs were all originally annotated on different scales. We re-annotated all MRIs independent of their initial grading system, using a verified, rapid automated MRI annotation system (SpineNet) which reported degeneration on the Pfirrmann (1-5) scale, and other degenerative features (herniation, endplate defects, marrow signs, spinal stenosis) as binary present/absent. We compared prevalence of degenerative features between symptomatics and asymptomatics. Pfirrmann degeneration grades in relation to age and spinal level were very similar for the two independent groups of symptomatics over all ages and spinal levels. Severe degenerative changes were significantly more prevalent in discs of symptomatics than asymptomatics in the caudal but not the rostral lumbar discs in subjects < 60 years. We found high co-existence of degenerative features in both populations. Degeneration was minimal in around 30% of symptomatics < 50 years. We confirmed age and disc level are significant in determining imaging differences between asymptomatic and symptomatic populations and should not be ignored. Automated analysis, by rapidly combining and comparing data from existing groups with MRIs and information on LBP, provides a way in which epidemiological and ‘big data’ analysis could be advanced without the expense of collecting new groups

Aims. This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Methods. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization. Results. A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms. Conclusion. The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets. Cite this article: Bone Joint Res 2024;13(2):66–82

Aims. Degenerative cervical spondylosis (DCS) is a common musculoskeletal disease that encompasses a wide range of progressive degenerative changes and affects all components of the cervical spine. DCS imposes very large social and economic burdens. However, its genetic basis remains elusive. Methods. Predicted whole-blood and skeletal muscle gene expression and genome-wide association study (GWAS) data from a DCS database were integrated, and functional summary-based imputation (FUSION) software was used on the integrated data. A transcriptome-wide association study (TWAS) was conducted using FUSION software to assess the association between predicted gene expression and DCS risk. The TWAS-identified genes were verified via comparison with differentially expressed genes (DEGs) in DCS RNA expression profiles in the Gene Expression Omnibus (GEO) (Accession Number: GSE153761). The Functional Mapping and Annotation (FUMA) tool for genome-wide association studies and Meta tools were used for gene functional enrichment and annotation analysis. Results. The TWAS detected 420 DCS genes with p < 0.05 in skeletal muscle, such as ribosomal protein S15A (RPS15A) (PTWAS = 0.001), and 110 genes in whole blood, such as selectin L (SELL) (PTWAS = 0.001). Comparison with the DCS RNA expression profile identified 12 common genes, including Apelin Receptor (APLNR) (PTWAS = 0.001, PDEG = 0.025). In total, 148 DCS-enriched Gene Ontology (GO) terms were identified, such as mast cell degranulation (GO:0043303); 15 DCS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified, such as the sphingolipid signalling pathway (ko04071). Nine terms, such as degradation of the extracellular matrix (R-HSA-1474228), were common to the TWAS enrichment results and the RNA expression profile. Conclusion. Our results identify putative susceptibility genes; these findings provide new ideas for exploration of the genetic mechanism of DCS development and new targets for preclinical intervention and clinical treatment. Cite this article: Bone Joint Res 2023;12(1):80–90

Aims. The metabolic variations between the cartilage of osteoarthritis (OA) and Kashin-Beck disease (KBD) remain largely unknown. Our study aimed to address this by conducting a comparative analysis of the metabolic profiles present in the cartilage of KBD and OA. Methods. Cartilage samples from patients with KBD (n = 10) and patients with OA (n = 10) were collected during total knee arthroplasty surgery. An untargeted metabolomics approach using liquid chromatography coupled with mass spectrometry (LC-MS) was conducted to investigate the metabolomics profiles of KBD and OA. LC-MS raw data files were converted into mzXML format and then processed by the XCMS, CAMERA, and metaX toolbox implemented with R software. The online Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate the metabolites by matching the exact molecular mass data of samples with those from the database. Results. A total of 807 ion features were identified for KBD and OA, including 577 positive (240 for upregulated and 337 for downregulated) and 230 negative (107 for upregulated and 123 for downregulated) ions. After annotation, LC-MS identified significant expressions of ten upregulated and eight downregulated second-level metabolites, and 183 upregulated and 162 downregulated first-level metabolites between KBD and OA. We identified differentially expressed second-level metabolites that are highly associated with cartilage damage, including dimethyl sulfoxide, uric acid, and betaine. These metabolites exist in sulphur metabolism, purine metabolism, and glycine, serine, and threonine metabolism. Conclusion. This comprehensive comparative analysis of metabolism in OA and KBD cartilage provides new evidence of differences in the pathogenetic mechanisms underlying cartilage damage in these two conditions. Cite this article: Bone Joint Res 2024;13(7):362–371

