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PurposeThe aim of this work was to investigate the risk factors for cement leakage and new-onset OVCF after Percutaneous vertebroplasty (PVP) and to develop and validate a clinical prediction model (Nomogram).MethodsPatients with Osteoporotic VCF (OVCF) treated with PVP at Liuzhou People’s Hospital from June 2016 to June 2018 were reviewed and met the inclusion criteria. Relevant data affecting bone cement leakage and new onset of OVCF were collected. Predictors were screened using univariate and multi-factor logistic analysis to construct Nomogram and web calculators. The consistency of the prediction models was assessed using calibration plots, and their predictive power was assessed by tenfold cross-validation. Clinical value was assessed using Decision curve analysis (DCA) and clinical impact plots.ResultsHigher BMI was associated with lower bone mineral density (BMD). Higher BMI, lower BMD, multiple vertebral fractures, no previous anti-osteoporosis treatment, and steroid use were independent risk factors for new vertebral fractures. Cement injection volume, time to surgery, and multiple vertebral fractures were risk factors for cement leakage after PVP. The development and validation of the Nomogram also demonstrated the predictive ability and clinical value of the model.ConclusionsThe established Nomogram and web calculator (https://dr-lee.shinyapps.io/RefractureApp/) (https://dr-lee.shinyapps.io/LeakageApp/) can effectively predict the occurrence of cement leakage and new OVCF after PVP.

DeepSeek, an AI-driven platform, revolutionizes epidemic management through early warning systems, trend prediction, treatment optimization, and public consultation. By integrating multi-source real-time data (epidemiological, social media, mobility) with deep learning models (LSTM, Transformers) and explainable decision frameworks,, it enhances public health responsiveness. Challenges include data privacy and model accuracy, but its potential to fortify global health resilience is transformative.

Glioma is the most malignant cancer in the brain. As a major vitamin-K-dependent protein in the central nervous system, PROS1 not only plays a vital role in blood coagulation, and some studies have found that it was associated with tumor immune infiltration. However, the prognostic significance of PROS1 in glioma and the underlying mechanism of PROS1 in shaping the tumor immune microenvironment (TIME) remains unclear. The raw data (including RNA-seq, sgRNA-seq, clinicopathological variables and prognosis, and survival data) were acquired from public databases, including TCGA, GEPIA, CGGA, TIMER, GEO, UALCAN, and CancerSEA. GO enrichment and KEGG pathway analyses were performed using \"cluster profiler\" package and visualized by the \"ggplot2\" package. GSEA was conducted using R package \"cluster profiler\". Tumor immune estimation resource (TIMER) and spearman correlation analysis were applied to evaluate the associations between infiltration levels of immune cells and the expression of PROS1. qRT-PCR and WB were used to assay the expression of PROS1. Wound-healing assay, transwell chambers assays, and CCK-8 assays, were performed to assess migration and proliferation. ROC and KM curves were constructed to determine prognostic significance of PROS1 in glioma. The level of PROS1 expression was significantly increased in glioma in comparison to normal tissue, which was further certificated by qRT-PCR and WB in LN-229 and U-87MG glioma cells. High expression of PROS1 positively correlated with inflammation, EMT, and invasion identified by CancerSEA, which was also proved by downregulation of PROS1 could suppress cells migration, and proliferation in LN-229 and U-87MG glioma cells. GO and KEGG analysis suggested that PROS1 was involved in disease of immune system and T cell antigen receptor pathway. Immune cell infiltration analysis showed that expression of PROS1 was negatively associated with pDC and NK CD56 bright cells while positively correlated with Macrophages, Neutrophils in glioma. Immune and stromal scores analysis indicated that PROS1 was positively associated with immune score. The high level of PROS1 resulted in an immune suppressive TIME the recruitment of immunosuppressive molecules. In addition, Increased expression of PROS1 was correlated with T-cell exhaustion, M2 polarization, poor Overall-Survival (OS) in glioma. And it was significantly related to tumor histological level, age, primary therapy outcome. The results of our experiment and various bioinformatics approaches validated that PROS1 was a valuable poor prognostic marker. Increased expression of PROS1 was correlated with malignant phenotype and associated with poor prognosis in glioma. Besides, PROS1 could be a possible biomarker and potential immunotherapeutic target through promoting the glioma immunosuppressive microenvironment and inducing tumor-associated macrophages M2 polarization.

