The growth plate is crucial for skeletal growth in children, but research on repairing growth plate damage and restoring growth is limited. Here, a high-toughness adaptive dual-crosslinked hydrogel is designed to mimic the growth plate’s structure, supporting regeneration and bone growth. Composed of aldehyde-modified bacterial cellulose (DBNC), methacrylated gelatin (GelMA) and sodium alginate (Alg), the hydrogel is engineered through ionic bonding and Schiff base reactions, creating a macroporous structure. This structure can transform into a denser form by binding with calcium ions. In vitro, the loose macroporous structure of the hydrogels can promote chondrogenic differentiation, and when it forms a dense structure by binding with calcium ions, it also can activate relevant chondrogenic signaling pathways under the influence of insulin-like growth factor I (IGF-1), further inhibiting osteogenesis. In vivo experiments in a rat model of growth plate injury demonstrated that the hydrogel promoted growth plate cartilage regeneration and minimized bone bridge formation by creating a hypoxic microenvironment that activates IGF-1-related pathways. This environment encourages chondrogenic differentiation while preventing the undesired formation of bone tissue within the growth plate area. Overall, the dual-crosslinked hydrogel not only mimics the growth plate’s structure but also facilitates localized IGF-1 expression, effectively reshaping the growth plate’s function. This approach represents a promising therapeutic strategy for treating growth plate injuries, potentially addressing challenges associated with skeletal growth restoration in pediatric patients.

Background: Scoliosis is a spinal deformity in which one or more spinal segments bend to the side or show vertebral rotation. Some artificial intelligence (AI) apps have already been developed for measuring the Cobb angle in patients with scoliosis. These apps still require doctors to perform certain measurements, which can lead to interobserver variability. The AI app (cobbAngle pro) in this study will eliminate the need for doctor measurements, achieving complete automation. Objective: We aimed to evaluate the reliability and accuracy of our new AI app that is based on deep learning to automatically measure the Cobb angle in patients with scoliosis. Methods: A retrospective analysis was conducted on the clinical data of children with scoliosis who were treated at the Pediatric Orthopedics Department of the Children’s Hospital affiliated with Fudan University from July 2019 to July 2022. Three measurers used the Picture Archiving and Communication System (PACS) to measure the coronal main curve Cobb angle in 802 full-length anteroposterior and lateral spine X-rays of 601 children with scoliosis, and recorded the results of each measurement. After an interval of 2 weeks, the mobile AI app was used to remeasure the Cobb angle once. The Cobb angle measurements from the PACS were used as the reference standard, and the accuracy of the Cobb angle measurements by the app was analyzed through the Bland-Altman test. The intraclass correlation coefficient (ICC) was used to compare the repeatability within measurers and the consistency between measurers. Results: Among 601 children with scoliosis, 89 were male and 512 were female (age range: 10-17 years), and 802 full-length spinal X-rays were analyzed. Two functionalities of the app (photography and photo upload) were compared with the PACS for measuring the Cobb angle. The consistency was found to be excellent. The average absolute errors of the Cobb angle measured by the photography and upload methods were 2.00 and 2.08, respectively. Using a clinical allowance maximum error of 5°, the 95% limits of agreement (LoAs) for Cobb angle measurements by the photography and upload methods were –4.7° to 4.9° and –4.9° to 4.9°, respectively. For the photography and upload methods, the 95% LoAs for measuring Cobb angles were –4.3° to 4.6° and –4.4° to 4.7°, respectively, in mild scoliosis patients; –4.9° to 5.2° and –5.1° to 5.1°, respectively, in moderate scoliosis patients; and –5.2° to 5.0° and –6.0° to 4.8°, respectively, in severe scoliosis patients. The Cobb angle measured by the 3 observers twice before and after using the photography method had good repeatability (P<.001). The consistency between the observers was excellent (P<.001). Conclusions: The new AI platform is accurate and repeatable in the automatic measurement of the Cobb angle of the main curvature in patients with scoliosis.