To detect early signs of infection infrared thermography has been suggested to provide quantitative information. Our vision is to invent a pin site infection thermographic surveillance tool for patients at home. A preliminary step to this goal is the aim of this study, to automate the process of locating the pin and detecting the pin sites in thermal images efficiently, exactly, and reliably for extracting pin site temperatures. A total of 1708 pin sites was investigated with Thermography and augmented by 9 different methods in to totally 10.409 images. The dataset was divided into a training set (n=8325), a validation set (n=1040), and a test set (n=1044) of images. The Pin Detection Model (PDM) was developed as follows: A You Only Look Once (YOLOv5) based object detection model with a Complete Detection Intersection over Union (CDIoU), it was pre-trained and finetuned by the through transfer learning. The basic performance of the YOLOv5 with CDIoU model was compared with other conventional models (FCOS and YOLOv4) for deep and transition learning to improve performance and precision. Maximum Temperature Extraction (MTE) Based on Region of Interest (ROI) for all pin sites was generated by the model. Inference of MTE using PDM with infected and un-infected datasets was investigated. An automatic tool that can identify and annotate pin sites on conventional images using bounding boxes was established. The bounding box was transferred to the infrared image. The PMD algorithm was built on YOLOv5 with CDIoU and has a precision of 0.976. The model offers the pin site detection in 1.8 milliseconds. The thermal data from ROI at the pin site was automatically extracted. These results enable automatic pin site annotation on thermography. The model tracks the correlation between temperature and infection from the detected pin sites and demonstrates it is a promising tool for automatic pin site detection and maximum temperature extraction for further infection studies. Our work for automatic pin site annotation on thermography paves the way for future research on infection assessment using thermography

Tennis elbow (lateral epicondylitis or lateral elbow tendinopathy) is a self-limiting condition in most patients. Surgery is often offered to patients who fail to improve with conservative treatment. However, there is no evidence to support the superiority of surgery over continued nonoperative care or no treatment. New evidence also suggests that the prognosis of tennis elbow is not influenced by the duration of symptoms, and that there is a 50% probability of recovery every three to four months. This finding challenges the belief that failed nonoperative care is an indication for surgery. In this annotation, we discuss the clinical and research implications of the benign clinical course of tennis elbow. Cite this article: Bone Joint J 2023;105-B(2):109–111

Aims. This study aimed, through bioinformatics analysis and in vitro experiment validation, to identify the key extracellular proteins of intervertebral disc degeneration (IDD). Methods. The gene expression profile of GSE23130 was downloaded from the Gene Expression Omnibus (GEO) database. Extracellular protein-differentially expressed genes (EP-DEGs) were screened by protein annotation databases, and we used Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to analyze the functions and pathways of EP-DEGs. STRING and Cytoscape were used to construct protein-protein interaction (PPI) networks and identify hub EP-DEGs. NetworkAnalyst was used to analyze transcription factors (TFs) and microRNAs (miRNAs) that regulate hub EP-DEGs. A search of the Drug Signatures Database (DSigDB) for hub EP-DEGs revealed multiple drug molecules and drug-target interactions. Results. A total of 56 EP-DEGs were identified in the differential expression analysis. EP-DEGs were enriched in the extracellular structure organization, ageing, collagen-activated signalling pathway, PI3K-Akt signalling pathway, and AGE-RAGE signalling pathway. PPI network analysis showed that the top ten hub EP-DEGs are closely related to IDD. Correlation analysis also demonstrated a significant correlation between the ten hub EP-DEGs (p＜0.05), which were selected to construct TF–gene interaction and TF–miRNA coregulatory networks. In addition, ten candidate drugs were screened for the treatment of IDD. Conclusion. The findings clarify the roles of extracellular proteins in IDD and highlight their potential as promising novel therapeutic targets. Cite this article: Bone Joint Res 2023;12(9):522–535

Aims. Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results. We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion. This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680

Prediction tools are instruments which are commonly used to estimate the prognosis in oncology and facilitate clinical decision-making in a more personalized manner. Their popularity is shown by the increasing numbers of prediction tools, which have been described in the medical literature. Many of these tools have been shown to be useful in the field of soft-tissue sarcoma of the extremities (eSTS). In this annotation, we aim to provide an overview of the available prediction tools for eSTS, provide an approach for clinicians to evaluate the performance and usefulness of the available tools for their own patients, and discuss their possible applications in the management of patients with an eSTS. Cite this article: Bone Joint J 2022;104-B(9):1011–1016

