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Evaluating the impact of contrast agents on micro and nano mechanics of soft-to-hard tissue interface
Abstract Transmission of strain across the tendon-bone interface otherwise known as the enthesis, is crucial to the movement of the skeleton. Imaging the inner structure and understanding the way that strain is transmitted across this interface is crucial to understanding the way it responds to load, how it becomes injured through trauma and how intervention and materials can be used to repair the enthesis after injury. Micro-CT imaging and digital volume correlation (DVC) have been widely used for musculoskeletal biomechanics analysis. However, there are limitations for soft tissue visualization. Contrast agents (CA) are used to address this, but understanding their potential effects is essential to ensure accurate and reliable characterization of musculoskeletal tissues biomechanics. In this research, four different contrast-enhanced staining solutions (CESS) including Iodine (I2) in Dulbecco’s modified eagle medium (DMEM), Phosphotungstic acid (PTA) in deionized water, PTA in ethanol, and Mercury II Chloride (HgCl2) in deionized water were used to visualize the tendon-to-bone interface using a combination of high resolution in-situ micro-computed tomography (micro-CT) imaging. The imaging was combined with DVC, nanoindentation, and quantitative 3D structural analysis to evaluate the effects of the CESS on the mechanical properties of the enthesis. The findings revealed significant alterations in mechanical behaviour and structural features of soft-to-hard tissue interfaces treated by CESS. The findings suggest that I₂ in DMEM provides a better balance between visualization and mechanical analysis. However, none of the CESSs completely preserved both structural and mechanical integrity.
RGB-D camera and graph neural network-based SLAM for dynamic and low-texture environments
The effect of soil physicochemical properties on intraspecific variability of pollen morphology in Staphylea pinnata L.
The relationship between sperm secretion and the Hippo signaling pathway in the rat seminiferous tubules
Modelling and simulation of the block pouring construction system considering spatial–temporal conflict of construction machinery in arch dams
Abstract The spatial–temporal conflicts in the construction process may cause a series of construction quality, safety and schedule problems. The outbreak of mechanical spatial–temporal conflict in the construction process of the arch dam pouring block is random and uncertain. Scientific simulation and preview of the pouring construction process and analysis of the level, time, and influence degree of the outbreak of spatial–temporal conflict are significant means to optimize the construction organization and management. According to the degree of spatial–temporal conflict and its effect on security and efficiency, the subsidiary space scope of construction machinery is divided into three levels from inside to outside. The quantification algorithm of spatial–temporal conflict is proposed based on the three-layered space and time–space microelement model. The discrete system theory is employed to develop a simulation framework that systematically incorporates four core components: simulation objectives, construction machinery operational cycles, resource allocation mechanisms, and modeling assumptions. Combined with the typical pouring block in Baihetan arch dam, the construction process is simulated and the visualization system is developed, which achieves the information integration such as the quantification of the spatial–temporal conflict, the analysis of the influence effects, and the visualization of conflict information. The system simulation results show that the spatial–temporal conflict problem always exists in the pouring construction process, the problems of security risk and efficiency loss are inevitable. And the reasonable unloading point planning and mechanical trajectory setting can effectively reduce the risk of spatial–temporal conflict. Those studies provide a reference for the rational organization and scientific decision-making of pouring construction activities, and new ideas and methods for the safe and efficient construction, as well as the scientific and refined management, of arch dams.
The risk of heart-specific death in breast cancer patients
Abstract With the improvement of comprehensive anti-cancer treatment for breast cancer (BC), more and more BC survivors will die from non-cancer diseases, including cardiovascular disease dominated by heart disease (HD). Therefore, this study aimed to analyze the risk of heart-specific death (HSD) in patients with BC by using the Surveillance, Epidemiology, and End Results (SEER) database. The eligible patients diagnosed with BC between 2000 and 2019 were exported from the SEER database. The standard mortality ratios (SMR) were calculated to compare the difference in HSD between patients with BC and the general population. The Cox Proportional hazard model was used to estimate the risk factors for HSD in BC patients and the 95% confidence intervals (CI) were calculated. Overall, 655,552 eligible patients were included in our study, and 149,708 (22.8%) patients died. Among the deaths, 22,718 (15.2%) cases were attributed HD which was the second cause of death for BC patients. With the extension of follow-up (> 10 years), HD surpassed breast cancer as the leading cause of death for BC (22.3% vs. 20.2%). The SMR for HD was 8.14 (95%CI: 8.04–8.25) in the whole cohort. Multivariate analysis showed that race, age, marital status, median household income, grade, stage and subtype were independent risk factors for HSD in BC patients. The risk of HSD is significantly higher in BC patients than in the general population and closely related to demographic characteristics and tumor clinicopathological factors. Medical approaches are needed to reduce the risk of HD among patients with BC.
