Background: The incidence of osteoporotic fractures has increased rapidly, and because of the poor prognosis and high mortality associated with osteoporotic fractures, they remain a prospective research area globally. One way to reduce their incidence is to investigate their intervention risk factors in the elderly. Hence, this study explores the correlation between serum 25-hydroxyvitamin D [25(OH)D] levels and osteoporotic fractures in elderly patients through a meta-analysis. Methods: We conducted our literature search mainly in PubMed and Embase for identifying studies that investigated the relationship between serum 25(OH)D levels and the risk for osteoporotic fractures. We performed categorical analysis, heterogeneity checks, publication bias analysis, and subgroup analyses. Results: In total, 20 studies were included, of which 4 were case-cohort studies and 16 were cohort studies. A total of 41,738 patients from 20 studies were included in the meta-analysis, of which 5916 had fractures, including 3237 hip fractures. By combining the lowest and highest categories of relative risks (RRs) and 95% confidence intervals (CIs), it was suggested that lower serum 25-hydroxyvitamin D levels may be a risk factor for fractures. RR (95% CI) for total and hip fractures were 1.11 (0.99, 1.24) and 0.89 (0.80, 0.98) after adjustments. Conclusions: Our study showed that compared to low serum 25(OH)D levels, high serum 25(OH)D levels reduce the risk of hip fractures in the patients aged 60 years or older. In contrast, serum 25(OH)D has no significant relationship with total fracture risk.

In this study, a convection nowcasting method based on machine learning was proposed. First, the historical data were back-calculated using the pyramid optical flow method. Next, the generated optical flow field information of each pixel and the Red-Green-Blue (RGB) image information were input into the Convolutional Long Short-Term Memory (ConvLSTM) algorithm for training purposes. During the extrapolation process, dynamic characteristics such as the rotation, convergence, and divergence in the optical flow field were also used as predictors to form an optimal nowcasting model. The test analysis demonstrated that the algorithm combined the image feature extraction ability of the convolutional neural network (CNN) and the sequential learning ability of the long short-term memory network (LSTM) model to establish an end-to-end deep learning network, which could deeply extract high-order features of radar echoes such as structural texture, spatial correlation, and temporal evolution compared with the traditional algorithm. Based on learning through the above features, this algorithm can forecast the generation and dissipation trends of convective cells to some extent. The addition of the optical flow information can more accurately simulate nonlinear trends such as the rotation, or merging, or separation of radar echoes. The trajectories of radar echoes obtained through nowcasting are closer to their actual movements, which prolongs the valid forecasting period and improves forecast accuracy.

Objective: New vertebral compression fracture (NVCF) occurring after bone cement injection in middle-aged and elderly patients with vertebral compression fracture is very common. Preoperative baseline characteristics and surgical treatment parameters have been widely studied as a risk factor, but the importance of the patients' laboratory indicators has not been thoroughly explored. We aimed to explore the relationship between laboratory indicators and NVCF, and attempt to construct a clinical prediction model of NVCF together with other risk factors.Methods: Retrospective analysis was performed for 200 patients who underwent bone cement injection (percutaneous kyphoplasty or vertebroplasty) for vertebral compression fractures between January 2019 and January 2020. We consulted the relevant literature and collated the factors affecting the occurrence of NVCF. Feature selection of patients with NVCF was optimized using the least absolute shrinkage and selection operator regression model, which was used to conduct multivariable logistic regression analysis, to create a predictive model incorporating the selected features. The discrimination, calibration, and clinical feasibility of the predictive model were assessed using the concordance index (C-index), calibration plots, and decision curve analysis. Internal validation was performed using Bootstrap resampling verification.Results: Time from injury to surgery exceeding 7 days, low osteocalcin levels, elevated homocysteine levels, osteoporosis, mode of operation (percutaneous vertebroplasty), lack of postoperative anti-osteoporosis treatment, and poor diffusion of bone cement were independent risk factors for NVCF in middle-aged and elderly patients with vertebral compression fracture after bone cement injection. The C-index of the nomogram constructed using these seven factors was 0.895, indicating good discriminatory ability. The calibration plot showed that the model was well calibrated. Bootstrap resampling verification yielded a significant C-index of 0.866. Decision curve analysis demonstrated that the greatest clinical net benefit for predicting NVCF after bone cement injection could be achieved with a threshold of 1%-91%. Conclusion:This nomogram can effectively predict NVCF incidence after bone cement injection in middle-aged and elderly patients with vertebral compression fracture, thus aiding clinical decision-making and postoperative management, promoting effective postoperative rehabilitation, and improving the quality of life.

Background Osteosarcoma (OS) is a common type of bone malignancy that often occurs in children and adolescents. Chemoresistance is a huge barrier to cancer therapy. This study aimed to investigate the role and potential mechanism of circ_0001721 in doxorubicin (DXR) resistance and OS development. Methods The levels of circ_0001721, miR-758 and transcription factor 4 (TCF4) were detected by quantitative real-time polymerase chain reaction or western blot assay. Cell Counting Kit-8 (CCK-8) assay was used to calculate the half inhibition concentration (IC50) of DXR and assess cell viability. Cell migration and invasion were evaluated by transwell assay. Cell apoptosis was monitored by flow cytometry. The levels of multidrug resistance-related and Wnt/β-catenin pathway-related proteins were measured by western blot assay. The interaction among circ_0001721, miR-758 and TCF4 were confirmed by dual-luciferase reporter assay, RNA immunoprecipitation assay or RNA pull-down assay. The xenograft model was established to analyze tumor growth in vivo. Results Circ_0001721 and TCF4 were upregulated, whereas miR-758 was down-regulated in DXR-resistant OS tissues and cells. Circ_0001721 silence reduced DXR resistance of KHOS/DXR and MG63/DXR cells. Circ_0001721 regulated DXR resistance via sponging miR-758. Moreover, miR-758 modulated DXR resistance by targeting TCF4. Besides, circ_0001721 knockdown inhibited tumor growth in vivo. Conclusion Circ_0001721 potentiated DXR resistance and facilitated the progression of OS by regulating miR-758/TCF4 axis, which provides promising therapeutic targets for OS treatment.