Initial treatment of traumatic spinal cord injury remains as controversial in 2023 as it was in the early 19th century, when Sir Astley Cooper and Sir Charles Bell debated the merits or otherwise of surgery to relieve cord compression. There has been a lack of high-class evidence for early surgery, despite which expeditious intervention has become the surgical norm. This evidence deficit has been progressively addressed in the last decade and more modern statistical methods have been used to clarify some of the issues, which is demonstrated by the results of the SCI-POEM trial. However, there has never been a properly conducted trial of surgery versus active conservative care. As a result, it is still not known whether early surgery or active physiological management of the unstable injured spinal cord offers the better chance for recovery. Surgeons who care for patients with traumatic spinal cord injuries in the acute setting should be aware of the arguments on all sides of the debate, a summary of which this annotation presents. Cite this article: Bone Joint J 2023;105-B(4):347–355

Evaluation of patient specific spinopelvic mobility requires the detection of bony landmarks in lateral functional radiographs. Current manual landmarking methods are inefficient, and subjective. This study proposes a deep learning model to automate landmark detection and derivation of spinopelvic measurements (SPM). A deep learning model was developed using an international multicenter imaging database of 26,109 landmarked preoperative, and postoperative, lateral functional radiographs (HREC: Bellberry: 2020-08-764-A-2). Three functional positions were analysed: 1) standing, 2) contralateral step-up and 3) flexed seated. Landmarks were manually captured and independently verified by qualified engineers during pre-operative planning with additional assistance of 3D computed tomography derived landmarks. Pelvic tilt (PT), sacral slope (SS), and lumbar lordotic angle (LLA) were derived from the predicted landmark coordinates. Interobserver variability was explored in a pilot study, consisting of 9 qualified engineers, annotating three functional images, while blinded to additional 3D information. The dataset was subdivided into 70:20:10 for training, validation, and testing. The model produced a mean absolute error (MAE), for PT, SS, and LLA of 1.7°±3.1°, 3.4°±3.8°, 4.9°±4.5°, respectively. PT MAE values were dependent on functional position: standing 1.2°±1.3°, step 1.7°±4.0°, and seated 2.4°±3.3°, p< 0.001. The mean model prediction time was 0.7 seconds per image. The interobserver 95% confidence interval (CI) for engineer measured PT, SS and LLA (1.9°, 1.9°, 3.1°, respectively) was comparable to the MAE values generated by the model. The model MAE reported comparable performance to the gold standard when blinded to additional 3D information. LLA prediction produced the lowest SPM accuracy potentially due to error propagation from the SS and L1 landmarks. Reduced PT accuracy in step and seated functional positions may be attributed to an increased occlusion of the pubic-symphysis landmark. Our model shows excellent performance when compared against the current gold standard manual annotation process

Aims. Osteoarthritis (OA) is the most prevalent joint disease. However, the specific and definitive genetic mechanisms of OA are still unclear. Methods. Tissue-related transcriptome-wide association studies (TWAS) of hip OA and knee OA were performed utilizing the genome-wide association study (GWAS) data of hip OA and knee OA (including 2,396 hospital-diagnosed hip OA patients versus 9,593 controls, and 4,462 hospital-diagnosed knee OA patients versus 17,885 controls) and gene expression reference to skeletal muscle and blood. The OA-associated genes identified by TWAS were further compared with the differentially expressed genes detected by the messenger RNA (mRNA) expression profiles of hip OA and knee OA. Functional enrichment and annotation analysis of identified genes was performed by the DAVID and FUMAGWAS tools. Results. We detected 33 common genes, eight common gene ontology (GO) terms, and one common pathway for hip OA, such as calcium and integrin-binding protein 1 (CIB1) (PTWAS = 0.025, FCmRNA = -1.575 for skeletal muscle), adrenomedullin (ADM) (PTWAS = 0.022, FCmRNA = -4.644 for blood), Golgi apparatus (PTWAS <0.001, PmRNA = 0.012 for blood), and phosphatidylinositol 3' -kinase-protein kinase B (PI3K-Akt) signalling pathway (PTWAS = 0.033, PmRNA = 0.005 for blood). For knee OA, we detected 24 common genes, eight common GO terms, and two common pathways, such as histocompatibility complex, class II, DR beta 1 (HLA-DRB1) (PTWAS = 0.040, FCmRNA = 4.062 for skeletal muscle), Follistatin-like 1 (FSTL1) (PTWAS = 0.048, FCmRNA = 3.000 for blood), cytoplasm (PTWAS < 0.001, PmRNA = 0.005 for blood), and complement and coagulation cascades (PTWAS = 0.017, PmRNA = 0.001 for skeletal muscle). Conclusion. We identified a group of OA-associated genes and pathways, providing novel clues for understanding the genetic mechanism of OA. Cite this article:Bone Joint Res. 2020;9(3):130–138

