Bone defect repair is one of the most common issues in clinic. Developmental multifunctional scaffolds have become a promising strategy to effectively promote bone defect repair. Here, a series of multifunctional hydrogels that integrate stable mechanical properties, non-swelling property, conductivity, and photothermal antibacterial properties were developed based on gelatin methacrylate (GM), acryloyl-β-cyclodextrin (Ac-CD), and β-cyclodextrin (β-CD)-functionalized reduced graphene oxide (rGO) for skull defect regeneration. Ac-CD was added as a host macromolecule to improve the toughness of the hydrogels. rGO was selected as the conductive element to endow the hydrogel with conductive properties, and the β-CD unit in rGO allowed rGO to interact with GM to improve the dispersity of rGO. In vitro/in vivo studies confirmed that the GM/Ac-CD/rGO hydrogel had good biocompatibility and simultaneously promoted the proliferation and osteogenic differentiation of MC3T3-E1 cells, and further accelerated in vivo bone defect repair in a rat skull defect model. Moreover, two-photon laser scanning microscopy (TPLSM) was used for the first time to evaluate bone defect repair by exploring the collagen and mineralized structure directly in bone defect specimens. In short, these multifunctional hydrogels have shown promising applications in bone tissue formation and further accelerate bone defect repair, indicating their great potential for clinical application.

Background: Muscle strength has been shown to exert positive effects on bone health. The causal relationship between hand grip strength and osteoporosis is an important public health issue but is not fully revealed. The goal of this study was to investigate whether and to what extent hand grip strength affects bone mineral density (BMD) and fracture risk.Methods: We conducted a state-of-the-art two-sample Mendelian randomization analysis. Genomewide significant (P<5×10^-8) single nucleotide polymorphisms associated with hand grip strength were obtained. Summary level data of BMD and fractures at different body sites (lumbar spine, heel, forearm and femoral neck) was obtained from a large-scale osteoporosis database. The inverse variance weighted method was the primary method used for analysis, and the weighted-median, MR-Egger were utilized for sensitivity analyses.Results: The results provided strong evidence that hand grip strength trait was causally and positively associated with lumbar spine BMD (β: 0.288, 95% CI: 0.079 to 0.497; P=0.007), while no causal relationship was found between hand grip strength and BMD at heel (β: -0.081, 95% CI: -0.232 to 0.070; P=0.295), forearm (β: 0.-0.101, 95% CI: -0.451 to 0.248; P=0.571) or femoral neck (β: 0.054, 95% CI: -0.171 to 0.278; P=0.639). In addition, no statistically significant effects were observed for hand grip strength on fracture risks (β: -0.004, 95% CI: -0.019 to 0.012; P=0.662).Conclusions: This study showed a positive causal relationship between hand grip strength and lumbar BMD, which is the most common site of osteoporotic fracture, but did not find a causal relationship between hand grip strength and BMD of heel, forearm, or femoral neck. No statistically significant effect of hand grip strength on fracture risk was observed. This study indicates variations in the abilities of hand grip strength trait to causally influence BMD at different skeleton sites. These results should be considered in further studies and public health measures on osteoporosis prevention strategies.

This study aimed to evaluate the epidemiology of osteoarthritis in China in a comprehensive and reliable way, to project its future epidemiological patterns, and to mitigate its health hazards. Data were extracted and analyzed from the Global Burden of Diseases Study 2019. Trends in osteoarthritis epidemiology were explored using joinpoint regression analysis. Additionally, we analyzed dynamic trends using the sociodemographic index (SDI) of China. To assess and predict the epidemiology of osteoarthritis from 2020 to 2039, we used both the Bayesian age-period-cohort model and Nordpred model. The number of prevalent cases, incident cases, and years lived with disability (YLDs) for osteoarthritis in China increased from 51.8, 4.6, and 1.8 million, respectively, in 1990, to 132.8, 10.7, and 4.7 million, respectively, in 2019, and the average annual percentage changes were 3.286, 2.938, and 3.324, respectively. The prevalence and YLDs peaked in the population aged over 90 years old, while the incidence peaked in the population aged around 50 years old. A significant positive correlation was found between osteoarthritis burden and SDI. Osteoarthritis burden is expected to continue to increase. In the population studied here, it was higher in women than in men, but this may invert by 2039. The prevalence, incidence, and YLDs of osteoarthritis had significantly increased and may continue to increase during the next 2 decades. Prevention and treatment strategies should target women, middle-aged individuals, and the elderly.