A new motor-driven lever-based servo actuator is proposed for automatic manipulation of dry clutches. The dynamic model considering the negative stiffness of the diaphragm spring is built. The characteristics of torque-saving and power-saving are analyzed. Moreover, the stability addressing the negative stiffness is analyzed. The simulation results are verified by the experimental results. The results from quasi-steady experiments show the proposed actuator reduces the motor torque up to 23.9%, the motor power loss up to 38.7%, and the motor output power up to 21.9% compared to the ordinary lever-based actuator. As a whole, the energy consumption during a usual clutch engagement is reduced by 30.1%. In addition, the proposed actuator can track the desired displacement well under the negative stiffness case, and thus overcomes the weakness of the “Luk-like” actuator.

Bayberry detection plays a crucial role in yield prediction. However, bayberry targets in complex orchard environments present significant detection challenges, including small volume, severe occlusion, and dense distribution, making traditional methods inadequate for practical applications. This study proposes a Multi-Domain Enhanced DETR (MDE-DETR) detection algorithm based on multi-domain enhanced feature fusion. First, an Enhanced Feature Extraction Network (EFENet) backbone is constructed, which incorporates Multi-Path Feature Enhancement Module (MFEM) and reparameterized convolution techniques to enhance feature perception capabilities while reducing model parameters. Second, a Multi-Domain Feature Fusion Network (MDFFN) architecture is designed, integrating SPDConv spatial pixel rearrangement, Cross-Stage Multi-Kernel Block (CMKBlock), and dual-domain attention mechanisms to achieve multi-scale feature fusion and improve small target detection performance. Third, an Adaptive Deformable Sampling (ADSample) downsampling module is constructed, which dynamically adjusts sampling positions through learnable spatial offset prediction to enhance model robustness for occluded and dense targets. Experimental results demonstrate that on a self-constructed bayberry dataset, MDE-DETR achieves improvements of 3.8% and 5.1% in mAP50 and mAP50:95 respectively compared to the RT-DETR baseline model, reaching detection accuracies of 92.9% and 67.9%, while reducing parameters and memory usage by 25.76% and 25.14% respectively. Generalization experiments on VisDrone2019 (a small-target dataset) and TomatoPlantfactoryDataset (a dense occlusion dataset) datasets further validate the algorithm's effectiveness, providing an efficient and lightweight solution for small-target bayberry detection in complex environments.

Background Established prediction models of Diabetic kidney disease (DKD) are limited to the analysis of clinical research data or general population data and do not consider hospital visits. Construct a 3-year diabetic kidney disease risk prediction model in patients with type 2 diabetes mellitus (T2DM) using machine learning, based on electronic medical records (EMR). Methods Data from 816 patients (585 males) with T2DM and 3 years of follow-up at the PLA General Hospital. 46 medical characteristics that are readily available from EMR were used to develop prediction models based on seven machine learning algorithms (light gradient boosting machine [LightGBM], eXtreme gradient boosting, adaptive boosting, artificial neural network, decision tree, support vector machine, logistic regression). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was used to interpret the results of the best performing model. Results The LightGBM model had the highest AUC (0.815, 95% CI 0.747–0.882). Recursive feature elimination with random forest and SHAP plot based on LightGBM showed that older patients with T2DM with high homocysteine (Hcy), poor glycemic control, low serum albumin (ALB), low estimated glomerular filtration rate (eGFR), and high bicarbonate had an increased risk of developing DKD over the next 3 years. Conclusions This study constructed a 3-year DKD risk prediction model in patients with T2DM and normo-albuminuria using machine learning and EMR. The LightGBM model is a tool with potential to facilitate population management strategies for T2DM care in the EMR era.