Cell therapies hold significant promise for the treatment of injured or diseased musculoskeletal tissues. However, despite advances in research, there is growing concern about the increasing number of clinical centres around the world that are making unwarranted claims or are performing risky biological procedures. Such providers have been known to recommend, prescribe, or deliver so called ‘stem cell’ preparations without sufficient data to support their true content and efficacy. In this annotation, we outline the current environment of stem cell-based treatments and the strategies of marketing directly to consumers. We also outline the difficulties in the regulation of these clinics and make recommendations for best practice and the identification and reporting of illegitimate providers. Cite this article: Bone Joint J 2020;102-B(2):148–154

Metallic implants are widely used in orthopedic, oral and maxillofacial surgery. Durable osseous fixation of an implant requires that osteoprogenitor cells attach and adhere to the implant, proliferate, differentiate into osteoblasts, and produce mineralized matrix. We previously observed that human mesenchymal stem cells (MSCs) adherent to smooth tantalum (Ta) surfaces demonstrated superior biocompatibility compared with titanium (Ti) coatings. The aim of the present study was to investigate the interactions between MSCs and smooth surfaces of Ta and by means of whole-genome microarray technology. Immortalized human mesenchymal stem cells were cultivated on smooth surfaces of Ti and Ta. Total RNA was extracted after culturing for 1, 2, 4, and 8 days and hybridized to Affymetrix whole-genome microarrays (N=16). Replicate arrays were averaged and the ratios of gene expression by MSCs cultivated on Ta versus Ti coating were calculated. Absolute fold differences were also calculated and lists of upregulated genes were generated. Moreover, gene Ontology (GO) annotation analysis of differentially regulated genes was performed. For both Ta and Ti coatings, the vast majority of genes were upregulated after 4 d of cultivation. Genes upregulated by MSCs cultivated on Ta coating for 4 d were annotated to relevant GO terms. Ti-regulated GO annotation clusters were predominantly transcription-related. By using the K-means clustering algorithm, 10 clusters containing more than 5 genes were identified. Moreover, various genes related to osteogenesis and cell adhesion were upregulated by MSCs exposed to Ta surface. Microarray analysis of MSCs exposed to smooth metallic surfaces of both Ta and Ti generally showed a huge increase in transcriptional activity after 4 d of cultivation. According to GO annotation analysis Ta coating may induce increased adhesion and earlier differentiation of MSCs compared to Ti surface making Ta a promising biocompatible material for bone implants

Aim. Osteoarthritis (OA) is caused by complex interactions between genetic and environmental factors. Epigenetic mechanisms control the expression of genes and are likely to regulate the OA transcriptome. We performed integrative genomic analyses to define methylation-gene expression relationships in osteoarthritic cartilage. Patients and Methods. Genome-wide DNA methylation profiling of articular cartilage from five patients with OA of the knee and five healthy controls was conducted using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, San Diego, California). Other independent genome-wide mRNA expression profiles of articular cartilage from three patients with OA and three healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Integrative pathway enrichment analysis of DNA methylation and mRNA expression profiles was performed using integrated analysis of cross-platform microarray and pathway software. Gene ontology (GO) analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Results. We identified 1265 differentially methylated genes, of which 145 are associated with significant changes in gene expression, such as DLX5, NCOR2 and AXIN2 (all p-values of both DNA methylation and mRNA expression < 0.05). Pathway enrichment analysis identified 26 OA-associated pathways, such as mitogen-activated protein kinase (MAPK) signalling pathway (p = 6.25 × 10-4), phosphatidylinositol (PI) signalling system (p = 4.38 × 10-3), hypoxia-inducible factor 1 (HIF-1) signalling pathway (p = 8.63 × 10-3 pantothenate and coenzyme A (CoA) biosynthesis (p = 0.017), ErbB signalling pathway (p = 0.024), inositol phosphate (IP) metabolism (p = 0.025), and calcium signalling pathway (p = 0.032). Conclusion. We identified a group of genes and biological pathwayswhich were significantly different in both DNA methylation and mRNA expression profiles between patients with OA and controls. These results may provide new clues for clarifying the mechanisms involved in the development of OA. Cite this article: A. He, Y. Ning, Y. Wen, Y. Cai, K. Xu, Y. Cai, J. Han, L. Liu, Y. Du, X. Liang, P. Li, Q. Fan, J. Hao, X. Wang, X. Guo, T. Ma, F. Zhang. Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage. Bone Joint Res 2018;7:343–350. DOI: 10.1302/2046-3758.75.BJR-2017-0284.R1