Background: The treatment of congenital pseudarthrosis of the tibia (CPT) remains a challenge because of the difficulties of achieving and maintaining bone union, as well as complications of joint deformity and limb-length discrepancy. The purpose of this study was to evaluate the efficacy of cross-union of the tibia and fibula in achieving union and preventing refracture for patients with refractory CPT as a complementary approach to improve upon conventional surgical treatments. Methods: A retrospective study including patients with refractory CPT who attended our department between June 2014 and August 2020. Eighteen CPT patients, who had sustained refracture that required cast immobilization or secondary surgery, and were managed by pseudarthrosis resection, cross-union of the tibia and fibula, bone morphogenetic protein-2 and autogenous iliac bone grafting, were included. Clinical outcomes of the bone union rate and the frequency of refracture after performing cross-union of the tibia and fibula were assessed during the follow-up period. Results: The mean follow-up period was 4.3 years (range: 1.5 to 6.25 y). The mean age of the patients at surgery was 5.4 years (range: 2.6 to 10 y), and all 18 (100%) of the 18 patients had final healing at the site of pseudarthrosis. The average time spent to achieve radiologic bone union of the pseudarthrosis after operation was 2.96 months (range: 2.2 to 4.1 mo). Two (11.1%) patients had an average 2.5 cm limb-length discrepancy, none (0%) sustained refracture which needed cast immobilization or secondary surgery. Patients were all pain-free and move actively. Conclusions: Cross-union of the tibia and fibula is a promising complementary procedure for treating refractory CPT patients. Level of Evidence: Level IV—case series.

Most 3D spine reconstruction methods require X-ray images as input, which usually leads to high cost and radiation damage. Therefore, these methods are hardly ever applied to large scale scoliosis screening or spine pose monitoring during treatment. We propose a novel, low-cost, easy-to-operate and none-radioactive 3D spine model reconstruction method, which is based on human back surface information without requiring X-ray images as input. Our method fits a pre-built Spine Priors Model (SPrM) to human back surface information and reconstructs the main part of spine with 17 vertebrae: lumbar vertebrae L1–L5 and thoracic vertebrae T1–T12. The Spine Priors Model is constructed according to human spine priors, including Statistical Spine Shape Model (SSSM), Spine Pose Model (SPM) and Spine Biomechanical Simplified Model (SBSM). The spine-related information on back surface, including back surface spinous curve and local symmetry nearby spinous curve is extracted from the RGBD images of human back surface. We formulate the spine optimization constraints from spine-related feature on back surface and spine priors, then optimize the spine model by gradient descent to get the optimal personalized shape parameters and pose parameters of the Spine Priors Model (SPrM). We assess our reconstruction by scoliosis Cobb angle error, and the result is comparable to current X-ray based methods.

Summary Osteogenesis imperfecta (OI) is a genetic disease with an estimated prevalence of 1 in 13,500 and 1 in 9700. The classification into subtypes of OI is important for prognosis and management. In this study, we established a clinical severity prediction model depending on multiple features of variants in COL1A1/2 genes. Introduction Ninety percent of OI cases are caused by pathogenic variants in the COL1A1/COL1A2 gene. The Sillence classification describes four OI types with variable clinical features ranging from mild symptoms to lethal and progressively deforming symptoms. Methods We established a prediction model of the clinical severity of OI based on the random forest model with a training set obtained from the Human Gene Mutation Database, including 790 records of the COL1A1/COL1A2 genes. The features used in the prediction model were respectively based on variant-type features only, and the optimized features. Results With the training set, the prediction results showed that the area under the receiver operating characteristic curve (AUC) for predicting lethal to severe OI or mild/moderate OI was 0.767 and 0.902, respectively, when using variant-type features only and optimized features for COL1A1 defects, 0.545 and 0.731, respectively, for COL1A2 defects. For the 17 patients from our hospital, prediction accuracy for the patient with the COL1A1 and COL1A2 defects was 76.5% (95% CI: 50.1–93.2%) and 88.2% (95% CI: 63.6–98.5%), respectively. Conclusion We established an OI severity prediction model depending on multiple features of the specific variants in COL1A1/2 genes, with a prediction accuracy of 76–88%. This prediction algorithm is a promising alternative that could prove to be valuable in clinical practice.