Background Population aging in China has intensified psychological challenges for family caregivers. This study investigates the relationships among social support, caregiving experiences, and depressed mood in family caregivers of home-dwelling older adults with disabilities, aiming to provide theoretical guidance for enhancing caregivers’ physical and mental well-being and optimizing home-based care services. Methods A random sample of 494 family caregivers from three districts in a specified city was surveyed. Data included demographics of family caregivers and care recipients, scores from the Social Support Rating Scale (SSRS), Zarit Burden Interview Scale (ZBI), Positive Aspects of Caregiving Scale (PAC), and Center for Epidemiologic Studies Depression Scale (CES-D 10). Statistical analyses comprised t-tests, ANOVA, Pearson’s correlation, and stratified linear regression. Results Mean scores for social support, positive caregiving experience, caregiving burden, and depressed mood were 28.62 ± 5.82, 32.01 ± 6.13, 34.59 ± 18.41, and 8.22 ± 5.96, respectively. Depressed mood scores varied significantly with family caregivers’ education, self-rated health, and care recipients’ disability severity ( P  < 0.01). Social support was negatively correlated with both caregiving burden and depressed mood ( P  < 0.01), and positively correlated with positive caregiving experience ( P  < 0.01). Positive caregiving experience was negatively associated with caregiving burden and depressed mood ( P  < 0.01), whereas caregiving burden was positively correlated with depressed mood ( P  < 0.01). Regression analysis indicated that social support and positive experience negatively predicted depressed mood (path coefficients: -0.11 and − 0.37, P  < 0.01), while caregiving burden had a significant positive effect (path coefficient: 0.35, P  < 0.01). Conclusion Social support exerts a direct negative effect on caregivers’ depressed mood, with caregiving experience playing a chain mediating role in this relationship. Strengthening the social support and enhancing the positive caregiving experience may reduce depressed mood. Governments and communities should strengen systemic support by establishing caregiver networks, expanding skill-building programs, and integrating mental health resources. These measures could empower family caregivers to manage responsibilities effectively, improving well-being for both family caregivers and care recipients.

State of charge (SOC) is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a challenge to the accurate estimation of SOC in real applications. This paper adds two contributions to the existing literature. First, the auto regressive exogenous (ARX) model is proposed here to simulate the battery nonlinear dynamics. Due to its discrete form and ease of implemention, this straightforward approach could be more suitable for real applications. Second, its order selection principle and parameter identification method is illustrated in detail in this paper. The hybrid pulse power characterization (HPPC) cycles are implemented on the 60AH LiFePO4 battery module for the model identification and validation. Based on the proposed ARX model, SOC estimation is pursued using the extended Kalman filter. Evaluation of the adaptability of the battery models and robustness of the SOC estimation algorithm are also verified. The results indicate that the SOC estimation method using the Kalman filter based on the ARX model shows great performance. It increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs.

Autoimmune diseases refer to a group of conditions where the immune system produces an immune response against self-antigens, resulting in tissue damage. These diseases have profound impacts on the health of patients. In recent years, with the rapid development in the field of biomedicine, engineered exosomes have emerged as a noteworthy class of biogenic nanoparticles. By precisely manipulating the cargo and surface markers of exosomes, engineered exosomes have gained enhanced anti-inflammatory, immunomodulatory, and tissue reparative abilities, providing new prospects for the treatment of autoimmune diseases. Engineered exosomes not only facilitate the efficient delivery of bioactive molecules including nucleic acids, proteins, and cytokines, but also possess the capability to modulate immune cell functions, suppress inflammation, and restore immune homeostasis. This review mainly focuses on the applications of engineered exosomes in several typical autoimmune diseases. Additionally, this article comprehensively summarizes the current approaches for modification and engineering of exosomes and outlines their prospects in clinical applications. In conclusion, engineered exosomes, as an innovative therapeutic approach, hold promise for the management of autoimmune diseases. However, while significant progress has been made, further rigorous research is still needed to address the challenges that engineered exosomes may encounter in the therapeutic intervention process, in order to facilitate their successful translation into clinical practice and ultimately benefit a broader population of patients.

The number of rice leaves largely reflects the growth stage and health status of rice. However, the current rice leaf counting method is time-consuming and laborious, with low accuracy and poor efficiency, which is difficult to meet the needs of rice growth monitoring. This study proposes a field rice leaf detection method based on an improved YOLOv5s model. First, we added a high-resolution layer and removed the original low-resolution detection layer, using the K-Means++ clustering algorithm to reset the anchor box sizes, enhancing the model's ability to identify small leaf tip targets while reducing the number of parameters. Second, we introduced a coordinate attention mechanism (CA) to strengthen focus on leaf tip features under weed interference and leaf occlusion conditions. Finally, we employed a content-aware reassembly of feature (CARAFE) upsampling operator to enhance the detail reconstruction capability of leaf tip features. Experimental results showed that the improved rice leaf tip detection model achieved precision, recall, and mean average precision rates of 93.7%, 87%, and 93.5%, respectively, with a parameter count of 5.02 million (M), improving by 6.5%, 22.1%, and 18.5% compared to the YOLOv5s baseline model, while reducing the parameter count by 28.4%. The improved model effectively reduced the missed detection rate of rice leaves and enhanced the accuracy and robustness of field rice leaf tip detection, providing strong technical support for rice phenotype feature extraction and growth monitoring.