Trauma surgeries in the pelvic area are often difficult and prolonged processes that require comprehensive preoperative planning based on a CT scan. Preoperative planning is essential for the appreciation and spatial visualisation of the bone fragments, for planning the reduction strategy, and for determining the optimal type, size, and location of the fixation hardware. We have developed a novel haptic-based patient specific preoperative planning system for pelvic bone fractures surgery planning. The system provides a virtual environment in which 3D bone fragments and fixation hardware models are interactively manipulated with full spatial depth and tactile perception. It supports the choice of the surgical approach and the planning of the two mains steps of bone fracture surgery: reduction and fixation. The purpose of the tool is to provide an intuitive haptic spatial interface for the manipulation of bone fracture 3D models extracted from CT images, to support the selection of bone fragments, the annotation of the fracture surface, the selection and placement of fixation screws, and the creation and placement of fixation plates with an anatomically fit shape. The system incorporates ligament models that constrain the bone fragments motions and provides a realistic interactive fracture reduction support feeling to the surgeon. It allows the surgeon to view the fracture from various directions, thereby allowing fast and accurate fracture reduction planning. Two haptic devices, one for each hand, provide tactile feedback when objects touch without interpenetrating. To facilitate the reduction, the system provides an interactive, haptic fracture surface annotation tool and a fracture reduction algorithm that automatically minimises the pairwise distance between the fracture surfaces. For fracture fixation, the system provides a screw creation and placement capability as well as custom anatomical-fit fixation plate creation and placement. The screw placement is facilitated by the transparent viewing mode that allows the surgeon to navigate the screws inside the bone fragments while constraining them to remain within the bone fragments with haptic forces. Our experimental results with five surgeons show that the method allows highly accurate reduction planning to within 1 mm or less. To evaluate the alignment in terms of quantity, we created a model of an artificial fracture in a healthy pelvis bone. The created model is placed in its anatomic location thus allowing us to measure the error in relation to its initial position. We calculate the anatomic alignment error by measuring the Hausdorff distance in mm between the fragment positioned in the desired location and the fragment placed by the surgeon. The new haptic-based system also supports patient-specific training of pelvic fracture surgeries

The present study investigates the repeatability of two new methods of measuring acetabular wear with differing levels of automation. Experimental evaluation showed that the more automated method was more repeatable. Both methods segmented the femoral head and acetabular rim with ellipses. The displacement of the ellipse centres was measured and the difference at year 1 and 5 taken as a measure of wear. Measurements were obtained twice for each case. The less automated of the two methods involved the annotation of 9 points on the femoral head and 18 on the acetabular rim to which two least squares ellipse fits[. 1. ] were performed. The second and more automated method was active ellipses[. 2. ][. 3. ]. This method uses iterative robust ellipse fitting and a model of appearance learned from a training set to cause two ellipses to converge on the contours of the femoral head and acetabular rim from a single starting point. Fifty cases with radiographs taken at year 1 and year 5 were measured by both methods. The radiographs contained CPTs with 28mm heads and were digitized at 150 dpi. Fifty postoperative radiographs containing 22.225mm Zimmer CPT heads trained the more automated method. None of the radiographs had metal backed cups or highly eccentric rims. The repeatability coefficient (2 standard deviations) of the active ellipses was 0.23mm and that of the best annotator was 0.40mm while the worst was 2.69mm due to an outlying measurement. Limits of agreement were calculated between the two methods as −0.61mm to 0.91mm and show the active ellipses could replace annotation. Given that the active ellipses are nearly twice as repeatable this is desirable. The range of difference in measurements for the active ellipses is less than that of the annotator

Aims

Lumbar spinal stenosis (LSS) is a common skeletal system disease that has been partly attributed to genetic variation. However, the correlation between genetic variation and pathological changes in LSS is insufficient, and it is difficult to provide a reference for the early diagnosis and treatment of the disease.