Investigation of the mechanism and application of pressure relief roadway in preventing rock burst
Age-related variation in hemoglobin glycation index and stroke mortality: mediation and machine learning in a cohort study
AMF30a promotes survival and function of human corneal endothelial cells by regulating TGF-β/ROCK/HIPPO pathway
Evaluating protein complexes between human aquaporin and calmodulin using biomolecular fluorescence complementation
Abstract Aquaporins (AQPs) are a family of integral membrane proteins crucial for the flow of water and other small molecules across cellular membranes. The involvement of calmodulin (CaM), a multifunctional calcium-binding protein, has emerged as a central regulator for specific aquaporin homologues from eukaryotes. Using a systematic approach, applying advanced high throughput screening methods in vivo, combining flow cytometry with microscopy, we have evaluated the putative interaction between CaM and the 13 human AQP homologues recombinantly produced in the yeast Saccharomyces cerevisiae. This comprehensive approach is complemented by a theoretical validation of potential CaM binding sites and a review of confirmed CaM binding locations from previous research. Our investigation is based on the established interaction of hAQP0 and CaM and we have successfully validated the binding of hAQP1 and hAQP4 to CaM. Noteworthy, discernibly high fluorescence frequency signals were observed for hAQP8 and hAQP9, which did not correlate with a particularly high production level, supporting protein complex formation with CaM for those AQP homologues. Overall, we present a systematic approach to screen novel membrane protein interactions in vivo, relying on co-expression in yeast of Bimolecular Fluorescence Complementation (BiFC) complexes providing new insights into the regulation of the hAQPs.
High-risk human papillomavirus genotype distribution and attribution to cervical lesions in a Shanxi Province screening population
Long-term water types and satellite chlorophyll a variability in a river discharge environment around Ieodo Ocean Research Station
Removal of reactive red 45 dye from aqueous solution using activated carbon developed from Catha edulis stem as a potential biosorbent
Multi-level data fusion enables collaborative dynamics analysis in team sports using wearable sensor networks
Abstract This research proposes a novel multi-level data fusion method for analyzing collaborative dynamics in team sports using wearable sensor networks. We developed and validated this approach through controlled experiments with 40 semi-professional athletes across basketball and soccer scenarios. The multi-level fusion architecture integrates IMU, GPS, physiological, and positioning data through adaptive weight allocation and asynchronous alignment algorithms. Experimental validation demonstrated 8.6 dB improvement in signal quality and 42.3% enhancement in positional accuracy compared to single-source approaches. Cross-sport testing across basketball, soccer, volleyball, and handball showed consistent performance (84.2–91.4% accuracy) with real-time response times of 192-312ms. The developed collaborative dynamics indicator system revealed that temporal coordination parameters strongly correlate with team performance (r = 0.73), while four key metrics predict match outcomes with 73.6% accuracy. This methodology provides coaches and analysts with objective tools for quantifying previously subjective aspects of team coordination.
Postoperative functional training program for vascularised Iliac flap donor site in jaw defect reconstruction based on the Delphi method
Abstract Vascularised iliac flaps (VIFs) are widely used for the reconstruction of jawbone defects; however, postoperative donor-site complications, such as gait disturbances, with an incidence of 13.9–50%, significantly impede patient recovery. Despite this, evidence-based rehabilitation protocols specific to VIFs remain lacking. Existing rehabilitation guidelines for hip surgeries are unsuitable owing to differences in surgical mechanisms. This study employed the Delphi method, engaging 20 multidisciplinary experts (oral and maxillofacial surgery: 5; orthopaedics: 7; rehabilitation: 6; nursing: 2). Through three rounds of anonymous consultations, and by integrating literature evidence with postoperative mobility assessments, we developed a phased, individualised progressive functional training (PFT) protocol featuring dynamic evaluation, coordinated activation of abdominal and hip muscle groups, and safe exercise strategies during head and neck immobilisation, while overcoming conventional hip rehabilitation limitations (e.g., restrictions on flexion < 90°, and bans on squatting or cross-legged sitting). PFT is structured into six progressive phases, with exercise intensity tailored to assessment outcomes. A single-centre randomised controlled trial (n = 62) demonstrated that PFT significantly accelerated lower limb functional recovery, improved hip mobility and balance, reduced donor-site pain, and enhanced quality of life (University of Washington Quality of Life questionnaire: F (1,60) = 17.262, P < 0.001), without increasing the risk of flap vascular compromise or iliac hematoma. The limitations of the study include the single-centre design and lack of cross-cultural validation. Future multicentre studies are required to enhance adaptability. This study establishes a foundational yet effective framework for post-VIF rehabilitation, guiding clinical practice and research advancements.
Transfer learning based deep architecture for lung cancer classification using CT image with pattern and entropy based feature set
Abstract Early detection of lung cancer, which remains one of the leading causes of death worldwide, is important for improved prognosis, and CT scanning is an important diagnostic modality. Lung cancer classification according to CT scan is challenging since the disease is characterized by very variable features. A hybrid deep architecture, ILN-TL-DM, is presented in this paper for precise classification of lung cancer from CT scan images. Initially, an Adaptive Gaussian filtering method is applied during pre-processing to eliminate noise and enhance the quality of the CT image. This is followed by an Improved Attention-based ResU-Net (P-ResU-Net) model being utilized during the segmentation process to accurately isolate the lung and tumor areas from the remaining image. During the process of feature extraction, various features are derived from the segmented images, such as Local Gabor Transitional Pattern (LGTrP), Pyramid of Histograms of Oriented Gradients (PHOG), deep features and improved entropy-based features, all intended to improve the representation of the tumor areas. Finally, classification exploits a hybrid deep learning architecture integrating an improved LeNet structure with Transfer Learning (ILN-TL) and a DeepMaxout (DM) structure. Both model outputs are finally merged with the help of a soft voting strategy, which results in the final classification result that separates cancerous and non-cancerous tissues. The strategy greatly enhances lung cancer detection’s accuracy and strength, showcasing how combining sophisticated neural network structures with feature engineering and ensemble methods could be used to achieve better medical image classification. The ILN-TL-DM model consistently outperforms the conventional methods with greater accuracy (0.962), specificity (0.955) and NPV (0.964).