Purpose: The objective of this study was to create and assess a Deep Learning-Based Radiomics model using a single sequence MRI that could accurately predict early Femoral Head Osteonecrosis (ONFH). This is the first time such a model was used for the diagnosis of early ONFH. Its simpler than the previously published multi-sequence MRI radiomics based method, and it implements Deep learning to improve on radiomics. It has the potential to be highly beneficial in the early stages of diagnosis and treatment planning.Methods: MRI scans from 150 patients in total (80 healthy, 70 necrotic) were used, and split into training and testing sets in a 7:3 ratio. Handcrafted as well as deep learning features were retrieved from Tesla 2 weighted (T2W1) MRI slices. After a rigorous selection process, these features were used to construct three models: a Radiomics-based (Rad-model), a Deep Learning-based (DL-model), and a Deep Learning-based Radiomics (DLR-model). The performance of these models in predicting early ONFH was evaluated by comparing them using the receiver operating characteristic (ROC) and decision curve analysis (DCA).Results: 1,197 handcrafted radiomics and 512 DL features were extracted then processed; after the final selection: 15 features were used for the Rad-model, 12 features for the DL-model, and only 9 features were selected for the DLR-model. The most effective algorithm that was used in all of the models was Logistic regression (LR). The Rad-model depicted good results outperforming the DL-model; AUC = 0.944 (95%CI, 0.862–1.000) and AUC = 0.930 (95%CI, 0.838–1.000) respectively. The DLR-model showed superior results to both Rad-model and the DL-model; AUC = 0.968 (95%CI, 0.909–1.000); and a sensitivity of 0.95 and specificity of 0.920. The DCA showed that DLR had a greater net clinical benefit in detecting early ONFH.Conclusion: Using a single sequence MRI scan, our work constructed and verified a Deep Learning-Based Radiomics Model for early ONFH diagnosis. This strategy outperformed a Deep learning technique based on Resnet18 and a model based on Radiomics. This straightforward method can offer essential diagnostic data promptly and enhance early therapy strategizing for individuals with ONFH, all while utilizing just one MRI sequence and a more standardized and objective interpretation of MRI images.

Purpose: Distinguishing between Osteonecrosis of the femoral head (ONFH) and Osteoarthritis (OA) can be subjective and vary between users with different backgrounds and expertise. This study aimed to construct and evaluate several Radiomics-based machine learning models using MRI to differentiate between those two disorders and compare their efficacies to those of medical experts.Methods: 140 MRI scans were retrospectively collected from the electronic medical records. They were split into training and testing sets in a 7:3 ratio. Handcrafted radiomics features were harvested following the careful manual segmentation of the regions of interest (ROI). After thoroughly selecting these features, various machine learning models have been constructed. The evaluation was carried out using receiver operating characteristic (ROC) curves. Then NaiveBayes (NB) was selected to establish our final Radiomics-model as it performed the best. Three users with different expertise and backgrounds diagnosed and labeled the dataset into either OA or ONFH. Their results have been compared to our Radiomics-model.Results: The amount of handcrafted radiomics features was 1197 before processing; after the final selection, only 12 key features were retained and used. User 1 had an AUC of 0.632 (95% CI 0.4801-0.7843), User 2 recorded an AUC of 0.565 (95% CI 0.4102-0.7196); while User 3 was on top with an AUC of 0.880 (95% CI 0.7753-0.9843). On the other hand, the Radiomics model attained an AUC of 0.971 (95% CI 0.9298-1.0000); showing greater efficacy than all other users. It also demonstrated a sensitivity of 0.937 and a specificity of 0.885. DCA (Decision Curve Analysis displayed that the radiomics-model had a greater clinical benefit in differentiating OA and ONFH.Conclusion: We have successfully constructed and evaluated an interpretable radiomics-based machine learning model that could distinguish between OA and ONFH. This method has the ability to aid both junior and senior medical professionals to precisely diagnose and take prompt treatment measures.