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Gene implicated in the heart defects associated with Down’s syndrome
Network analysis of the growth process of novice psychological counselors
A study on the value of ultrasound strain elastography-based radiomics nomogram in the differential diagnosis of breast masses
Abstract The aim of this study was to construct a radiomics nomogram for prediction of breast masses (BMs) by analyzing the clinical characteristics of the patients as well as radiomics features of two-dimensional (2D) ultrasound images and strain elastography images. In this retrospective study, 219 patients diagnosed with BMs were enrolled and randomly divided into training set and testing set in a 7:3 ratio. Radiomics nomogram was constructed based on clinical features and Radscore to compare area under the receiver operating characteristic curve (AUC) with another models. The AUCs of the training set were 0.83, 0.91, 0.92, 0.96, and 0.99 for the clinical model, elastography radiomics model, 2D radiomics model, bimodal radiomics model, and nomogram, respectively, and the AUCs of the testing set were 0.86, 0.87, 0.91, 0.93, and 0.95, respectively. There were significant differences in AUC between nomogram and another models ( p < 0.05). 2D ultrasound radiomics model and strain elastography radiomics model were of diagnostic value in identifying BMs. The bimodal radiomics model was superior to these two single-modal radiomics models. Nomogram can further enhance the diagnosis of BMs and contribute valuable information for clinical decision making.
Effects of banana blossom supplementation on metabolic parameters in prediabetic adults: a double-blind randomized clinical trial
Enhancing the LEACH protocol and lightweight chaotic cryptography for secure data transmission in wireless sensor networks
Why does calorie information produce mixed evidence for its effect on food choices?
VISGAB: Virtual staining-driven GAN benchmarking for optimizing skin tissue histology
Lower limb motor effects of DBS neurofeedback in Parkinson’s disease assessed through IMU-based UPDRS movement quality metrics
Abstract Parkinson’s disease (PD) is characterized by progressive motor impairments, including lower limb dysfunction, leading to reduced mobility and increased fall risk. To counteract these deficits, neurofeedback based on deep brain stimulation (DBS) electrodes has been proposed as a novel approach to mitigate motor symptoms via modulation of abnormal beta-oscillations in the subthalamic nucleus. However, its potential to improve motor symptoms has yet to be fully established. This study examined whether a single session of DBS-based neurofeedback could have a short term effect on movement quality, quantified through inertial measurement unit recordings. Ten PD patients performed two standardized motor tasks, foot stomping and hand pronation-supination, from the Unified Parkinson’s Disease Rating Scale. Movement quality metrics from inertial measurement units were extracted and compared before and after neurofeedback-induced beta-power downregulation. Beta-power was successfully reduced by -12.42% on average, and the reduction was associated with significant improvements in lower limb movement quality metrics—acceleration magnitude (p = 0.037), movement speed (steps per second: p = 0.010; mean peak velocity: p = 0.002), and reduced halts (p = 0.020)—with a strong coupling between beta reduction and speed gain (Spearman $$\rho$$ = 0.976, p < 0.001). No significant improvements were observed in upper limb movements. These findings indicate that neurofeedback-driven downregulation of beta-power produces measurable enhancements in lower limb movement quality, captured through wearable sensor metrics. Future work should assess whether these improvements translate into lasting functional benefits and validate the clinical relevance of these metrics.
Ferroptosis mediated by ferritinophagy is involved in liver injury caused by sepsis
Correction: The effect of protic ionic liquid in the reduction of air contaminant gases emitted from electrical discharge machining in the presence of magnetic field
AI-Savvy leadership for enhancing AI utilization and employee engagement among digital natives in the EdTech sector
Disulfide-rich polysulfur alkaloids target vascular endothelial growth factor A through electronic duality and shape-responsive recognition
High-throughput in vitro screening and in silico analysis for Zika virus inhibitor identification
Modifier guided resilient CNN inference enables fault-tolerant edge collaboration for IoT
Latent profiles of falls self-awareness and associated factors in older maintenance hemodialysis patients
Influence of plant spacing and deblossoming on agromorphological and physiological traits of zombi pea
Multi-omics and experimental validation identify methylation-related genes and METTL16 as key regulators in diabetic foot ulcer pathogenesis
Automated detection of cylindrical structures in complex pipelines using iterative point cloud segmentation and high-precision fitting
Abstract Cylinders are prevalent structural units in pipelines, and their accurate and robust detection from 3D scanned point clouds is crucial for rapid reverse engineering. Existing methods often impose restrictions on cylinder parameters, limiting their applicability in complex pipeline scenarios. To address this, we propose a novel method for automatically detecting cylinders from unstructured point clouds. Our approach involves iterative clustering segmentation to reduce data complexity, reliable candidate cylinder estimation using three-point random sampling, high-precision cylinder fitting, and multi-filtering mechanisms to minimize false detections. Experimental results on both simulated and real-world data demonstrate that our method achieves precision, recall, and F1 scores of 0.8727, 0.8090, and 0.8397, respectively, outperforming existing methods. This work showcases the potential of our approach for automating the reverse engineering design of complex pipelines. Project Web: https://github.com/GCCao/Cylinders_detection_Cao_V2 .